A Survey of Computational Intelligence Techniques in Protein Function Prediction
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paper_title: Basic Local Alignment Search Tool

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A new approach to rapid sequence comparison, basic local alignment search tool (BLAST), directly approximates alignments that optimize a measure of local similarity, the maximal segment pair (MSP) score. Recent mathematical results on the stochastic properties of MSP scores allow an analysis of the performance of this method as well as the statistical significance of alignments it generates. The basic algorithm is simple and robust; it can be implemented in a number of ways and applied in a variety of contexts including straightforward DNA and protein sequence database searches, motif searches, gene identification searches, and in the analysis of multiple regions of similarity in long DNA sequences. In addition to its flexibility and tractability to mathematical analysis, BLAST is an order of magnitude faster than existing sequence comparison tools of comparable sensitivity.
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paper_title: Recent progresses in the application of machine learning approach for predicting protein functional class independent of sequence similarity

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Protein sequence contains clues to its function. Functional prediction from sequence presents a challenge particularly for proteins that have low or no sequence similarity to proteins of known function. Recently, machine learning methods have been explored for predicting functional class of proteins from sequence-derived properties independent of sequence similarity, which showed promising potential for low- and non-homologous proteins. These methods can thus be explored as potential tools to complement alignment- and clustering-based methods for predicting protein function. This article reviews the strategies, current progresses, and underlying difficulties in using machine learning methods for predicting the functional class of proteins. The relevant software and web-servers are described. The reported prediction performances in the application of these methods are also presented, which need to be interpreted with caution as they are dependent on such factors as datasets used and choice of parameters.
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paper_title: Prediction of protein function from protein sequence and structure.

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The sequence of a genome contains the plans of the possible life of an organism, but implementation of genetic information depends on the functions of the proteins and nucleic acids that it encodes. Many individual proteins of known sequence and structure present challenges to the understanding of their function. In particular, a number of genes responsible for diseases have been identified but their specific functions are unknown. Whole-genome sequencing projects are a major source of proteins of unknown function. Annotation of a genome involves assignment of functions to gene products, in most cases on the basis of amino-acid sequence alone. 3D structure can aid the assignment of function, motivating the challenge of structural genomics projects to make structural information available for novel uncharacterized proteins. Structure-based identification of homologues often succeeds where sequence-alone-based methods fail, because in many cases evolution retains the folding pattern long after sequence similarity becomes undetectable. Nevertheless, prediction of protein function from sequence and structure is a difficult problem, because homologous proteins often have different functions. Many methods of function prediction rely on identifying similarity in sequence and/or structure between a protein of unknown function and one or more well-understood proteins. Alternative methods include inferring conservation patterns in members of a functionally uncharacterized family for which many sequences and structures are known. However, these inferences are tenuous. Such methods provide reasonable guesses at function, but are far from foolproof. It is therefore fortunate that the development of whole-organism approaches and comparative genomics permits other approaches to function prediction when the data are available. These include the use of protein-protein interaction patterns, and correlations between occurrences of related proteins in different organisms, as indicators of functional properties. Even if it is possible to ascribe a particular function to a gene product, the protein may have multiple functions. A fundamental problem is that function is in many cases an ill-defined concept. In this article we review the state of the art in function prediction and describe some of the underlying difficulties and successes.
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paper_title: The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored

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An essential prerequisite for any systems-level understanding of cellular functions is to correctly uncover and annotate all functional interactions among proteins in the cell. Toward this goal, remarkable progress has been made in recent years, both in terms of experimental measurements and computational prediction techniques. However, public efforts to collect and present protein interaction information have struggled to keep up with the pace of interaction discovery, partly because protein–protein interaction information can be error-prone and require considerable effort to annotate. Here, we present an update on the online database resource Search Tool for the Retrieval of Interacting Genes (STRING); it provides uniquely comprehensive coverage and ease of access to both experimental as well as predicted interaction information. Interactions in STRING are provided with a confidence score, and accessory information such as protein domains and 3D structures is made available, all within a stable and consistent identifier space. New features in STRING include an interactive network viewer that can cluster networks on demand, updated on-screen previews of structural information including homology models, extensive data updates and strongly improved connectivity and integration with third-party resources. Version 9.0 of STRING covers more than 1100 completely sequenced organisms; the resource can be reached at http://string-db.org.
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paper_title: The PROSITE database, its status in 1995

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The PROSITE database consists of biologically significant patterns and profiles formulated in such a way that with appropriate computational tools it can help to determine to which known family of proteins (if any) a new sequence belongs or which known domain(s) it contains.
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paper_title: Effective protein sequence comparison.

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Although there are several different comparison programs available (e.g., BLASTP, FASTA, SSEARCH, and BLITZ) that can be used with different scoring systems (e.g., PAM120, PAM250, BLOSUM50, BLOSUM62) and different databases (e.g., PIR, SWISS-PROT, GenPept), the following search protocol should identify homologous sequences whenever they can be found. 1. Always compare protein sequences if the genes encode proteins. Protein sequence comparison will typically double the evolutionary lookback time over DNA sequence comparison. 2. Search several sequence databases using a rapid sequence comparison program (e.g., BLASTP or FASTA, ktup = 2). Well-curated databases like PIR or SWISS-PROT tend to have fewer redundant sequences, which improves the statistical significance of a match, but they are less comprehensive and up-to-date than GenPept. 3. If there is good agreement between the distribution of scores and the theoretical distribution, and the alignments do not include "simple sequence" domains, accept sequences with FASTA E() values or BLASTP P() values below 0.02 as homologous. 4. If no library sequences are found with E values below 0.02, perform additional searches with FASTA, ktup = 1, or SSEARCH. If library sequences with E values less than 0.02 are found, the sequences are probably homologous, unless a low-complexity domain is aligned. However, sequences with similarity scores from 0.02 to 10.0 may be homologous as well. To characterize these more distantly related sequences, select "marginal" library sequences and use them to search the databases. Additional family members should have E values less than 0.05. 5. Homologous sequences share a common ancestor, and thus a common protein fold. Depending on the evolutionary distance and divergence path, two or more homologous sequences may have very few absolutely conserved residues. However, if homology has been inferred between A and B, between B and C, and between C and D, A and D must be homologous, even if they share no significant similarity. 6. Sequences with marginal E values should also be tested using the PRSS program. Compare the query and library sequences using at least 200 (and preferably 1000) shuffles. Shuffles using a window (-w) of 10-20 are more stringent than a uniform shuffle. Use the E value after 1000 shuffles to confirm an inference of homology. 7. Homologous sequences are usually similar over an entire sequence or domain, typically sharing 20-25% or greater identity for more than 200 residues. Matches that are more than 50% identical in a 20- to 40-amino acid region occur frequently by chance and do not indicate homology. By following these steps, one will very rarely assert that two sequences are homologous when in fact they are not. However, these criteria are stringent; distantly related homologous sequences may fail to be detected because their similarity is not statistically significant. These tests are biased toward missing some distantly related sequences to avoid the possibility of misidentifying unrelated ones. In most database searches, the ratio of related to unrelated sequences is more than 4000:1 (e.g., 10 related and 40,000 unrelated sequences). Thus, one is more likely to mistakenly identify two sequences as related than to overlook a genuine relationship, and our conservative evaluation criteria reflect that bias.
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paper_title: The swiss-prot protein knowledgebase and its supplement trembl in 2003

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The SWISS-PROT protein knowledgebase (http:// www.expasy.org/sprot/ and http://www.ebi.ac.uk/ swissprot/) connects amino acid sequences with the current knowledge in the Life Sciences. Each protein entry provides an interdisciplinary overview of relevant information by bringing together experimental results, computed features and sometimes even contradictory conclusions. Detailed expertise that goes beyond the scope of SWISS-PROT is made available via direct links to specialised databases. SWISS-PROT provides annotated entries for all species, but concentrates on the annotation of entries from human (the HPI project) and other model organisms to ensure the presence of high quality annotation for representative members of all protein families. Part of the annotation can be transferred to other family members, as is already done for microbes by the High-quality Automated and Manual Annotation of microbial Proteomes (HAMAP) project. Protein families and groups of proteins are regularly reviewed to keep up with current scientific findings. Complementarily, TrEMBL strives to comprise all protein sequences that are not yet represented in SWISS-PROT, by incorporating a perpetually increasing level of mostly automated annotation. Researchers are welcome to contribute their knowledge to the scientific community by submitting relevant findings to SWISS-PROT at swiss-prot@expasy.org.
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paper_title: DIP, the Database of Interacting Proteins: a research tool for studying cellular networks of protein interactions

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The Database of Interacting Proteins (DIP: http:// dip.doe-mbi.ucla.edu) is a database that documents experimentally determined protein–protein interactions. It provides the scientific community with an integrated set of tools for browsing and extracting information about protein interaction networks. As of September 2001, the DIP catalogs ∼11 000 unique interactions among 5900 proteins from >80 organisms; the vast majority from yeast, Helicobacter pylori and human. Tools have been developed that allow users to analyze, visualize and integrate their own experimental data with the information about protein–protein interactions available in the DIP database.
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paper_title: GPCRsclass: a web tool for the classification of amine type of G-protein-coupled receptors

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The receptors of amine subfamily are specifically major drug targets for therapy of nervous disorders and psychiatric diseases. The recognition of novel amine type of receptors and their cognate ligands is of paramount interest for pharmaceutical companies. In the past, Chou and co-workers have shown that different types of amine receptors are correlated with their amino acid composition and are predictable on its basis with considerable accuracy [Elrod and Chou (2002) Protein Eng., 15, 713–715]. This motivated us to develop a better method for the recognition of novel amine receptors and for their further classification. The method was developed on the basis of amino acid composition and dipeptide composition of proteins using support vector machine. The method was trained and tested on 167 proteins of amine subfamily of G-protein-coupled receptors (GPCRs). The method discriminatedaminesubfamilyofGPCRsfromglobular proteins with Matthew’s correlation coefficient of 0.98 and 0.99 using amino acid composition and dipeptide composition, respectively. In classifying different types of amine receptors using amino acid composition and dipeptide composition, the method achieved an accuracy of 89.8 and 96.4%, respectively. The performance of the method was evaluated using 5-fold cross-validation. The dipeptide composition based method predicted 67.6% of protein sequences with an accuracy of 100% with a reliability index >5. A web server GPCRsclass has been developed for predicting amine-binding receptors from its amino acid sequence [http://www.imtech.res.in/raghava/gpcrsclass/ and http://bioinformatics.uams.edu/raghava/gpersclass/ (mirror site)].
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paper_title: Recognizing the fold of a protein structure

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This paper reports a graph-theoretic program, GRATH, that rapidly, and accurately, matches a novel structure against a library of domain structures to find the most similar ones. GRATH generates distributions of scores by comparing the novel domain against the different types of folds that have been classified previously in the CATH database of structural domains. GRATH uses a measure of similarity that details the geometric information, number of secondary structures and number of residues within secondary structures, that any two protein structures share. Although GRATH builds on well established approaches for secondary structure comparison, a novel scoring scheme has been introduced to allow ranking of any matches identified by the algorithm. More importantly, we have benchmarked the algorithm using a large dataset of 1702 non-redundant structures from the CATH database which have already been classified into fold groups, with manual validation. This has facilitated introduction of further constraints, optimization of parameters and identification of reliable thresholds for fold identification. Following these benchmarking trials, the correct fold can be identified with the top score with a frequency of 90%. It is identified within the ten most likely assignments with a frequency of 98%. GRATH has been implemented to use via a server (http://www.biochem.ucl.ac.uk/cgi-bin/cath/Grath.pl). GRATH's speed and accuracy means that it can be used as a reliable front-end filter for the more accurate, but computationally expensive, residue based structure comparison algorithm SSAP, currently used to classify domain structures in the CATH database. With an increasing number of structures being solved by the structural genomics initiatives, the GRATH server also provides an essential resource for determining whether newly determined structures are related to any known structures from which functional properties may be inferred.
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paper_title: The Protein Data Bank

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The Protein Data Bank (PDB; http://www.rcsb.org/pdb/ ) is the single worldwide archive of structural data of biological macromolecules. This paper describes the goals of the PDB, the systems in place for data deposition and access, how to obtain further information, and near-term plans for the future development of the resource.
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paper_title: Improved tools for biological sequence comparison.

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We have developed three computer programs for comparisons of protein and DNA sequences. They can be used to search sequence data bases, evaluate similarity scores, and identify periodic structures based on local sequence similarity. The FASTA program is a more sensitive derivative of the FASTP program, which can be used to search protein or DNA sequence data bases and can compare a protein sequence to a DNA sequence data base by translating the DNA data base as it is searched. FASTA includes an additional step in the calculation of the initial pairwise similarity score that allows multiple regions of similarity to be joined to increase the score of related sequences. The RDF2 program can be used to evaluate the significance of similarity scores using a shuffling method that preserves local sequence composition. The LFASTA program can display all the regions of local similarity between two sequences with scores greater than a threshold, using the same scoring parameters and a similar alignment algorithm; these local similarities can be displayed as a "graphic matrix" plot or as individual alignments. In addition, these programs have been generalized to allow comparison of DNA or protein sequences based on a variety of alternative scoring matrices.
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paper_title: Improving structure-based function prediction using molecular dynamics.

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The number of molecules with solved three-dimensional structure but unknown function is increasing rapidly. Particularly problematic are novel folds with little detectable similarity to molecules of known function. Experimental assays can determine the functions of such molecules, but are time-consuming and expensive. Computational approaches can identify potential functional sites; however, these approaches generally rely on single static structures and do not use information about dynamics. In fact, structural dynamics can enhance function prediction: we coupled molecular dynamics simulations with structure-based function prediction algorithms that identify Ca(2+) binding sites. When applied to 11 challenging proteins, both methods showed substantial improvement in performance, revealing 22 more sites in one case and 12 more in the other, with a modest increase in apparent false positives. Thus, we show that treating molecules as dynamic entities improves the performance of structure-based function prediction methods.
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paper_title: Predicting Protein Ligand Binding Sites by Combining Evolutionary Sequence Conservation and 3D Structure

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Identifying a protein's functional sites is an important step towards characterizing its molecular function. Numerous structure- and sequence-based methods have been developed for this problem. Here we introduce ConCavity, a small molecule binding site prediction algorithm that integrates evolutionary sequence conservation estimates with structure-based methods for identifying protein surface cavities. In large-scale testing on a diverse set of single- and multi-chain protein structures, we show that ConCavity substantially outperforms existing methods for identifying both 3D ligand binding pockets and individual ligand binding residues. As part of our testing, we perform one of the first direct comparisons of conservation-based and structure-based methods. We find that the two approaches provide largely complementary information, which can be combined to improve upon either approach alone. We also demonstrate that ConCavity has state-of-the-art performance in predicting catalytic sites and drug binding pockets. Overall, the algorithms and analysis presented here significantly improve our ability to identify ligand binding sites and further advance our understanding of the relationship between evolutionary sequence conservation and structural and functional attributes of proteins. Data, source code, and prediction visualizations are available on the ConCavity web site (http://compbio.cs.princeton.edu/concavity/).
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paper_title: Gene Expression Omnibus: NCBI gene expression and hybridization array data repository

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The Gene Expression Omnibus (GEO) project was initiated in response to the growing demand for a public repository for high-throughput gene expression data. GEO provides a flexible and open design that facilitates submission, storage and retrieval of heterogeneous data sets from high-throughput gene expression and genomic hybridization experiments. GEO is not intended to replace in house gene expression databases that benefit from coherent data sets, and which are constructed to facilitate a particular analytic method, but rather complement these by acting as a tertiary, central data distribution hub. The three central data entities of GEO are platforms, samples and series, and were designed with gene expression and genomic hybridization experiments in mind. A platform is, essentially, a list of probes that define what set of molecules may be detected. A sample describes the set of molecules that are being probed and references a single platform used to generate its molecular abundance data. A series organizes samples into the meaningful data sets which make up an experiment. The GEO repository is publicly accessible through the World Wide Web at http://www.ncbi.nlm.nih.gov/geo.
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paper_title: Functional inferences from reconstructed evolutionary biology involving rectified databases--an evolutionarily grounded approach to functional genomics.

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If bioinformatics tools are constructed to reproduce the natural, evolutionary history of the biosphere, they offer powerful approaches to some of the most difficult tasks in genomics, including the organization and retrieval of sequence data, the updating of massive genomic databases, the detection of database error, the assignment of introns, the prediction of protein conformation from protein sequences, the detection of distant homologs, the assignment of function to open reading frames, the identification of biochemical pathways from genomic data, and the construction of a comprehensive model correlating the history of biomolecules with the history of planet Earth.
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paper_title: BindN: a web-based tool for efficient prediction of DNA and RNA binding sites in amino acid sequences

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BindN (http://bioinformatics.ksu.edu/bindn/) takes an amino acid sequence as input and predicts potential DNA or RNA-binding residues with support vector machines (SVMs). Protein datasets with known DNA or RNA-binding residues were selected from the Protein Data Bank (PDB), and SVM models were constructed using data instances encoded with three sequence features, including the side chain pKa value, hydrophobicity index and molecular mass of an amino acid. The results suggest that DNA-binding residues can be predicted at 69.40% sensitivity and 70.47% specificity, while prediction of RNA-binding residues achieves 66.28% sensitivity and 69.84% specificity. When compared with previous studies, the SVM models appear to be more accurate and more efficient for online predictions. BindN provides a useful tool for understanding the function of DNA and RNA-binding proteins based on primary sequence data.
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paper_title: PRINTS--a database of protein motif fingerprints.

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Abstract ::: PRINTS is a compendium of protein motif 'fingerprints'. A fingerprint is defined as a group of motifs excised from conserved regions of a sequence alignment, whose diagnostic power or potency is refined by iterative databasescanning (in this case the OWL composite sequence database). Generally, the motifs do not overlap, but are separated along a sequence, though they may be contiguous in 3D-space. The use of groups of independent, linearly- or spatially-distinct motifs allows protein folds and functionalities to be characterised more flexibly and powerfully than conventional single-component patterns or regular expressions. The current version of the database contains 200 entries (encoding 950 motifs), covering a wide range of globular and membrane proteins, modular polypeptides, and so on. The growth of the databaseis influenced by a number of factors; e.g. the use of multiple motifs; the maximisation of sequence information through iterative database scanning; and the fact that the database searched is a large composite. The information contained within PRINTS is distinct from, but complementary to the consensus expressions stored in the widely-used PROSITE dictionary of patterns.
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paper_title: MemType-2L: A Web server for predicting membrane proteins and their types by incorporating evolution information through Pse-PSSM

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Abstract Given an uncharacterized protein sequence, how can we identify whether it is a membrane protein or not? If it is, which membrane protein type it belongs to? These questions are important because they are closely relevant to the biological function of the query protein and to its interaction process with other molecules in a biological system. Particularly, with the avalanche of protein sequences generated in the Post-Genomic Age and the relatively much slower progress in using biochemical experiments to determine their functions, it is highly desired to develop an automated method that can be used to help address these questions. In this study, a 2-layer predictor, called MemType-2L, has been developed: the 1st layer prediction engine is to identify a query protein as membrane or non-membrane; if it is a membrane protein, the process will be automatically continued with the 2nd-layer prediction engine to further identify its type among the following eight categories: (1) type I, (2) type II, (3) type III, (4) type IV, (5) multipass, (6) lipid-chain-anchored, (7) GPI-anchored, and (8) peripheral. MemType-2L is featured by incorporating the evolution information through representing the protein samples with the Pse-PSSM (Pseudo Position-Specific Score Matrix) vectors, and by containing an ensemble classifier formed by fusing many powerful individual OET-KNN (Optimized Evidence-Theoretic K-Nearest Neighbor) classifiers. The success rates obtained by MemType-2L on a new-constructed stringent dataset by both the jackknife test and the independent dataset test are quite high, indicating that MemType-2L may become a very useful high throughput tool. As a Web server, MemType-2L is freely accessible to the public at http://chou.med.harvard.edu/bioinf/MemType .
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paper_title: DBD-Hunter: a knowledge-based method for the prediction of DNA–protein interactions

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The structures of DNA–protein complexes have illuminated the diversity of DNA–protein binding mechanisms shown by different protein families. This lack of generality could pose a great challenge for predicting DNA–protein interactions. To address this issue, we have developed a knowledge-based method, DNA-binding Domain Hunter (DBD-Hunter), for identifying DNA-binding proteins and associated binding sites. The method combines structural comparison and the evaluation of a statistical potential, which we derive to describe interactions between DNA base pairs and protein residues. We demonstrate that DBD-Hunter is an accurate method for predicting DNA-binding function of proteins, and that DNA-binding protein residues can be reliably inferred from the corresponding templates if identified. In benchmark tests on ∼4000 proteins, our method achieved an accuracy of 98% and a precision of 84%, which significantly outperforms three previous methods. We further validate the method on DNA-binding protein structures determined in DNA-free (apo) state. We show that the accuracy of our method is only slightly affected on apo-structures compared to the performance on holo-structures cocrystallized with DNA. Finally, we apply the method to ∼1700 structural genomics targets and predict that 37 targets with previously unknown function are likely to be DNA-binding proteins. DBD-Hunter is freely available at http://cssb.biology.gatech.edu/skolnick/webservice/DBD-Hunter/.
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paper_title: Subcellular location prediction of proteins using support vector machines with alignment of block sequences utilizing amino acid composition

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BackgroundSubcellular location prediction of proteins is an important and well-studied problem in bioinformatics. This is a problem of predicting which part in a cell a given protein is transported to, where an amino acid sequence of the protein is given as an input. This problem is becoming more important since information on subcellular location is helpful for annotation of proteins and genes and the number of complete genomes is rapidly increasing. Since existing predictors are based on various heuristics, it is important to develop a simple method with high prediction accuracies.ResultsIn this paper, we propose a novel and general predicting method by combining techniques for sequence alignment and feature vectors based on amino acid composition. We implemented this method with support vector machines on plant data sets extracted from the TargetP database. Through fivefold cross validation tests, the obtained overall accuracies and average MCC were 0.9096 and 0.8655 respectively. We also applied our method to other datasets including that of WoLF PSORT.ConclusionAlthough there is a predictor which uses the information of gene ontology and yields higher accuracy than ours, our accuracies are higher than existing predictors which use only sequence information. Since such information as gene ontology can be obtained only for known proteins, our predictor is considered to be useful for subcellular location prediction of newly-discovered proteins. Furthermore, the idea of combination of alignment and amino acid frequency is novel and general so that it may be applied to other problems in bioinformatics. Our method for plant is also implemented as a web-system and available on http://sunflower.kuicr.kyoto-u.ac.jp/~tamura/slpfa.html.
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paper_title: Exploiting structural and topological information to improve prediction of RNA-protein binding sites

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BackgroundRNA-protein interactions are important for a wide range of biological processes. Current computational methods to predict interacting residues in RNA-protein interfaces predominately rely on sequence data. It is, however, known that interface residue propensity is closely correlated with structural properties. In this paper we systematically study information obtained from sequences and structures and compare their contributions in this prediction problem. Particularly, different geometrical and network topological properties of protein structures are evaluated to improve interface residue prediction accuracy.ResultsWe have quantified the impact of structural information on the prediction accuracy in comparison to the purely sequence based approach using two machine learning techniques: Naïve Bayes classifiers and Support Vector Machines. The highest AUC of 0.83 was achieved by a Support Vector Machine, exploiting PSI-BLAST profile, accessible surface area, betweenness-centrality and retention coefficient as input features. Taking into account that our results are based on a larger non-redundant data set, the prediction accuracy is considerably higher than reported in previous, comparable studies. A protein-RNA interface predictor (PRIP) and the data set have been made available at http://www.qfab.org/PRIP.ConclusionGraph-theoretic properties of residue contact maps derived from protein structures such as betweenness-centrality can supplement sequence or structure features to improve the prediction accuracy for binding residues in RNA-protein interactions. While Support Vector Machines perform better on this task, Naïve Bayes classifiers also have been found to achieve good prediction accuracies but require much less training time and are an attractive choice for large scale predictions.
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paper_title: On the Structural Context and Identification of Enzyme Catalytic Residues

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Enzymes play important roles in most of the biological processes. Although only a small fraction of residues are directly involved in catalytic reactions, these catalytic residues are the most crucial parts in enzymes. The study of the fundamental and unique features of catalytic residues benefits the understanding of enzyme functions and catalytic mechanisms. In this work, we analyze the structural context of catalytic residues based on theoretical and experimental structure flexibility. The results show that catalytic residues have distinct structural features and context. Their neighboring residues, whether sequence or structure neighbors within specific range, are usually structurally more rigid than those of noncatalytic residues. The structural context feature is combined with support vector machine to identify catalytic residues from enzyme structure. The prediction results are better or comparable to those of recent structure-based prediction methods.
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paper_title: Prediction of Transmembrane Proteins from Their Primary Sequence by Support Vector Machine Approach

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Prediction of transmembrane (TM) proteins from their sequence facilitates functional study of genomes and the search of potential membrane-associated therapeutic targets. Computational methods for predicting TM sequences have been developed. These methods achieve high prediction accuracy for many TM proteins but some of these methods are less effective for specific class of TM proteins. Moreover, their performance has been tested by using a relatively small set of TM and non-membrane (NM) proteins. Thus it is useful to evaluate TM protein prediction methods by using a more diverse set of proteins and by testing their performance on specific classes of TM proteins. This work extensively evaluated the capability of support vector machine (SVM) classification systems for the prediction of TM proteins and those of several TM classes. These SVM systems were trained and tested by using 14962 TM and 12168 NM proteins from Pfam protein families. An independent set of 3389 TM and 6063 NM proteins from curated Pfam families were used to further evaluate the performance of these SVM systems. 90.1% and 86.7% of TM and NM proteins were correctly predicted respectively, which are comparable to those from other studies. The prediction accuracies for proteins of specific TM classes are 95.6%, 90.0%, 92.7% and 73.9% for G-protein coupled receptors, envelope proteins, outer membrane proteins, and transporters/channels respectively; and 98.1%, 99.5%, 86.4%, and 98.6% for non-G-protein coupled receptors, non-envelope proteins, non-outer membrane proteins, and non-transporters/non-channels respectively. Tested by using a significantly larger number and more diverse range of proteins than in previous studies, SVM systems appear to be capable of prediction of TM proteins and proteins of specific TM classes at accuracies comparable to those from previous studies. Our SVM systems – SVMProt, can be accessed at http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi.
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paper_title: Prediction of Multi-Type Membrane Proteins in Human by an Integrated Approach

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Membrane proteins were found to be involved in various cellular processes performing various important functions, which are mainly associated to their types. However, it is very time-consuming and expensive for traditional biophysical methods to identify membrane protein types. Although some computational tools predicting membrane protein types have been developed, most of them can only recognize one kind of type. Therefore, they are not as effective as one membrane protein can have several types at the same time. To our knowledge, few methods handling multiple types of membrane proteins were reported. In this study, we proposed an integrated approach to predict multiple types of membrane proteins by employing sequence homology and protein-protein interaction network. As a result, the prediction accuracies reached 87.65%, 81.39% and 70.79%, respectively, by the leave-one-out test on three datasets. It outperformed the nearest neighbor algorithm adopting pseudo amino acid composition. The method is anticipated to be an alternative tool for identifying membrane protein types. New metrics for evaluating performances of methods dealing with multi-label problems were also presented. The program of the method is available upon request.
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paper_title: Sequence Based Prediction of DNA-Binding Proteins Based on Hybrid Feature Selection Using Random Forest and Gaussian Naïve Bayes

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Developing an efficient method for determination of the DNA-binding proteins, due to their vital roles in gene regulation, is becoming highly desired since it would be invaluable to advance our understanding of protein functions. In this study, we proposed a new method for the prediction of the DNA-binding proteins, by performing the feature rank using random forest and the wrapper-based feature selection using forward best-first search strategy. The features comprise information from primary sequence, predicted secondary structure, predicted relative solvent accessibility, and position specific scoring matrix. The proposed method, called DBPPred, used Gaussian naive Bayes as the underlying classifier since it outperformed five other classifiers, including decision tree, logistic regression, k-nearest neighbor, support vector machine with polynomial kernel, and support vector machine with radial basis function. As a result, the proposed DBPPred yields the highest average accuracy of 0.791 and average MCC of 0.583 according to the five-fold cross validation with ten runs on the training benchmark dataset PDB594. Subsequently, blind tests on the independent dataset PDB186 by the proposed model trained on the entire PDB594 dataset and by other five existing methods (including iDNA-Prot, DNA-Prot, DNAbinder, DNABIND and DBD-Threader) were performed, resulting in that the proposed DBPPred yielded the highest accuracy of 0.769, MCC of 0.538, and AUC of 0.790. The independent tests performed by the proposed DBPPred on completely a large non-DNA binding protein dataset and two RNA binding protein datasets also showed improved or comparable quality when compared with the relevant prediction methods. Moreover, we observed that majority of the selected features by the proposed method are statistically significantly different between the mean feature values of the DNA-binding and the non DNA-binding proteins. All of the experimental results indicate that the proposed DBPPred can be an alternative perspective predictor for large-scale determination of DNA-binding proteins.
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paper_title: A novel approach for protein subcellular location prediction using amino acid exposure

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BACKGROUND ::: Proteins perform their functions in associated cellular locations. Therefore, the study of protein function can be facilitated by predictions of protein location. Protein location can be predicted either from the sequence of a protein alone by identification of targeting peptide sequences and motifs, or by homology to proteins of known location. A third approach, which is complementary, exploits the differences in amino acid composition of proteins associated to different cellular locations, and can be useful if motif and homology information are missing. Here we expand this approach taking into account amino acid composition at different levels of amino acid exposure. ::: ::: ::: RESULTS ::: Our method has two stages. For stage one, we trained multiple Support Vector Machines (SVMs) to score eukaryotic protein sequences for membership to each of three categories: nuclear, cytoplasmic and extracellular, plus extra category nucleocytoplasmic, accounting for the fact that a large number of proteins shuttles between those two locations. In stage two we use an artificial neural network (ANN) to propose a category from the scores given to the four locations in stage one. The method reaches an accuracy of 68% when using as input 3D-derived values of amino acid exposure. Calibration of the method using predicted values of amino acid exposure allows classifying proteins without 3D-information with an accuracy of 62% and discerning proteins in different locations even if they shared high levels of identity. ::: ::: ::: CONCLUSIONS ::: In this study we explored the relationship between residue exposure and protein subcellular location. We developed a new algorithm for subcellular location prediction that uses residue exposure signatures. Our algorithm uses a novel approach to address the multiclass classification problem. The algorithm is implemented as web server 'NYCE' and can be accessed at http://cbdm.mdc-berlin.de/~amer/nyce.
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paper_title: Identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites.

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We have developed a new method for the identification of signal peptides and their cleavage sites based on neural networks trained on separate sets of prokaryotic and eukaryotic sequence. The method performs significantly better than previous prediction schemes and can easily be applied on genome-wide data sets. Discrimination between cleaved signal peptides and uncleaved N-terminal signal-anchor sequences is also possible, though with lower precision. Predictions can be made on a publicly available WWW server.
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paper_title: An improved prediction of catalytic residues in enzyme structures

paper_content:

edu.cn The protein databases contain a huge number of function unknown proteins, including many proteins with newly determined 3D structures resulted from the Structural Genomics Projects. To accelerate experiment-based assignment of function, de novo prediction of protein functional sites, like active sites in enzymes, becomes increasingly important. Here, we attempted to improve the prediction of catalytic residues in enzyme structures by seeking and refining different encodings (i.e. residue properties) as well as employing new machine learning algorithms. In particular, considering that catalytic residues can often reveal specific network centrality when representing enzyme structure as a residue contact network, the corresponding measurement (i.e. closeness centrality) was used as one of the most important encodings in our new predictor. Meanwhile, a genetic algorithm integrated neural network (GANN) was also employed. Thanks to the above strategies, our GANN predictor demonstrated a high accuracy of 91.2% in the prediction of catalytic residues based on balanced datasets (i.e. the 1:1 ratio of catalytic to non-catalytic residues). When the GANN method was optimally applied to real enzyme structures, 73.9% of the tested structures had the active site correctly located. Compared with two existing methods, the proposed GANN method also demonstrated a better performance.
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paper_title: GPCRpred: an SVM-based method for prediction of families and subfamilies of G-protein coupled receptors.

paper_content:

G-protein coupled receptors (GPCRs) belong to one of the largest superfamilies of membrane proteins and are important targets for drug design. In this study, a support vector machine (SVM)-based method, GPCRpred, has been developed for predicting families and subfamilies of GPCRs from the dipeptide composition of proteins. The dataset used in this study for training and testing was obtained from http://www.soe.ucsc.edu/research/compbio/gpcr/. The method classified GPCRs and non-GPCRs with an accuracy of 99.5% when evaluated using 5-fold cross-validation. The method is further able to predict five major classes or families of GPCRs with an overall Matthew's correlation coefficient (MCC) and accuracy of 0.81 and 97.5% respectively. In recognizing the subfamilies of the rhodopsin-like family, the method achieved an average MCC and accuracy of 0.97 and 97.3% respectively. The method achieved overall accuracy of 91.3% and 96.4% at family and subfamily level respectively when evaluated on an independent/blind dataset of 650 GPCRs. A server for recognition and classification of GPCRs based on multiclass SVMs has been set up at http://www.imtech.res.in/raghava/gpcrpred/. We have also suggested subfamilies for 42 sequences which were previously identified as unclassified ClassA GPCRs. The supplementary information is available at http://www.imtech.res.in/raghava/gpcrpred/info.html.
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paper_title: Predicting subcellular localization of gram-negative bacterial proteins by linear dimensionality reduction method.

paper_content:

With the rapid increase of protein sequences in the post-genomic age, the need for an automated and accurate tool to predict protein subcellular localization becomes increasingly important. Many efforts have been tried. Most of them aim to find the optimal classification scheme and less of them take the simplifying the complexity of biological system into consideration. This work shows how to decrease the complexity of biological system with linear DR (Dimensionality Reduction) method by transforming the original high-dimensional feature vectors into the low-dimensional feature vectors. A powerful sequence encoding scheme by fusing PSSM (Position-Specific Score Matrix) and Chou's PseAA (Pseudo Amino Acid) composition is proposed to represent the protein samples. Then, the K-NN (K-Nearest Neighbor) classifier is employed to identify the subcellular localization based on their reduced low-dimensional feature vectors. Experimental results thus obtained are quite encouraging, indicating that the aforementioned linear DR method is quite promising in dealing with complicated biological problems, such as predicting the subcellular localization of Gram-negative bacterial proteins.
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paper_title: An improved classification of G-protein-coupled receptors using sequence-derived features

paper_content:

BackgroundG-protein-coupled receptors (GPCRs) play a key role in diverse physiological processes and are the targets of almost two-thirds of the marketed drugs. The 3 D structures of GPCRs are largely unavailable; however, a large number of GPCR primary sequences are known. To facilitate the identification and characterization of novel receptors, it is therefore very valuable to develop a computational method to accurately predict GPCRs from the protein primary sequences.ResultsWe propose a new method called PCA-GPCR, to predict GPCRs using a comprehensive set of 1497 sequence-derived features. The principal component analysis is first employed to reduce the dimension of the feature space to 32. Then, the resulting 32-dimensional feature vectors are fed into a simple yet powerful classification algorithm, called intimate sorting, to predict GPCRs at five levels. The prediction at the first level determines whether a protein is a GPCR or a non-GPCR. If it is predicted to be a GPCR, then it will be further predicted into certain family, subfamily, sub-subfamily and subtype by the classifiers at the second, third, fourth, and fifth levels, respectively. To train the classifiers applied at five levels, a non-redundant dataset is carefully constructed, which contains 3178, 1589, 4772, 4924, and 2741 protein sequences at the respective levels. Jackknife tests on this training dataset show that the overall accuracies of PCA-GPCR at five levels (from the first to the fifth) can achieve up to 99.5%, 88.8%, 80.47%, 80.3%, and 92.34%, respectively. We further perform predictions on a dataset of 1238 GPCRs at the second level, and on another two datasets of 167 and 566 GPCRs respectively at the fourth level. The overall prediction accuracies of our method are consistently higher than those of the existing methods to be compared.ConclusionsThe comprehensive set of 1497 features is believed to be capable of capturing information about amino acid composition, sequence order as well as various physicochemical properties of proteins. Therefore, high accuracies are achieved when predicting GPCRs at all the five levels with our proposed method.
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paper_title: Identification of DNA-Binding Proteins Using Support Vector Machine with Sequence Information

paper_content:

DNA-binding proteins are fundamentally important in understanding cellular processes. Thus, the identification of DNA-binding proteins has the particularly important practical application in various fields, such as drug design. We have proposed a novel approach method for predicting DNA-binding proteins using only sequence information. The prediction model developed in this study is constructed by support vector machine-sequential minimal optimization (SVM-SMO) algorithm in conjunction with a hybrid feature. The hybrid feature is incorporating evolutionary information feature, physicochemical property feature, and two novel attributes. These two attributes use DNA-binding residues and nonbinding residues in a query protein to obtain DNA-binding propensity and nonbinding propensity. The results demonstrate that our SVM-SMO model achieves 0.67 Matthew's correlation coefficient (MCC) and 89.6% overall accuracy with 88.4% sensitivity and 90.8% specificity, respectively. Performance comparisons on various features indicate that two novel attributes contribute to the performance improvement. In addition, our SVM-SMO model achieves the best performance than state-of-the-art methods on independent test dataset.
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paper_title: Classification of G-protein coupled receptors at four levels

paper_content:

G-protein coupled receptors (GPCRs) are transmembraneproteinswhichviaG-proteinsinitiatesomeoftheimportantsignaling pathways in a cell and are involved in variousphysiological processes. Thus, computational predictionandclassificationofGPCRscansupplysignificantinforma-tion for the development of novel drugs in pharmaceuticalindustry.Inthispaper,anearestneighbormethodhasbeenintroduced to discriminate GPCRs from non-GPCRs andsubsequently classify GPCRs at four levels on the basis ofamino acid composition and dipeptide composition of pro-teins. Its performance is evaluated on a non-redundantdataset consisted of 1406 GPCRs for six families and 1406globular proteins using the jackknife test. The presentmethodbasedonaminoacidcompositionachieved anover-allaccuracyof96.4%andMatthew’scorrelationcoefficient(MCC) of 0.930 for correctly picking out the GPCRs fromglobular proteins. The overall accuracy and MCC werefurther enhanced to 99.8% and 0.996 by dipeptidecomposition-based method. On the other hand, the presentmethod has successfully classified 1406 GPCRs into sixfamilies with an overall accuracy of 89.6 and 98.8% usingamino acid composition and dipeptide composition, respec-tively. For the subfamily prediction of 1181 GPCRs ofrhodopsin-like family, the present method achieved anoverallaccuracyof76.7and94.5%basedontheaminoacidcomposition and dipeptide composition, respectively.Finally,GPCRsbelongingtotheaminesubfamilyandolfac-tory subfamily of rhodopsin-like family were furtheranalyzedatthetypelevel.Theoverallaccuracyofdipeptidecomposition-based method for the classification of aminetype and olfactory type of GPCRs reached 94.5 and86.9%, respectively, while the overall accuracy of aminoacid composition-based method was very low for bothsubfamilies. In comparison with existing methods in theliterature,thepresentmethodalsodisplayedgreatcompeti-tiveness. These results demonstrate the effectiveness of ourmethod on identifying and classifying GPCRs correctly.GPCRsIdentifier, a corresponding stand-alone executableprogram for GPCR identification and classification wasalso developed, which can be acquired freely on requestfrom the authors for academic purposes.IntroductionG-protein coupled receptors (GPCRs) play an extremelyimportant role in transducing extracellular signals across thecell membrane via guanine-binding proteins (G-proteins) withhigh speciﬁcity and sensitivity, a good example is the couplinginteraction mechanism between thromboxane A2 receptorand alpha-13 subunit of guanine nucleotide-binding proteinpresented detailedly in literature (Chou, 2005a). GPCRs arecentral to a broad range of physiologic events, including heartrate, proliferation, chemotaxis, neurotransmission and hor-mone secretion (Spiegel et al., 1992), and are consequentlypotential targets for the development of novel drugs. Thefamily of GPCRs is the largest known class of cell surfacereceptors (Strader et al., 1994), whose members have aseven-transmembrane domain structure with an extracellularN-terminus and a cytoplasmic C-terminus (Baldwin, 1994).Unfortunately, detailed information about the molecular struc-ture of GPCRs is insufﬁcient for structure-based drug designbecause of the difﬁculty in crystallization of membrane-boundproteins. The atomic-level structure has been solved only forbovine rhodopsin (Teller et al., 2001). With the rapid accu-mulation of biological data produced by many large-scalegenome sequencing projects, it is possible to develop theoreti-cal methods to predict the structure and function of GPCRs(Vaidehi et al., 2002). GPCRDB is a molecular class-speciﬁcinformation system that collects heterogeneous data onGPCRs, including data on sequences, ligand-binding constantsand mutations (Horn et al., 2003). All protein sequences inGPCRDB are mainly divided into six families: rhodopsin-likefamily, secretin-like family, metabotropic glutamate family,fungal pheromone family, cAMP receptor family andfrizzled/smoothened family. Each family covers severalsubfamilies of GPCRs. In addition, some putative families,orphan receptors and non-GPCRs families are also includedin the database. Detailed description refers to the websitevia http://www.gpcr.org/7tm/. Since the families of GPCRsare closely correlated with their structure and function, itis signiﬁcant to develop an automatic computational methodfor identifying these transmembrane proteins from genomesequences and classifying them based on their biologicalfunction.Many methods have been proposed for the prediction ofGPCRs in the past few years. One commonly used method issequence similarity searching in protein database by sequencealignment tools based on pairwise similarity, such as BLASTand FASTA (Altschul et al., 1997; Pearson, 2000). Severalpattern databases have been constructed by some investigators(Lapinsh et al., 2002; Sadowski and Parish, 2003). However,these methods are not always successful when the queryprotein sequences have no signiﬁcant sequence similarity tothe database sequences. On the other hand, in the case ofGPCRs, the function–similarity relationship is still unclear(Yabuki et al., 2005). Therefore, some statistical and machinelearning methods have been proposed, including the statisticalanalysis method (Chou and Elrod, 2002), covariant discrimi-nant algorithm (Elrod and Chou, 2002; Chou, 2005b), supportvector machines (SVMs) (Karchin et al., 2002; Bhasin andRaghava, 2004, 2005; Guo et al., 2005), hidden Markov
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paper_title: Predicting Signal Peptides and Their Cleavage Sites Using Support Vector Machines and Improved Position Weight Matrixes

paper_content:

In this paper, we develop a method for predicting signal peptides and their cleavage sites. Unlike other published work, we divide proteins into two segments and calculate the amino acid compositions on both segments. After that, we hybridize the pseudo amino acid compositions (PseAAs) to the feature vectors. Using support vector machines (SVMs) to train the datasets, we get better results than those with the optimized evidence-theoretic K nearest neighbor (OET-KNN) classifier. The overall rate of correct prediction for signal peptides is over 97%. For identifying cleavage sites, we use the scaled window proposed by Chou to extract cleavable secretory segments and non-cleavable secretory segments and improve the position weight matrix (PWM) method proposed by Hiller et al.. By hybridizing the scaled window and PWM methods, the correct prediction for signal peptides cleavage sites is also better or comparable to other methods.
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paper_title: Classification of Nuclear Receptors Based on Amino Acid Composition and Dipeptide Composition *

paper_content:

Nuclear receptors are key transcription factors that regulate crucial gene networks responsible for cell growth, differentiation, and homeostasis. Nuclear receptors form a superfamily of phylogenetically related proteins and control functions associated with major diseases (e.g. diabetes, osteoporosis, and cancer). In this study, a novel method has been developed for classifying the subfamilies of nuclear receptors. The classification was achieved on the basis of amino acid and dipeptide composition from a sequence of receptors using support vector machines. The training and testing was done on a non-redundant data set of 282 proteins obtained from the NucleaRDB data base (1). The performance of all classifiers was evaluated using a 5-fold cross validation test. In the 5-fold cross-validation, the data set was randomly partitioned into five equal sets and evaluated five times on each distinct set while keeping the remaining four sets for training. It was found that different subfamilies of nuclear receptors were quite closely correlated in terms of amino acid composition as well as dipeptide composition. The overall accuracy of amino acid composition-based and dipeptide compositionbased classifiers were 82.6 and 97.5%, respectively. Therefore, our results prove that different subfamilies of nuclear receptors are predictable with considerable accuracy using amino acid or dipeptide composition. Furthermore, based on above approach, an online web service, NRpred, was developed, which is available at www.imtech.res.in/raghava/nrpred.
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paper_title: Using A Neural Network and Spatial Clustering to Predict the Location of Active Sites in Enzymes

paper_content:

Structural genomics projects aim to provide a sharp increase in the number of structures of functionally unannotated, and largely unstudied, proteins. Algorithms and tools capable of deriving information about the nature, and location, of functional sites within a structure are increasingly useful therefore. Here, a neural network is trained to identify the catalytic residues found in enzymes, based on an analysis of the structure and sequence. The neural network output, and spatial clustering of the highly scoring residues are then used to predict the location of the active site. A comparison of the performance of differently trained neural networks is presented that shows how information from sequence and structure come together to improve the prediction accuracy of the network. Spatial clustering of the network results provides a reliable way of finding likely active sites. In over 69% of the test cases the active site is correctly predicted, and a further 25% are partially correctly predicted. The failures are generally due to the poor quality of the automatically generated sequence alignments. We also present predictions identifying the active site, and potential functional residues in five recently solved enzyme structures, not used in developing the method. The method correctly identifies the putative active site in each case. In most cases the likely functional residues are identified correctly, as well as some potentially novel functional groups. q 2003 Elsevier Science Ltd. All rights reserved
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paper_title: Predicting DNA-binding proteins: approached from Chou’s pseudo amino acid composition and other specific sequence features

paper_content:

DNA-binding proteins play a pivotal role in gene regulation. It is vitally important to develop an automated and efficient method for timely identification of novel DNA-binding proteins. In this study, we proposed a method based on alone the primary sequences of proteins to predict the DNA-binding proteins. DNA-binding proteins were encoded by autocross-covariance transform, pseudo-amino acid composition, dipeptide composition, respectively and also the different combinations of the three encoded methods; further, these feature matrices were applied to support vector machine classifiers to predict the DNA-binding proteins. All modules were trained and validated by the jackknife cross-validation test. Through comparing the performance of these substituted modules, the best result was obtained from pseudo-amino acid composition with the overall accuracy of 96.6% and the sensitivity of 90.7%. The results suggest that it can efficiently predict the novel DNA-binding proteins only using the primary sequences.
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paper_title: Prediction of protein subcellular location using a combined feature of sequence

paper_content:

To understand the structure and function of a protein, an important task is to know where it occurs in the cell. Thus, a computational method for properly predicting the subcellular location of proteins would be significant in interpreting the original data produced by the large-scale genome sequencing projects. The present work tries to explore an effective method for extracting features from protein primary sequence and find a novel measurement of similarity among proteins for classifying a protein to its proper subcellular location. We considered four locations in eukaryotic cells and three locations in prokaryotic cells, which have been investigated by several groups in the past. A combined feature of primary sequence defined as a 430D (dimensional) vector was utilized to represent a protein, including 20 amino acid compositions, 400 dipeptide compositions and 10 physicochemical properties. To evaluate the prediction performance of this encoding scheme, a jackknife test based on nearest neighbor algorithm was employed. The prediction accuracies for cytoplasmic, extracellular, mitochondrial, and nuclear proteins in the former dataset were 86.3%, 89.2%, 73.5% and 89.4%, respectively, and the total prediction accuracy reached 86.3%. As for the prediction accuracies of cytoplasmic, extracellular, and periplasmic proteins in the latter dataset, the prediction accuracies were 97.4%, 86.0%, and 79.7, respectively, and the total prediction accuracy of 92.5% was achieved. The results indicate that this method outperforms some existing approaches based on amino acid composition or amino acid composition and dipeptide composition.
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paper_title: pSLIP: SVM based protein subcellular localization prediction using multiple physicochemical properties

paper_content:

BackgroundProtein subcellular localization is an important determinant of protein function and hence, reliable methods for prediction of localization are needed. A number of prediction algorithms have been developed based on amino acid compositions or on the N-terminal characteristics (signal peptides) of proteins. However, such approaches lead to a loss of contextual information. Moreover, where information about the physicochemical properties of amino acids has been used, the methods employed to exploit that information are less than optimal and could use the information more effectively.ResultsIn this paper, we propose a new algorithm called pSLIP which uses Support Vector Machines (SVMs) in conjunction with multiple physicochemical properties of amino acids to predict protein subcellular localization in eukaryotes across six different locations, namely, chloroplast, cytoplasmic, extracellular, mitochondrial, nuclear and plasma membrane. The algorithm was applied to the dataset provided by Park and Kanehisa and we obtained prediction accuracies for the different classes ranging from 87.7% – 97.0% with an overall accuracy of 93.1%.ConclusionThis study presents a physicochemical property based protein localization prediction algorithm. Unlike other algorithms, contextual information is preserved by dividing the protein sequences into clusters. The prediction accuracy shows an improvement over other algorithms based on various types of amino acid composition (single, pair and gapped pair). We have also implemented a web server to predict protein localization across the six classes (available at http://pslip.bii.a-star.edu.sg/).
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paper_title: Hybrid SVM-ANFIS for protein subcellular location prediction

paper_content:

Predicting protein subcellular locations may help us understand protein functions and analyse protein interactions with other molecules. Many machine learning and computational techniques have been used to predict protein subcellular locations. In this paper, we propose a new hybrid classification system called SVM-ANFIS based on Support Vector Machines and Adaptive Neuro Fuzzy Inference System for protein subcellular location prediction. The experimental results show that the new system can not only achieve high total accuracies but also improve local accuracies in protein subcellular location prediction.
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paper_title: Using amino acid and peptide composition to predict membrane protein types.

paper_content:

Membrane proteins play an important role in many biological processes and are attractive drug targets. Determination of membrane protein structures or topologies by experimental methods is expensive and time consuming. Effective computational method in predicting the membrane protein types can provide useful information for large amount of protein sequences emerging in the post-genomic era. Although numerous algorithms have addressed this issue, the methods of extracting efficient protein sequence information are very limit. In this study, we provide a method of extracting high order sequence information with the stepwise discriminant analysis. Some important amino acids and peptides that are distinct for different types of the membrane proteins have been identified and their occurrence frequencies in membrane proteins can be used to predict the types of the membrane proteins. Consequently, an accuracy of 86.5% in the cross-validation test, and 99.8% in the resubstitution test has been achieved for a non-redundant dataset, which includes type-I, type-II, multipass transmembrane proteins, lipid chain-anchored and GPI-anchored membrane proteins. The fingerprint features of the identified peptides in each membrane protein type are also discussed.
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paper_title: Prediction of DNA-binding residues from protein sequence information using random forests

paper_content:

Background ::: Protein-DNA interactions are involved in many biological processes essential for cellular function. To understand the molecular mechanism of protein-DNA recognition, it is necessary to identify the DNA-binding residues in DNA-binding proteins. However, structural data are available for only a few hundreds of protein-DNA complexes. With the rapid accumulation of sequence data, it becomes an important but challenging task to accurately predict DNA-binding residues directly from amino acid sequence data.
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paper_title: SherLoc2: a high-accuracy hybrid method for predicting subcellular localization of proteins.

paper_content:

SherLoc2 is a comprehensive high-accuracy subcellular localization prediction system. It is applicable to animal, fungal, and plant proteins and covers all main eukaryotic subcellular locations. SherLoc2 integrates several sequence-based features as well as text-based features. In addition, we incorporate phylogenetic profiles and Gene Ontology (GO) terms derived from the protein sequence to considerably improve the prediction performance. SherLoc2 achieves an overall classification accuracy of up to 93% in 5-fold cross-validation. A novel feature, DiaLoc, allows users to manually provide their current background knowledge by describing a protein in a short abstract which is then used to improve the prediction. SherLoc2 is available both as a free Web service and as a stand-alone version at http://www-bs.informatik.uni-tuebingen.de/Services/SherLoc2.
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paper_title: Prediction of DNA-binding residues from sequence

paper_content:

Motivation: Thousands of proteins are known to bind to DNA; for most of them the mechanism of action and the residues that bind to DNA, i.e. the binding sites, are yet unknown. Experimental identification of binding sites requires expensive and laborious methods such as mutagenesis and binding essays. Hence, such studies are not applicable on a large scale. If the 3D structure of a protein is known, it is often possible to predict DNA-binding sites in silico. However, for most proteins, such knowledge is not available. ::: ::: Results: It has been shown that DNA-binding residues have distinct biophysical characteristics. Here we demonstrate that these characteristics are so distinct that they enable accurate prediction of the residues that bind DNA directly from amino acid sequence, without requiring any additional experimental or structural information. In a cross-validation based on the largest non-redundant dataset of high-resolution protein–DNA complexes available today, we found that 89% of our predictions are confirmed by experimental data. Thus, it is now possible to identify DNA-binding sites on a proteomic scale even in the absence of any experimental data or 3D-structural information. ::: ::: Availability: http://cubic.bioc.columbia.edu/services/disis ::: ::: Contact: yo135@columbia.edu
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paper_title: Identification of catalytic residues from protein structure using support vector machine with sequence and structural features.

paper_content:

Identification of catalytic residues can provide valuable insights into protein function. With the increasing number of protein 3D structures having been solved by X-ray crystallography and NMR techniques, it is highly desirable to develop an efficient method to identify their catalytic sites. In this paper, we present an SVM method for the identification of catalytic residues using sequence and structural features. The algorithm was applied to the 2096 catalytic residues derived from Catalytic Site Atlas database. We obtained overall prediction accuracy of 88.6% from 10-fold cross validation and 95.76% from resubstitution test. Testing on the 254 catalytic residues shows our method can correctly predict all 254 residues. This result suggests the usefulness of our approach for facilitating the identification of catalytic residues from protein structures.
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paper_title: Robust recognition of zinc binding sites in proteins

paper_content:

Metals play a variety of roles in biological processes, and hence their presence in a protein structure can yield vital functional information. Because the residues that coordinate a metal often undergo conformational changes upon binding, detection of binding sites based on simple geometric criteria in proteins without bound metal is difficult. However, aspects of the physicochemical environment around a metal binding site are often conserved even when this structural rearrangement occurs. We have developed a Bayesian classifier using known zinc binding sites as positive training examples and nonmetal binding regions that nonetheless contain residues frequently observed in zinc sites as negative training examples. In order to allow variation in the exact positions of atoms, we average a variety of biochemical and biophysical properties in six concentric spherical shells around the site of interest. At a specificity of 99.8%, this method achieves 75.5% sensitivity in unbound proteins at a positive predictive value of 73.6%. We also test its accuracy on predicted protein structures obtained by homology modeling using templates with 30%–50% sequence identity to the target sequences. At a specificity of 99.8%, we correctly identify at least one zinc binding site in 65.5% of modeled proteins. Thus, in many cases, our model is accurate enough to identify metal binding sites in proteins of unknown structure for which no high sequence identity homologs of known structure exist. Both the source code and a Web interface are available to the public at http://feature.stanford.edu/metals.
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paper_title: Machine learning approaches for the prediction of signal peptides and other protein sorting signals

paper_content:

Prediction of protein sorting signals from the sequence of amino acids has great importance in the field of proteomics today. Recently, the growth of protein databases, combined with machine learning approaches, such as neural networks and hidden Markov models, have made it possible to achieve a level of reliability where practical use in, for example automatic database annotation is feasible. In this review, we concentrate on the present status and future perspectives of SignalP, our neural network-based method for prediction of the most well-known sorting signal: the secretory signal peptide. We discuss the problems associated with the use of SignalP on genomic sequences, showing that signal peptide prediction will improve further if integrated with predictions of start codons and transmembrane helices. As a step towards this goal, a hidden Markov model version of SignalP has been developed, making it possible to discriminate between cleaved signal peptides and uncleaved signal anchors. Furthermore, we show how SignalP can be used to characterize putative signal peptides from an archaeon, Methanococcus jannaschii. Finally, we briefly review a few methods for predicting other protein sorting signals and discuss the future of protein sorting prediction in general.
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paper_title: Using evolutionary and structural information to predict DNA-binding sites on DNA-binding proteins

paper_content:

Proteins that interact with DNA are involved in a number of fundamental biological activities such as DNA replication, transcription, and repair. A reliable identification of DNA-binding sites in DNA-binding proteins is important for functional annotation, site-directed mutagenesis, and modeling protein-DNA interactions. We apply Support Vector Machine (SVM), a supervised pattern recognition method, to predict DNA-binding sites in DNA-binding proteins using the following features: amino acid sequence, profile of evolutionary conservation of sequence positions, and low-resolution structural information. We use a rigorous statistical approach to study the performance of predictors that utilize different combinations of features and how this performance is affected by structural and sequence properties of proteins. Our results indicate that an SVM predictor based on a properly scaled profile of evolutionary conservation in the form of a position specific scoring matrix (PSSM) significantly outperforms a PSSM-based neural network predictor. The highest accuracy is achieved by SVM predictor that combines the profile of evolutionary conservation with low-resolution structural information. Our results also show that knowledge-based predictors of DNA-binding sites perform significantly better on proteins from mainly-alpha structural class and that the performance of these predictors is significantly correlated with certain structural and sequence properties of proteins. These observations suggest that it may be possible to assign a reliability index to the overall accuracy of the prediction of DNA-binding sites in any given protein using its sequence and structural properties. A web-server implementation of the predictors is freely available online at http://lcg.rit.albany.edu/dp-bind/.
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paper_title: Predicting Protein Subcellular Location Using Chous Pseudo Amino Acid Composition and Improved Hybrid Approach

paper_content:

The location of a protein in a cell is closely correlated with its biological function. Based on the concept that the protein subcellular location is mainly determined by its amino acid and pseudo amino acid composition (PseAA), a new algorithm of increment of diversity combined with support vector machine is proposed to predict the protein subcellular location. The subcellular locations of plant and non-plant proteins are investigated by our method. The overall prediction accuracies in jackknife test are 88.3% for the eukaryotic plant proteins and 92.4% for the eukaryotic non-plant proteins, respectively. In order to estimate the effect of the sequence identity on predictive result, the proteins with sequence identity <or=40% are selected. The overall success rates of prediction are 86.2% and 92.3% for plant and non-plant proteins in jackknife test, respectively.
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paper_title: Support Vector Machine-based method for predicting subcellular localization of mycobacterial proteins using evolutionary information and motifs

paper_content:

BackgroundIn past number of methods have been developed for predicting subcellular location of eukaryotic, prokaryotic (Gram-negative and Gram-positive bacteria) and human proteins but no method has been developed for mycobacterial proteins which may represent repertoire of potent immunogens of this dreaded pathogen. In this study, attempt has been made to develop method for predicting subcellular location of mycobacterial proteins.ResultsThe models were trained and tested on 852 mycobacterial proteins and evaluated using five-fold cross-validation technique. First SVM (Support Vector Machine) model was developed using amino acid composition and overall accuracy of 82.51% was achieved with average accuracy (mean of class-wise accuracy) of 68.47%. In order to utilize evolutionary information, a SVM model was developed using PSSM (Position-Specific Scoring Matrix) profiles obtained from PSI-BLAST (Position-Specific Iterated BLAST) and overall accuracy achieved was of 86.62% with average accuracy of 73.71%. In addition, HMM (Hidden Markov Model), MEME/MAST (Multiple Em for Motif Elicitation/Motif Alignment and Search Tool) and hybrid model that combined two or more models were also developed. We achieved maximum overall accuracy of 86.8% with average accuracy of 89.00% using combination of PSSM based SVM model and MEME/MAST. Performance of our method was compared with that of the existing methods developed for predicting subcellular locations of Gram-positive bacterial proteins.ConclusionA highly accurate method has been developed for predicting subcellular location of mycobacterial proteins. This method also predicts very important class of proteins that is membrane-attached proteins. This method will be useful in annotating newly sequenced or hypothetical mycobacterial proteins. Based on above study, a freely accessible web server TBpred http://www.imtech.res.in/raghava/tbpred/ has been developed.
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paper_title: Prediction of nuclear receptors with optimal pseudo amino acid composition.

paper_content:

Nuclear receptors are involved in multiple cellular signaling pathways that affect and regulate processes such as organ development and maintenance, ion transport, homeostasis, and apoptosis. In this article, an optimal pseudo amino acid composition based on physicochemical characters of amino acids is suggested to represent proteins for predicting the subfamilies of nuclear receptors. Six physicochemical characters of amino acids were adopted to generate the protein sequence features via web server PseAAC. The optimal values of the rank of correlation factor and the weighting factor about PseAAC were determined to get the appropriate descriptor of proteins that leads to the best performance. A nonredundant dataset of nuclear receptors in four subfamilies is constructed to evaluate the method using support vector machines. An overall accuracy of 99.6% was achieved in the fivefold cross-validation test as well as the jackknife test, and an overall accuracy of 98.4% was reached in a blind dataset test. The performance is very competitive with that of some previous methods.
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paper_title: GPCRsclass: a web tool for the classification of amine type of G-protein-coupled receptors

paper_content:

The receptors of amine subfamily are specifically major drug targets for therapy of nervous disorders and psychiatric diseases. The recognition of novel amine type of receptors and their cognate ligands is of paramount interest for pharmaceutical companies. In the past, Chou and co-workers have shown that different types of amine receptors are correlated with their amino acid composition and are predictable on its basis with considerable accuracy [Elrod and Chou (2002) Protein Eng., 15, 713–715]. This motivated us to develop a better method for the recognition of novel amine receptors and for their further classification. The method was developed on the basis of amino acid composition and dipeptide composition of proteins using support vector machine. The method was trained and tested on 167 proteins of amine subfamily of G-protein-coupled receptors (GPCRs). The method discriminatedaminesubfamilyofGPCRsfromglobular proteins with Matthew’s correlation coefficient of 0.98 and 0.99 using amino acid composition and dipeptide composition, respectively. In classifying different types of amine receptors using amino acid composition and dipeptide composition, the method achieved an accuracy of 89.8 and 96.4%, respectively. The performance of the method was evaluated using 5-fold cross-validation. The dipeptide composition based method predicted 67.6% of protein sequences with an accuracy of 100% with a reliability index >5. A web server GPCRsclass has been developed for predicting amine-binding receptors from its amino acid sequence [http://www.imtech.res.in/raghava/gpcrsclass/ and http://bioinformatics.uams.edu/raghava/gpersclass/ (mirror site)].
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paper_title: BindN+ for accurate prediction of DNA and RNA-binding residues from protein sequence features

paper_content:

BackgroundUnderstanding how biomolecules interact is a major task of systems biology. To model protein-nucleic acid interactions, it is important to identify the DNA or RNA-binding residues in proteins. Protein sequence features, including the biochemical property of amino acids and evolutionary information in terms of position-specific scoring matrix (PSSM), have been used for DNA or RNA-binding site prediction. However, PSSM is rather designed for PSI-BLAST searches, and it may not contain all the evolutionary information for modelling DNA or RNA-binding sites in protein sequences.ResultsIn the present study, several new descriptors of evolutionary information have been developed and evaluated for sequence-based prediction of DNA and RNA-binding residues using support vector machines (SVMs). The new descriptors were shown to improve classifier performance. Interestingly, the best classifiers were obtained by combining the new descriptors and PSSM, suggesting that they captured different aspects of evolutionary information for DNA and RNA-binding site prediction. The SVM classifiers achieved 77.3% sensitivity and 79.3% specificity for prediction of DNA-binding residues, and 71.6% sensitivity and 78.7% specificity for RNA-binding site prediction.ConclusionsPredictions at this level of accuracy may provide useful information for modelling protein-nucleic acid interactions in systems biology studies. We have thus developed a web-based tool called BindN+ (http://bioinfo.ggc.org/bindn+/) to make the SVM classifiers accessible to the research community.
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paper_title: Prediction of protein subcellular locations using fuzzy k-NN method

paper_content:

Motivation: Protein localization data are a valuable information resource helpful in elucidating protein functions. It is highly desirable to predict a protein's subcellular locations automatically from its sequence. ::: ::: Results: In this paper, fuzzy k-nearest neighbors (k-NN) algorithm has been introduced to predict proteins' subcellular locations from their dipeptide composition. The prediction is performed with a new data set derived from version 41.0 SWISS-PROT databank, the overall predictive accuracy about 80% has been achieved in a jackknife test. The result demonstrates the applicability of this relative simple method and possible improvement of prediction accuracy for the protein subcellular locations. We also applied this method to annotate six entirely sequenced proteomes, namely Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster, Oryza sativa, Arabidopsis thaliana and a subset of all human proteins. ::: ::: Availability: Supplementary information and subcellular location annotations for eukaryotes are available at http://166.111.30.65/hying/fuzzy_loc.htm
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paper_title: Support Vector Machine Approach for Protein Subcellular Localization Prediction

paper_content:

Motivation: Subcellular localization is a key functional characteristic of proteins. A fully automatic and reliable prediction system for protein subcellular localization is needed, especially for the analysis of large-scale genome sequences. Results: In this paper, Support Vector Machine has been introduced to predict the subcellular localization of proteins from their amino acid compositions. The total prediction accuracies reach 91.4% for three subcellular locations in prokaryotic organisms and 79.4% for four locations in eukaryotic organisms. Predictions by our approach are robust to errors in the protein N-terminal sequences. This new approach provides superior prediction performance compared with existing algorithms based on amino acid composition and can be a complementary method to other existing methods based on sorting signals. Availability: A web server implementing the prediction method is available at http://www.bioinfo.tsinghua.edu.cn/ SubLoc/.
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paper_title: A novel method for predicting protein subcellular localization based on pseudo amino acid composition

paper_content:

In this paper, a novel approach, ELM-PCA, is introduced for the first time to predict protein subcellular localization. Firstly, Protein Samples are represented by the pseudo amino acid composition (PseAAC). Secondly, the principal component analysis (PCA) is employed to extract essential features. Finally, the Elman Recurrent Neural Network (RNN) is used as a classifier to identify the protein sequences. The results demonstrate that the proposed approach is effective and practical.
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paper_title: High-accuracy prediction of bacterial type III secreted effectors based on position-specific amino acid composition profiles

paper_content:

MOTIVATION ::: Bacterial type III secreted (T3S) effectors are delivered into host cells specifically via type III secretion systems (T3SSs), which play important roles in the interaction between bacteria and their hosts. Previous computational methods for T3S protein prediction have only achieved limited accuracy, and distinct features for effective T3S protein prediction remain to be identified. ::: ::: ::: RESULTS ::: In this work, a distinctive N-terminal position-specific amino acid composition (Aac) feature was identified for T3S proteins. A large portion (∼50%) of T3S proteins exhibit distinct position-specific Aac features that can tolerate position shift. A classifier, BPBAac, was developed and trained using Support Vector Machine (SVM) based on the Aac feature extracted using a Bi-profile Bayes model. We demonstrated that the BPBAac model outperformed other implementations in classification of T3S and non-T3S proteins, giving an average sensitivity of ∼90.97% and an average selectivity of ∼97.42% in a 5-fold cross-validation evaluation. The model was also robust when a small-size training dataset was used. The fact that the position-specific Aac feature is commonly found in T3S proteins across different bacterial species gives this model wide application. To demonstrate the model's application, a genome-wide prediction of T3S effector proteins was performed for Ralstonia solanacearum, an important plant pathogenic bacterium, and a number of putative candidates were identified using this model. ::: ::: ::: AVAILABILITY ::: An R package of BPBAac tool is freely downloadable from: http://biocomputer.bio.cuhk.edu.hk/softwares/BPBAac.
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paper_title: Prediction of subcellular protein localization based on functional domain composition.

paper_content:

Assigning subcellular localization (SL) to proteins is one of the major tasks of functional proteomics. Despite the impressive technical advances of the past decades, it is still time-consuming and laborious to experimentally determine SL on a high throughput scale. Thus, computational predictions are the preferred method for large-scale assignment of protein SL, and if appropriate, followed up by experimental studies. In this report, using a machine learning approach, the Nearest Neighbor Algorithm (NNA), we developed a prediction system for protein SL in which we incorporated a protein functional domain profile. The overall accuracy achieved by this system is 93.96%. Furthermore, comparisons with other methods have been conducted to demonstrate the validity and efficiency of our prediction system. We also provide an implementation of our Subcellular Location Prediction System (SLPS), which is available at http://pcal.biosino.org.
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paper_title: PreDNA: accurate prediction of DNA-binding sites in proteins by integrating sequence and geometric structure information

paper_content:

Motivation: Protein–DNA interactions often take part in various crucial processes, which are essential for cellular function. The identification of DNA-binding sites in proteins is important for understanding the molecular mechanisms of protein–DNA interaction. Thus, we have developed an improved method to predict DNA-binding sites by integrating structural alignment algorithm and support vector machine–based methods. Results: Evaluated on a new non-redundant protein set with 224 chains, the method has 80.7% sensitivity and 82.9% specificity in the 5-fold cross-validation test. In addition, it predicts DNA-binding sites with 85.1% sensitivity and 85.3% specificity when tested on a dataset with 62 protein–DNA complexes. Compared with a recently published method, BindNþ, our method predicts DNA-binding sites with a 7% better area under the receiver operating characteristic curve value when tested on the same dataset. Many important problems in cell biology require the dense non-linear interactions between functional modules be considered. Thus, our prediction method will be useful in detecting such complex interactions. Availability: The PreDNA webserver is freely available at: http://202.
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paper_title: Prediction of G-protein-coupled receptor classes in low homology using Chou's pseudo amino acid composition with approximate entropy and hydrophobicity patterns.

paper_content:

We use approximate entropy and hydrophobicity patterns to predict G-protein-coupled receptors. Adaboost classifier is adopted as the prediction engine. A low homology dataset is used to validate the proposed method. Compared with the results reported, the successful rate is encouraging. The source code is written by Matlab.
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paper_title: Prediction of the functional class of metal-binding proteins from sequence derived physicochemical properties by support vector machine approach

paper_content:

Metal-binding proteins play important roles in structural stability, signaling, regulation, transport, immune response, metabolism control, and metal homeostasis. Because of their functional and sequence diversity, it is desirable to explore additional methods for predicting metal-binding proteins irrespective of sequence similarity. This work explores support vector machines (SVM) as such a method. SVM prediction systems were developed by using 53,333 metal-binding and 147,347 non-metal-binding proteins, and evaluated by an independent set of 31,448 metal-binding and 79,051 non-metal-binding proteins. The computed prediction accuracy is 86.3%, 81.6%, 83.5%, 94.0%, 81.2%, 85.4%, 77.6%, 90.4%, 90.9%, 74.9% and 78.1% for calcium-binding, cobalt-binding, copper-binding, iron-binding, magnesium-binding, manganese-binding, nickel-binding, potassium-binding, sodium-binding, zinc-binding, and all metal-binding proteins respectively. The accuracy for the non-member proteins of each class is 88.2%, 99.9%, 98.1%, 91.4%, 87.9%, 94.5%, 99.2%, 99.9%, 99.9%, 98.0%, and 88.0% respectively. Comparable accuracies were obtained by using a different SVM kernel function. Our method predicts 67% of the 87 metal-binding proteins non-homologous to any protein in the Swissprot database and 85.3% of the 333 proteins of known metal-binding domains as metal-binding. These suggest the usefulness of SVM for facilitating the prediction of metal-binding proteins. Our software can be accessed at the SVMProt server http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi.
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paper_title: Structure-based prediction of RNA-binding domains and RNA-binding sites and application to structural genomics targets

paper_content:

Mechanistic understanding of many key cellular processes often involves identification of RNA binding proteins (RBPs) and RNA binding sites in two separate steps. Here, they are predicted simultaneously by structural alignment to known protein-RNA complex structures followed by binding assessment with a DFIRE-based statistical energy function. This method achieves 98% accuracy and 91% precision for predicting RBPs and 93% accuracy and 78% precision for predicting RNA-binding amino-acid residues for a large benchmark of 212 RNA binding and 6761 non-RNA binding domains (leave-one-out cross-validation). Additional tests revealed that the method makes no false positive prediction from 311 DNA binding domains but correctly detects six domains binding with both DNA and RNA. In addition, it correctly identified 31 of 75 unbound RNA-binding domains with 92% accuracy and 65% precision for predicted binding residues and achieved 86% success rate in its application to SCOP RNA binding domain superfamily (Structural Classification Of Proteins). It further predicts 25 targets as RBPs in 2076 structural genomics targets: 20 of 25 predicted ones (80%) are putatively RNA binding. The superior performance over existing methods indicates the importance of dividing structures into domains, using a Z-score to measure relative structural similarity, and a statistical energy function to measure protein-RNA binding affinity.
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paper_title: NRPred-FS: A Feature Selection based Two-level Predictor for Nuclear Receptors

paper_content:

Motivation: Nuclear receptors (NRs) play a role in all developmental and physiological processes and are important drug targets in a wide variety of disease and healthy states. In the past years, to identify NRs and their subfamilies with high throughput and low-cost, many machine learning methods have been introduced. However, these predictors are all developed based on old dataset in the NucleaRDB, what’s more, no feature selection technique is employed, so that the performances are very limited. ::: Result: In this study, a feature selection based two-level predictor, called NRPred-FS, is developed that can be used to identify a query protein as a nuclear receptor or not based on its sequence information alone, if it is, the prediction will be automatically continued to further identify it among the following eight subfamilies: (1) Thyroid hormone like (NR1), (2) HNF4-like (NR2), (3) Estrogen like, (4) Nerve growth factor IB-like (NR4), (5) Fushi tarazu-F1 like (NR5), (6) Germ cell nuclear factor like (NR6), (7) knirps like (NR0A), and (8) DAX like (NR0B). The nuclear receptor sequences are encoded as sequence-derived feature vectors formed by incorporating various physicochemical and statistical features. Furthermore, the features set are optimized by forward feature selection algorithm for reducing the feature dimensions and for getting higher classifying accuracy. As a demonstration, this method gone through rigorous testing on a benchmark datasets derived from the latest version of NucleaRDB and UniProt. The overall prediction accuracies of leave-one-out cross-validation were about 97% and 93% in the first and second level respectively. As a convenience to the users, the powerful predictor, NRPred-FS, is freely accessible at http://www.jci-bioinfo.cn/NRPred-FS. Hopefully it will be a useful vehicle for identifying NRs and their subfamilies.
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paper_title: Signal-3L: A 3-layer approach for predicting signal peptides.

paper_content:

Functioning as an "address tag" that directs nascent proteins to their proper cellular and extracellular locations, signal peptides have become a crucial tool in finding new drugs or reprogramming cells for gene therapy. To effectively and timely use such a tool, however, the first important thing is to develop an automated method for rapidly and accurately identifying the signal peptide for a given nascent protein. With the avalanche of new protein sequences generated in the post-genomic era, the challenge has become even more urgent and critical. In this paper, we have developed a novel method for predicting signal peptide sequences and their cleavage sites in human, plant, animal, eukaryotic, Gram-positive, and Gram-negative protein sequences, respectively. The new predictor is called Signal-3L that consists of three prediction engines working, respectively, for the following three progressively deepening layers: (1) identifying a query protein as secretory or non-secretory by an ensemble classifier formed by fusing many individual OET-KNN (optimized evidence-theoretic K nearest neighbor) classifiers operated in various dimensions of PseAA (pseudo amino acid) composition spaces; (2) selecting a set of candidates for the possible signal peptide cleavage sites of a query secretory protein by a subsite-coupled discrimination algorithm; (3) determining the final cleavage site by fusing the global sequence alignment outcome for each of the aforementioned candidates through a voting system. Signal-3L is featured by high success prediction rates with short computational time, and hence is particularly useful for the analysis of large-scale datasets. Signal-3L is freely available as a web-server at http://chou.med.harvard.edu/bioinf/Signal-3L/ or http://202.120.37.186/bioinf/Signal-3L, where, to further support the demand of the related areas, the signal peptides identified by Signal-3L for all the protein entries in Swiss-Prot databank that do not have signal peptide annotations or are annotated with uncertain terms but are classified by Signal-3L as secretory proteins are provided in a downloadable file. The large-scale file is prepared with Microsoft Excel and named "Tab-Signal-3L.xls", and will be updated once a year to include new protein entries and reflect the continuous development of Signal-3L.
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paper_title: iNR-PhysChem: A Sequence-Based Predictor for Identifying Nuclear Receptors and Their Subfamilies via Physical-Chemical Property Matrix

paper_content:

Nuclear receptors (NRs) form a family of ligand-activated transcription factors that regulate a wide variety of biological processes, such as homeostasis, reproduction, development, and metabolism. Human genome contains 48 genes encoding NRs. These receptors have become one of the most important targets for therapeutic drug development. According to their different action mechanisms or functions, NRs have been classified into seven subfamilies. With the avalanche of protein sequences generated in the postgenomic age, we are facing the following challenging problems. Given an uncharacterized protein sequence, how can we identify whether it is a nuclear receptor? If it is, what subfamily it belongs to? To address these problems, we developed a predictor called iNR-PhysChem in which the protein samples were expressed by a novel mode of pseudo amino acid composition (PseAAC) whose components were derived from a physical-chemical matrix via a series of auto-covariance and cross-covariance transformations. It was observed that the overall success rate achieved by iNR-PhysChem was over 98% in identifying NRs or non-NRs, and over 92% in identifying NRs among the following seven subfamilies: NR1--thyroid hormone like, NR2--HNF4-like, NR3--estrogen like, NR4--nerve growth factor IB-like, NR5--fushi tarazu-F1 like, NR6--germ cell nuclear factor like, and NR0--knirps like. These rates were derived by the jackknife tests on a stringent benchmark dataset in which none of protein sequences included has ≥60% pairwise sequence identity to any other in a same subset. As a user-friendly web-server, iNR-PhysChem is freely accessible to the public at either http://www.jci-bioinfo.cn/iNR-PhysChem or http://icpr.jci.edu.cn/bioinfo/iNR-PhysChem. Also a step-by-step guide is provided on how to use the web-server to get the desired results without the need to follow the complicated mathematics involved in developing the predictor. It is anticipated that iNR-PhysChem may become a useful high throughput tool for both basic research and drug design.
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paper_title: Finding Signal Peptides in Human Protein Sequences Using Recurrent Neural Networks

paper_content:

A new approach called Sigfind for the prediction of signal peptides in human protein sequences is introduced. The method is based on the bidirectional recurrent neural network architecture. The modifications to this architecture and a better learning algorithm result in a very accurate identification of signal peptides (99.5% correct in fivefold crossvalidation). The Sigfind system is available on the WWW for predictions (http://www.stepc.gr/synaptic/sigfind.html).
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paper_title: Analysis and prediction of DNA-binding proteins and their binding residues based on composition, sequence and structural information

paper_content:

MOTIVATION ::: Though vitally important to cell function, the mechanism of protein-DNA binding has not yet been completely understood. We therefore analysed the relationship between DNA binding and protein sequence composition, solvent accessibility and secondary structure. Using non-redundant databases of transcription factors and protein-DNA complexes, neural network models were developed to utilize the information present in this relationship to predict DNA-binding proteins and their binding residues. ::: ::: ::: RESULTS ::: Sequence composition was found to provide sufficient information to predict the probability of its binding to DNA with nearly 69% sensitivity at 64% accuracy for the considered proteins; sequence neighbourhood and solvent accessibility information were sufficient to make binding site predictions with 40% sensitivity at 79% accuracy. Detailed analysis of binding residues shows that some three- and five-residue segments frequently bind to DNA and that solvent accessibility plays a major role in binding. Although, binding behaviour was not associated with any particular secondary structure, there were interesting exceptions at the residue level. Over-representation of some residues in the binding sites was largely lost at the total sequence level, but a different kind of compositional preference was observed in DNA-binding proteins.
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paper_title: Prediction of RNA binding sites in proteins from amino acid sequence

paper_content:

RNA-protein interactions are vitally important in a wide range of biological processes, including regulation of gene expression, protein synthesis, and replication and assembly of many viruses. We have developed a computational tool for predicting which amino acids of an RNA binding protein participate in RNA-protein interactions, using only the protein sequence as input. RNABindR was developed using machine learning on a validated nonredundant data set of interfaces from known RNA-protein complexes in the Protein Data Bank. It generates a classifier that captures primary sequence signals sufficient for predicting which amino acids in a given protein are located in the RNA-protein interface. In leave-one-out cross-validation experiments, RNABindR identifies interface residues with >85% overall accuracy. It can be calibrated by the user to obtain either high specificity or high sensitivity for interface residues. RNABindR, implementing a Naive Bayes classifier, performs as well as a more complex neural network classifier (to our knowledge, the only previously published sequence-based method for RNA binding site prediction) and offers the advantages of speed, simplicity and interpretability of results. RNABindR predictions on the human telomerase protein hTERT are in good agreement with experimental data. The availability of computational tools for predicting which residues in an RNA binding protein are likely to contact RNA should facilitate design of experiments to directly test RNA binding function and contribute to our understanding of the diversity, mechanisms, and regulation of RNA-protein complexes in biological systems. (RNABindR is available as a Web tool from http://bindr.gdcb.iastate.edu.).
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paper_title: Predicting protein submitochondria locations by combining different descriptors into the general form of Chou’s pseudo amino acid composition

paper_content:

Knowledge of the submitochondria location of protein is integral to understanding its function and a necessity in the proteomics era. In this work, a new submitochondria data set is constructed, and an approach for predicting protein submitochondria locations is proposed by combining the amino acid composition, dipeptide composition, reduced physicochemical properties, gene ontology, evolutionary information, and pseudo-average chemical shift. The overall prediction accuracy is 93.57% for the submitochondria location and 97.79% for the three membrane protein types in the mitochondria inner membrane using the algorithm of the increment of diversity combined with the support vector machine. The performance of the pseudo-average chemical shift is excellent. For contrast, the method is also used to predict submitochondria locations in the data set constructed by Du and Li; an accuracy of 94.95% is obtained by our method, which is better than that of other existing methods.
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paper_title: An Automated Combination of Kernels for Predicting Protein Subcellular Localization

paper_content:

Protein subcellular localization is a crucial ingredient to many important inferences about cellular processes, including prediction of protein function and protein interactions. While many predictive computational tools have been proposed, they tend to have complicated architectures and require many design decisions from the developer. ::: ::: Here we utilize the multiclass support vector machine (m-SVM) method to directly solve protein subcellular localization without resorting to the common approach of splitting the problem into several binary classification problems. We further propose a general class of protein sequence kernels which considers all motifs, including motifs with gaps. Instead of heuristically selecting one or a few kernels from this family, we utilize a recent extension of SVMs that optimizes over multiple kernels simultaneously. This way, we automatically search over families of possible amino acid motifs. ::: ::: We compare our automated approach to three other predictors on four different datasets, and show that we perform better than the current state of the art. Further, our method provides some insights as to which sequence motifs are most useful for determining subcellular localization, which are in agreement with biological reasoning. Data files, kernel matrices and open source software are available at http://www.fml.mpg.de/raetsch/projects/protsubloc .
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paper_title: ProLoc: Prediction of protein subnuclear localization using SVM with automatic selection from physicochemical composition features

paper_content:

Accurate prediction methods of protein subnuclear localizations rely on the cooperation between informative features and classifier design. Support vector machine (SVM) based learning methods are shown effective for predictions of protein subcellular and subnuclear localizations. This study proposes an evolutionary support vector machine (ESVM) based classifier with automatic selection from a large set of physicochemical composition (PCC) features to design an accurate system for predicting protein subnuclear localization, named ProLoc. ESVM using an inheritable genetic algorithm combined with SVM can automatically determine the best number m of PCC features and identify m out of 526 PCC features simultaneously. To evaluate ESVM, this study uses two datasets SNL6 and SNL9, which have 504 proteins localized in 6 subnuclear compartments and 370 proteins localized in 9 subnuclear compartments. Using a leave-one-out cross-validation, ProLoc utilizing the selected m = 33 and 28 PCC features has accuracies of 56.37% for SNL6 and 72.82% for SNL9, which are better than 51.4% for the SVM-based system using k-peptide composition features applied on SNL6, and 64.32% for an optimized evidence-theoretic k-nearest neighbor classifier utilizing pseudo amino acid composition applied on SNL9, respectively.
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paper_title: Prediction of DNA-binding residues in proteins from amino acid sequences using a random forest model with a hybrid feature

paper_content:

Motivation: In this work, we aim to develop a computational approach for predicting DNA-binding sites in proteins from amino acid sequences. To avoid overfitting with this method, all available DNA-binding proteins from the Protein Data Bank (PDB) are used to construct the models. The random forest (RF) algorithm is used because it is fast and has robust performance for different parameter values. A novel hybrid feature is presented which incorporates evolutionary information of the amino acid sequence, secondary structure (SS) information and orthogonal binary vector (OBV) information which reflects the characteristics of 20 kinds of amino acids for two physical–chemical properties (dipoles and volumes of the side chains). The numbers of binding and non-binding residues in proteins are highly unbalanced, so a novel scheme is proposed to deal with the problem of imbalanced datasets by downsizing the majority class. ::: ::: Results: The results show that the RF model achieves 91.41% overall accuracy with Matthew's correlation coefficient of 0.70 and an area under the receiver operating characteristic curve (AUC) of 0.913. To our knowledge, the RF method using the hybrid feature is currently the computationally optimal approach for predicting DNA-binding sites in proteins from amino acid sequences without using three-dimensional (3D) structural information. We have demonstrated that the prediction results are useful for understanding protein–DNA interactions. ::: ::: Availability: DBindR web server implementation is freely available at http://www.cbi.seu.edu.cn/DBindR/DBindR.htm. ::: ::: Contact: [email protected] ::: ::: Supplementary information:Supplementary data are available at Bioinformatics online.
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paper_title: Classification of nuclear receptor subfamilies with RBF kernel in support vector machine

paper_content:

Nuclear receptors (NRs) are ligand-inducible transcription factors that regulate diverse functions as a superfamily of crucial medical significance. Because of their involvement in many physiological and pathological processes, the development of methods to infer the different NR subfamilies has become an important goal in biomedical research. In this paper we introduce a sequence-based computational approach-Support Vector Machine to classify the 19 subfamilies of NRs. We use 4-tuple residue composition instead of dipeptide composition to encode the NR sequences. The overall predictive accuracy about 96% has been achieved in a five fold cross-validation.
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paper_title: Prediction of RNA-binding residues in proteins from primary sequence using an enriched random forest model with a novel hybrid feature

paper_content:

The identification of RNA-binding residues in proteins is important in several areas such as protein function, posttranscriptional regulation and drug design. We have developed PRBR (Prediction of RNA Binding Residues), a novel method for identifying RNA-binding residues from amino acid sequences. Our method combines a hybrid feature with the enriched random forest (ERF) algorithm. The hybrid feature is composed of predicted secondary structure information and three novel features: evolutionary information combined with conservation information of the physicochemical properties of amino acids and the information about dependency of amino acids with regards to polarity-charge and hydrophobicity in the protein sequences. Our results demonstrate that the PRBR model achieves 0.5637 Matthew's correlation coefficient (MCC) and 88.63% overall accuracy (ACC) with 53.70% sensitivity (SE) and 96.97% specificity (SP). By comparing the performance of each feature we found that all three novel features contribute to the improved predictions. Area under the curve (AUC) statistics from receiver operating characteristic curve analysis was compared between PRBR model and other models. The results show that PRBR achieves the highest AUC value (0.8675) which represents that PRBR attains excellent performance on predicting the RNA-binding residues in proteins. The PRBR web-server implementation is freely available at http://www.cbi.seu.edu.cn/PRBR/.
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paper_title: BindN: a web-based tool for efficient prediction of DNA and RNA binding sites in amino acid sequences

paper_content:

BindN (http://bioinformatics.ksu.edu/bindn/) takes an amino acid sequence as input and predicts potential DNA or RNA-binding residues with support vector machines (SVMs). Protein datasets with known DNA or RNA-binding residues were selected from the Protein Data Bank (PDB), and SVM models were constructed using data instances encoded with three sequence features, including the side chain pKa value, hydrophobicity index and molecular mass of an amino acid. The results suggest that DNA-binding residues can be predicted at 69.40% sensitivity and 70.47% specificity, while prediction of RNA-binding residues achieves 66.28% sensitivity and 69.84% specificity. When compared with previous studies, the SVM models appear to be more accurate and more efficient for online predictions. BindN provides a useful tool for understanding the function of DNA and RNA-binding proteins based on primary sequence data.
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paper_title: Protein subcellular localization prediction for Gram-negative bacteria using amino acid subalphabets and a combination of multiple support vector machines

paper_content:

Background ::: Predicting the subcellular localization of proteins is important for determining the function of proteins. Previous works focused on predicting protein localization in Gram-negative bacteria obtained good results. However, these methods had relatively low accuracies for the localization of extracellular proteins. This paper studies ways to improve the accuracy for predicting extracellular localization in Gram-negative bacteria.
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paper_title: Prediction of Membrane Protein Types by an Ensemble Classifier Based on Pseudo Amino Acid Composition and Approximate Entropy

paper_content:

Membrane proteins have different types. The function of a membrane protein is closely correlated with the type it belongs to. For a given membrane protein, it belongs to the following eight categories [1]: (1) type I, (2) type II, (3) type III, (4) type IV, (5) multipass, (6) lipid-chain-anchored, (7) GPI-anchored, and (8) peripheral. It is important to decide which type it belongs to because it is closely relevant to its biological function and to its interaction process with other molecules in a biological system. In this study, on the basis of the concept of pseudo amino acid (PseAA) composition originally introduced by Chou, the value of approximate entropy (ApEn) of the query membrane protein is used to integrate the complementary information. An ensemble classifier to fuse fifteen powerful individual FKNN (fuzzy K-nearest neighbor) classifiers is presented. Here each basic classifier is trained in PseAA composition of membrane protein sequences with different parameters. The success rates obtained by the ensemble classifier on both the jackknife dataset test and the independent dataset test are quite high, indicating that the ensemble classifier may become a very useful high throughput tool in identifying membrane proteins and their types.
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paper_title: Support vector machine-based method for subcellular localization of human proteins using amino acid compositions, their order, and similarity search

paper_content:

Here we report a systematic approach for predicting subcellular localization (cytoplasm, mitochondrial, nuclear, and plasma membrane) of human proteins. First, support vector machine (SVM)-based modules for predicting subcellular localization using traditional amino acid and dipeptide (i + 1) composition achieved overall accuracy of 76.6 and 77.8%, respectively. PSI-BLAST, when carried out using a similarity-based search against a nonredundant data base of experimentally annotated proteins, yielded 73.3% accuracy. To gain further insight, a hybrid module (hybrid1) was developed based on amino acid composition, dipeptide composition, and similarity information and attained better accuracy of 84.9%. In addition, SVM modules based on a different higher order dipeptide i.e. i + 2, i + 3, and i + 4 were also constructed for the prediction of subcellular localization of human proteins, and overall accuracy of 79.7, 77.5, and 77.1% was accomplished, respectively. Furthermore, another SVM module hybrid2 was developed using traditional dipeptide (i + 1) and higher order dipeptide (i + 2, i + 3, and i + 4) compositions, which gave an overall accuracy of 81.3%. We also developed SVM module hybrid3 based on amino acid composition, traditional and higher order dipeptide compositions, and PSI-BLAST output and achieved an overall accuracy of 84.4%. A Web server HSLPred (www.imtech.res.in/raghava/hslpred/ or bioinformatics.uams.edu/raghava/hslpred/) has been designed to predict subcellular localization of human proteins using the above approaches.
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paper_title: Using Functional Domain Composition and Support Vector Machines for Prediction of Protein Subcellular Location *

paper_content:

Proteins are generally classified into the following 12 subcellular locations: 1) chloroplast, 2) cytoplasm, 3) cytoskeleton, 4) endoplasmic reticulum, 5) extracellular, 6) Golgi apparatus, 7) lysosome, 8) mitochondria, 9) nucleus, 10) peroxisome, 11) plasma membrane, and 12) vacuole. Because the function of a protein is closely correlated with its subcellular location, with the rapid increase in new protein sequences entering into databanks, it is vitally important for both basic research and pharmaceutical industry to establish a high throughput tool for predicting protein subcellular location. In this paper, a new concept, the so-called "functional domain composition" is introduced. Based on the novel concept, the representation for a protein can be defined as a vector in a high-dimensional space, where each of the clustered functional domains derived from the protein universe serves as a vector base. With such a novel representation for a protein, the support vector machine (SVM) algorithm is introduced for predicting protein subcellular location. High success rates are obtained by the self-consistency test, jackknife test, and independent dataset test, respectively. The current approach not only can play an important complementary role to the powerful covariant discriminant algorithm based on the pseudo amino acid composition representation (Chou, K. C. (2001) Proteins Struct. Funct. Genet. 43, 246-255; Correction (2001) Proteins Struct. Funct. Genet. 44, 60), but also may greatly stimulate the development of this area.
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paper_title: MultiLoc : prediction of protein subcellular localization using N-terminal targeting sequences , sequence motifs , and amino acid composition

paper_content:

Motivation: Functional annotation of unknown proteins is a major goal in proteomics. A key annotation is the prediction of a protein's subcellular localization. Numerous prediction techniques have been developed, typically focusing on a single underlying biological aspect or predicting a subset of all possible localizations. An important step is taken towards emulating the protein sorting process by capturing and bringing together biologically relevant information, and addressing the clear need to improve prediction accuracy and localization coverage. ::: ::: Results: Here we present a novel SVM-based approach for predicting subcellular localization, which integrates N-terminal targeting sequences, amino acid composition and protein sequence motifs. We show how this approach improves the prediction based on N-terminal targeting sequences, by comparing our method TargetLoc against existing methods. Furthermore, MultiLoc performs considerably better than comparable methods predicting all major eukaryotic subcellular localizations, and shows better or comparable results to methods that are specialized on fewer localizations or for one organism. ::: ::: Availability: http://www-bs.informatik.uni-tuebingen.de/Services/MultiLoc/ ::: ::: Contact: hoeglund@informatik.uni-tuebingen.de
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paper_title: PSSM-based prediction of DNA binding sites in proteins

paper_content:

BackgroundDetection of DNA-binding sites in proteins is of enormous interest for technologies targeting gene regulation and manipulation. We have previously shown that a residue and its sequence neighbor information can be used to predict DNA-binding candidates in a protein sequence. This sequence-based prediction method is applicable even if no sequence homology with a previously known DNA-binding protein is observed. Here we implement a neural network based algorithm to utilize evolutionary information of amino acid sequences in terms of their position specific scoring matrices (PSSMs) for a better prediction of DNA-binding sites.ResultsAn average of sensitivity and specificity using PSSMs is up to 8.7% better than the prediction with sequence information only. Much smaller data sets could be used to generate PSSM with minimal loss of prediction accuracy.ConclusionOne problem in using PSSM-derived prediction is obtaining lengthy and time-consuming alignments against large sequence databases. In order to speed up the process of generating PSSMs, we tried to use different reference data sets (sequence space) against which a target protein is scanned for PSI-BLAST iterations. We find that a very small set of proteins can actually be used as such a reference data without losing much of the prediction value. This makes the process of generating PSSMs very rapid and even amenable to be used at a genome level. A web server has been developed to provide these predictions of DNA-binding sites for any new protein from its amino acid sequence.AvailabilityOnline predictions based on this method are available at http://www.netasa.org/dbs-pssm/
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paper_title: Fuzzy KNN for predicting membrane protein types from pseudo-amino acid composition

paper_content:

Abstract Cell membranes are vitally important to the life of a cell. Although the basic structure of biological membrane is provided by the lipid bilayer, membrane proteins perform most of the specific functions. Membrane proteins are putatively classified into five different types. Identification of their types is currently an important topic in bioinformatics and proteomics. In this paper, based on the concept of representing protein samples in terms of their pseudo-amino acid composition (Chou, K.C., 2001. Prediction of protein cellular attributes using pseudo amino acid composition. Proteins: Struct. Funct. Genet. 43, 246–255), the fuzzy K-nearest neighbors (KNN) algorithm has been introduced to predict membrane protein types, and high success rates were observed. It is anticipated that, the current approach, which is based on a branch of fuzzy mathematics and represents a new strategy, may play an important complementary role to the existing methods in this area. The novel approach may also have notable impact on prediction of the other attributes, such as protein structural class, protein subcellular localization, and enzyme family class, among many others.
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paper_title: Prediction of RNA binding sites in a protein using SVM and PSSM profile

paper_content:

RNA-binding proteins (RBPs) play key roles in post-transcriptional control of gene expression, which, along with transcriptional regulation, is a major way to regulate patterns of gene expression during development. Thus, the identification and prediction of RNA binding sites is an important step in comprehensive understanding of how RBPs control organism development. Combining evolutionary information and support vector machine (SVM), we have developed an improved method for predicting RNA binding sites or RNA interacting residues in a protein sequence. The prediction models developed in this study have been trained and tested on 86 RNA binding protein chains and evaluated using fivefold cross validation technique. First, a SVM model was developed that achieved a maximum Matthew's correlation coefficient (MCC) of 0.31. The performance of this SVM model further improved the MCC from 0.31 to 0.45, when multiple sequence alignment in the form of PSSM profiles was used as input to the SVM, which is far better than the maximum MCC achieved by previous methods (0.41) on the same dataset. In addition, SVM models were also developed on an alternative dataset that contained 107 RBP chains. Utilizing PSSM as input information to the SVM, the training/testing on this alternate dataset achieved a maximum MCC of 0.32. Conclusively, the prediction performance of SVM models developed in this study is better than the existing methods on the same datasets. A web server 'Pprint' was also developed for predicting RNA binding residues in a protein sequence which is freely available at http://www.imtech.res.in/raghava/pprint/.
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paper_title: De Novo protein subcellular localization prediction by N-to-1 neural networks

paper_content:

Knowledge of the subcellular location of a protein provides valuable information about its function and possible interaction with other proteins. In the post-genomic era, fast and accurate predictors of subcellular location are required if this abundance of sequence data is to be fully exploited. We have developed a subcellular localization predictor (SCL pred) which predicts the location of a protein into four classes for animals and fungi and five classes for plants (secretory pathway, cytoplasm, nucleus, mitochondrion and chloroplast) using high throughput machine learning techniques trained on large non-redundant sets of protein sequences. The algorithm powering SCL pred is a novel Neural Network (N-to-1 Neural Network, or N1-NN) which is capable of mapping whole sequences into single properties (a functional class, in this work) without resorting to predefined transformations, but rather by adaptively compressing the sequence into a hidden feature vector. We benchmark SCL pred against other publicly available predictors using two benchmarks including a new subset of Swiss-Prot release 57. We show that SCL pred compares favourably to the other state-of-the-art predictors. Moreover, the N1-NN algorithm is fully general and may be applied to a host of problems of similar shape, that is, in which a whole sequence needs to be mapped into a fixed-size array of properties, and the adaptive compression it operates may even shed light on the space of protein sequences. The predictive systems described in this paper are publicly available at http://distill.ucd.ie/distill/.
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paper_title: Prediction of catalytic residues using Support Vector Machine with selected protein sequence and structural properties

paper_content:

BACKGROUND ::: The number of protein sequences deriving from genome sequencing projects is outpacing our knowledge about the function of these proteins. With the gap between experimentally characterized and uncharacterized proteins continuing to widen, it is necessary to develop new computational methods and tools for functional prediction. Knowledge of catalytic sites provides a valuable insight into protein function. Although many computational methods have been developed to predict catalytic residues and active sites, their accuracy remains low, with a significant number of false positives. In this paper, we present a novel method for the prediction of catalytic sites, using a carefully selected, supervised machine learning algorithm coupled with an optimal discriminative set of protein sequence conservation and structural properties. ::: ::: ::: RESULTS ::: To determine the best machine learning algorithm, 26 classifiers in the WEKA software package were compared using a benchmarking dataset of 79 enzymes with 254 catalytic residues in a 10-fold cross-validation analysis. Each residue of the dataset was represented by a set of 24 residue properties previously shown to be of functional relevance, as well as a label {+1/-1} to indicate catalytic/non-catalytic residue. The best-performing algorithm was the Sequential Minimal Optimization (SMO) algorithm, which is a Support Vector Machine (SVM). The Wrapper Subset Selection algorithm further selected seven of the 24 attributes as an optimal subset of residue properties, with sequence conservation, catalytic propensities of amino acids, and relative position on protein surface being the most important features. ::: ::: ::: CONCLUSION ::: The SMO algorithm with 7 selected attributes correctly predicted 228 of the 254 catalytic residues, with an overall predictive accuracy of more than 86%. Missing only 10.2% of the catalytic residues, the method captures the fundamental features of catalytic residues and can be used as a "catalytic residue filter" to facilitate experimental identification of catalytic residues for proteins with known structure but unknown function.
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paper_title: Enhanced performance in prediction of protein active sites with THEMATICS and support vector machines.

paper_content:

Theoretical microscopic titration curves (THEMATICS) is a computational method for the identification of active sites in proteins through deviations in computed titration behavior of ionizable residues. While the sensitivity to catalytic sites is high, the previously reported sensitivity to catalytic residues was not as high, about 50%. Here THEMATICS is combined with support vector machines (SVM) to improve sensitivity for catalytic residue prediction from protein 3D structure alone. For a test set of 64 proteins taken from the Catalytic Site Atlas (CSA), the average recall rate for annotated catalytic residues is 61%; good precision is maintained selecting only 4% of all residues. The average false positive rate, using the CSA annotations is only 3.2%, far lower than other 3D-structure-based methods. THEMATICS–SVM returns higher precision, lower false positive rate, and better overall performance, compared with other 3D-structure-based methods. Comparison is also made with the latest machine learning methods that are based on both sequence alignments and 3D structures. For annotated sets of well-characterized enzymes, THEMATICS–SVM performance compares very favorably with methods that utilize sequence homology. However, since THEMATICS depends only on the 3D structure of the query protein, no decline in performance is expected when applied to novel folds, proteins with few sequence homologues, or even orphan sequences. An extension of the method to predict non-ionizable catalytic residues is also presented. THEMATICS–SVM predicts a local network of ionizable residues with strong interactions between protonation events; this appears to be a special feature of enzyme active sites.
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paper_title: MemType-2L: A Web server for predicting membrane proteins and their types by incorporating evolution information through Pse-PSSM

paper_content:

Abstract Given an uncharacterized protein sequence, how can we identify whether it is a membrane protein or not? If it is, which membrane protein type it belongs to? These questions are important because they are closely relevant to the biological function of the query protein and to its interaction process with other molecules in a biological system. Particularly, with the avalanche of protein sequences generated in the Post-Genomic Age and the relatively much slower progress in using biochemical experiments to determine their functions, it is highly desired to develop an automated method that can be used to help address these questions. In this study, a 2-layer predictor, called MemType-2L, has been developed: the 1st layer prediction engine is to identify a query protein as membrane or non-membrane; if it is a membrane protein, the process will be automatically continued with the 2nd-layer prediction engine to further identify its type among the following eight categories: (1) type I, (2) type II, (3) type III, (4) type IV, (5) multipass, (6) lipid-chain-anchored, (7) GPI-anchored, and (8) peripheral. MemType-2L is featured by incorporating the evolution information through representing the protein samples with the Pse-PSSM (Pseudo Position-Specific Score Matrix) vectors, and by containing an ensemble classifier formed by fusing many powerful individual OET-KNN (Optimized Evidence-Theoretic K-Nearest Neighbor) classifiers. The success rates obtained by MemType-2L on a new-constructed stringent dataset by both the jackknife test and the independent dataset test are quite high, indicating that MemType-2L may become a very useful high throughput tool. As a Web server, MemType-2L is freely accessible to the public at http://chou.med.harvard.edu/bioinf/MemType .
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paper_title: Predicting rRNA-, RNA-, and DNA-binding proteins from primary structure with support vector machines.

paper_content:

In the post-genome era, the prediction of protein function is one of the most demanding tasks in the study of bioinformatics. Machine learning methods, such as the support vector machines (SVMs), greatly help to improve the classification of protein function. In this work, we integrated SVMs, protein sequence amino acid composition, and associated physicochemical properties into the study of nucleic-acid-binding proteins prediction. We developed the binary classifications for rRNA-, RNA-, DNA-binding proteins that play an important role in the control of many cell processes. Each SVM predicts whether a protein belongs to rRNA-, RNA-, or DNA-binding protein class. Self-consistency and jackknife tests were performed on the protein data sets in which the sequences identity was < 25%. Test results show that the accuracies of rRNA-, RNA-, DNA-binding SVMs predictions are approximately 84%, approximately 78%, approximately 72%, respectively. The predictions were also performed on the ambiguous and negative data set. The results demonstrate that the predicted scores of proteins in the ambiguous data set by RNA- and DNA-binding SVM models were distributed around zero, while most proteins in the negative data set were predicted as negative scores by all three SVMs. The score distributions agree well with the prior knowledge of those proteins and show the effectiveness of sequence associated physicochemical properties in the protein function prediction. The software is available from the author upon request.
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paper_title: Prediction of protein subcellular localization by support vector machines using multi-scale energy and pseudo amino acid composition

paper_content:

As more and more genomes have been discovered in recent years, there is an urgent need to develop a reliable method to predict the subcellular localization for the explosion of newly found proteins. However, many well-known prediction methods based on amino acid composition have problems utilizing the sequence-order information. Here, based on the concept of Chou’s pseudo amino acid composition (PseAA), a new feature extraction method, the multi-scale energy (MSE) approach, is introduced to incorporate the sequence-order information. First, a protein sequence was mapped to a digital signal using the amino acid index. Then, by wavelet transform, the mapped signal was broken down into several scales in which the energy factors were calculated and further formed into an MSE feature vector. Following this, combining this MSE feature vector with amino acid composition (AA), we constructed a series of MSEPseAA feature vectors to represent the protein subcellular localization sequences. Finally, according to a new kind of normalization approach, the MSEPseAA feature vectors were normalized to form the improved MSEPseAA vectors, named as IEPseAA. Using the technique of IEPseAA, C-support vector machine (C-SVM) and three multi-class SVMs strategies, quite promising results were obtained, indicating that MSE is quite effective in reflecting the sequence-order effects and might become a useful tool for predicting the other attributes of proteins as well.
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paper_title: iDNA-Prot: Identification of DNA Binding Proteins Using Random Forest with Grey Model

paper_content:

DNA-binding proteins play crucial roles in various cellular processes. Developing high throughput tools for rapidly and effectively identifying DNA-binding proteins is one of the major challenges in the field of genome annotation. Although many efforts have been made in this regard, further effort is needed to enhance the prediction power. By incorporating the features into the general form of pseudo amino acid composition that were extracted from protein sequences via the "grey model" and by adopting the random forest operation engine, we proposed a new predictor, called iDNA-Prot, for identifying uncharacterized proteins as DNA-binding proteins or non-DNA binding proteins based on their amino acid sequences information alone. The overall success rate by iDNA-Prot was 83.96% that was obtained via jackknife tests on a newly constructed stringent benchmark dataset in which none of the proteins included has ≥25% pairwise sequence identity to any other in a same subset. In addition to achieving high success rate, the computational time for iDNA-Prot is remarkably shorter in comparison with the relevant existing predictors. Hence it is anticipated that iDNA-Prot may become a useful high throughput tool for large-scale analysis of DNA-binding proteins. As a user-friendly web-server, iDNA-Prot is freely accessible to the public at the web-site on http://icpr.jci.edu.cn/bioinfo/iDNA-Prot or http://www.jci-bioinfo.cn/iDNA-Prot. Moreover, for the convenience of the vast majority of experimental scientists, a step-by-step guide is provided on how to use the web-server to get the desired results.
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paper_title: Prediction of RNA-Binding Proteins by Voting Systems

paper_content:

It is important to identify which proteins can interact with RNA for the purpose of protein annotation, since interactions between RNA and proteins influence the structure of the ribosome and play important roles in gene expression. This paper tries to identify proteins that can interact with RNA using voting systems. Firstly through Weka, 34 learning algorithms are chosen for investigation. Then simple majority voting system (SMVS) is used for the prediction of RNA-binding proteins, achieving average ACC (overall prediction accuracy) value of 79.72% and MCC (Matthew's correlation coefficient) value of 59.77% for the independent testing dataset. Then mRMR (minimum redundancy maximum relevance) strategy is used, which is transferred into algorithm selection. In addition, the MCC value of each classifier is assigned to be the weight of the classifier's vote. As a result, best average MCC values are attained when 22 algorithms are selected and integrated through weighted votes, which are 64.70% for the independent testing dataset, and ACC value is 82.04% at this moment.
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paper_title: Kernel-based machine learning protocol for predicting DNA-binding proteins

paper_content:

DNA-binding proteins (DNA-BPs) play a pivotal role in various intra- and extra-cellular activities ranging from DNA replication to gene expression control. Attempts have been made to identify DNA-BPs based on their sequence and structural information with moderate accuracy. Here we develop a machine learning protocol for the prediction of DNA-BPs where the classifier is Support Vector Machines (SVMs). Information used for classification is derived from characteristics that include surface and overall composition, overall charge and positive potential patches on the protein surface. In total 121 DNA-BPs and 238 non-binding proteins are used to build and evaluate the protocol. In self-consistency, accuracy value of 100% has been achieved. For cross-validation (CV) optimization over entire dataset, we report an accuracy of 90%. Using leave 1-pair holdout evaluation, the accuracy of 86.3% has been achieved. When we restrict the dataset to less than 20% sequence identity amongst the proteins, the holdout accuracy is achieved at 85.8%. Furthermore, seven DNA-BPs with unbounded structures are all correctly predicted. The current performances are better than results published previously. The higher accuracy value achieved here originates from two factors: the ability of the SVM to handle features that demonstrate a wide range of discriminatory power and, a different definition of the positive patch. Since our protocol does not lean on sequence or structural homology, it can be used to identify or predict proteins with DNA-binding function(s) regardless of their homology to the known ones.
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paper_title: Signal-BNF: A Bayesian Network Fusing Approach to Predict Signal Peptides

paper_content:

A signal peptide is a short peptide chain that directs the transport of a protein and has become the crucial vehicle in finding new drugs or reprogramming cells for gene therapy. As the avalanche of new protein sequences generated in the postgenomic era, the challenge of identifying new signal sequences has become even more urgent and critical in biomedical engineering. In this paper, we propose a novel predictor called Signal-BNF to predict the N-terminal signal peptide as well as its cleavage site based on Bayesian reasoning network. Signal-BNF is formed by fusing the results of different Bayesian classifiers which used different feature datasets as its input through weighted voting system. Experiment results show that Signal-BNF is superior to the popular online predictors such as Signal-3L and PrediSi. Signal-BNF is featured by high prediction accuracy that may serve as a useful tool for further investigating many unclear details regarding the molecular mechanism of the zip code protein-sorting system in cells.
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paper_title: A machine learning based method for the prediction of secretory proteins using amino acid composition, their order and similarity-search.

paper_content:

Most of the prediction methods for secretory proteins require the presence of a correct N-terminal end of the preprotein for correct classification. As large scale genome sequencing projects sometimes assign the 5'-end of genes incorrectly, many proteins are encoded without the correct N-terminus leading to incorrect prediction. In this study, a systematic attempt has been made to predict secretory proteins irrespective of presence or absence of N-terminal signal peptides (also known as classical and non-classical secreted proteins respectively), using machine-learning techniques; artificial neural network (ANN) and support vector machine (SVM). We trained and tested our methods on a dataset of 3321 secretory and 3654 non-secretory mammalian proteins using five-fold cross-validation technique. First, ANN-based modules have been developed for predicting secretory proteins using 33 physico-chemical properties, amino acid composition and dipeptide composition and achieved accuracies of 73.1%, 76.1% and 77.1%, respectively. Similarly, SVM-based modules using 33 physico-chemical properties, amino acid, and dipeptide composition have been able to achieve accuracies of 77.4%, 79.4% and 79.9%, respectively. In addition, BLAST and PSI-BLAST modules designed for predicting secretory proteins based on similarity search achieved 23.4% and 26.9% accuracy, respectively. Finally, we developed a hybrid-approach by integrating amino acid and dipeptide composition based SVM modules and PSI-BLAST module that increased the accuracy to 83.2%, which is significantly better than individual modules. We also achieved high sensitivity of 60.4% with low value of 5% false positive predictions using hybrid module. A web server SRTpred has been developed based on above study for predicting classical and non-classical secreted proteins from whole sequence of mammalian proteins, which is available from http://www.imtech.res.in/raghava/srtpred/.
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paper_title: EzyPred: a top-down approach for predicting enzyme functional classes and subclasses.

paper_content:

Abstract Given a protein sequence, how can we identify whether it is an enzyme or non-enzyme? If it is, which main functional class it belongs to? What about its sub-functional class? It is important to address these problems because they are closely correlated with the biological function of an uncharacterized protein and its acting object and process. Particularly, with the avalanche of protein sequences generated in the Post Genomic Age and relatively much slower progress in determining their functions by experiments, it is highly desired to develop an automated method by which one can get a fast and accurate answer to these questions. Here, a top–down predictor, called EzyPred , is developed by fusing the results derived from the functional domain and evolution information. EzyPred is a 3-layer predictor: the 1st layer prediction engine is for identifying a query protein as enzyme or non-enzyme; the 2nd layer for the main functional class; and the 3rd layer for the sub-functional class. The overall success rates for all the three layers are higher than 90% that were obtained through rigorous cross-validation tests on the very stringent benchmark datasets in which none of the proteins has ⩾40% sequence identity to any other in a same class or subclass. EzyPred is freely accessible at http://chou.med.harvard.edu/bioinf/EzyPred/ , by which one can get the desired 3-level results for a query protein sequence within less than 90 s.
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paper_title: Identification of protein functions using a machine-learning approach based on sequence-derived properties

paper_content:

BackgroundPredicting the function of an unknown protein is an essential goal in bioinformatics. Sequence similarity-based approaches are widely used for function prediction; however, they are often inadequate in the absence of similar sequences or when the sequence similarity among known protein sequences is statistically weak. This study aimed to develop an accurate prediction method for identifying protein function, irrespective of sequence and structural similarities.ResultsA highly accurate prediction method capable of identifying protein function, based solely on protein sequence properties, is described. This method analyses and identifies specific features of the protein sequence that are highly correlated with certain protein functions and determines the combination of protein sequence features that best characterises protein function. Thirty-three features that represent subtle differences in local regions and full regions of the protein sequences were introduced. On the basis of 484 features extracted solely from the protein sequence, models were built to predict the functions of 11 different proteins from a broad range of cellular components, molecular functions, and biological processes. The accuracy of protein function prediction using random forests with feature selection ranged from 94.23% to 100%. The local sequence information was found to have a broad range of applicability in predicting protein function.ConclusionWe present an accurate prediction method using a machine-learning approach based solely on protein sequence properties. The primary contribution of this paper is to propose new PNPRD features representing global and/or local differences in sequences, based on positively and/or negatively charged residues, to assist in predicting protein function. In addition, we identified a compact and useful feature subset for predicting the function of various proteins. Our results indicate that sequence-based classifiers can provide good results among a broad range of proteins, that the proposed features are useful in predicting several functions, and that the combination of our and traditional features may support the creation of a discriminative feature set for specific protein functions.
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paper_title: Prediction of Enzyme Subfamily Class via Pseudo Amino Acid Composition by Incorporating the Conjoint Triad Feature

paper_content:

Predicting enzyme subfamily class is an imbalance multi-class classification problem due to the fact that the number of proteins in each subfamily makes a great difference. In this paper, we focus on developing the computational methods specially designed for the imbalance multi-class classification problem to predict enzyme subfamily class. We compare two support vector machine (SVM)-based methods for the imbalance problem, AdaBoost algorithm with RBFSVM (SVM with RBF kernel) and SVM with arithmetic mean (AM) offset (AM-SVM) in enzyme subfamily classification. As input features for our predictive model, we use the conjoint triad feature (CTF). We validate two methods on an enzyme benchmark dataset, which contains six enzyme main families with a total of thirty-four subfamily classes, and those proteins have less than 40% sequence identity to any other in a same functional class. In predicting oxidoreductases subfamilies, AM-SVM obtains the over 0.92 Matthew's correlation coefficient (MCC) and over 93% accuracy, and in predicting lyases, isomerases and ligases subfamilies, it obtains over 0.73 MCC and over 82% accuracy. The improvement in the predictive performance suggests the AM-SVM might play a complementary role to the existing function annotation methods.
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paper_title: A top-down approach to classify enzyme functional classes and sub-classes using random forest

paper_content:

Advancements in sequencing technologies have witnessed an exponential rise in the number of newly found enzymes. Enzymes are proteins that catalyze bio-chemical reactions and play an important role in metabolic pathways. Commonly, function of such enzymes is determined by experiments that can be time consuming and costly. Hence, a need for a computing method is felt that can distinguish protein enzyme sequences from those of non-enzymes and reliably predict the function of the former. To address this problem, approaches that cluster enzymes based on their sequence and structural similarity have been presented. But, these approaches are known to fail for proteins that perform the same function and are dissimilar in their sequence and structure. In this article, we present a supervised machine learning model to predict the function class and sub-class of enzymes based on a set of 73 sequence-derived features. The functional classes are as defined by International Union of Biochemistry and Molecular Biology. Using an efficient data mining algorithm called random forest, we construct a top-down three layer model where the top layer classifies a query protein sequence as an enzyme or non-enzyme, the second layer predicts the main function class and bottom layer further predicts the sub-function class. The model reported overall classification accuracy of 94.87% for the first level, 87.7% for the second, and 84.25% for the bottom level. Our results compare very well with existing methods, and in many cases report better performance. Using feature selection methods, we have shown the biological relevance of a few of the top rank attributes.
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paper_title: Accurate prediction of protein enzymatic class by N-to-1 Neural Networks

paper_content:

We present a novel ab initio predictor of protein enzymatic class. The predictor can classify proteins, solely based on their sequences, into one of six classes extracted from the enzyme commission (EC) classification scheme and is trained on a large, curated database of over 6,000 non-redundant proteins which we have assembled in this work. The predictor is powered by an ensemble of N-to-1 Neural Network, a novel architecture which we have recently developed. N-to-1 Neural Networks operate on the full sequence and not on predefined features. All motifs of a predefined length (31 residues in this work) are considered and are compressed by an N-to-1 Neural Network into a feature vector which is automatically determined during training. We test our predictor in 10-fold cross-validation and obtain state of the art results, with a 96% correct classification and 86% generalized correlation. All six classes are predicted with a specificity of at least 80% and false positive rates never exceeding 7%. We are currently investigating enhanced input encoding schemes which include structural information, and are analyzing trained networks to mine motifs that are most informative for the prediction, hence, likely, functionally relevant.
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paper_title: A novel approach for protein subcellular location prediction using amino acid exposure

paper_content:

BACKGROUND ::: Proteins perform their functions in associated cellular locations. Therefore, the study of protein function can be facilitated by predictions of protein location. Protein location can be predicted either from the sequence of a protein alone by identification of targeting peptide sequences and motifs, or by homology to proteins of known location. A third approach, which is complementary, exploits the differences in amino acid composition of proteins associated to different cellular locations, and can be useful if motif and homology information are missing. Here we expand this approach taking into account amino acid composition at different levels of amino acid exposure. ::: ::: ::: RESULTS ::: Our method has two stages. For stage one, we trained multiple Support Vector Machines (SVMs) to score eukaryotic protein sequences for membership to each of three categories: nuclear, cytoplasmic and extracellular, plus extra category nucleocytoplasmic, accounting for the fact that a large number of proteins shuttles between those two locations. In stage two we use an artificial neural network (ANN) to propose a category from the scores given to the four locations in stage one. The method reaches an accuracy of 68% when using as input 3D-derived values of amino acid exposure. Calibration of the method using predicted values of amino acid exposure allows classifying proteins without 3D-information with an accuracy of 62% and discerning proteins in different locations even if they shared high levels of identity. ::: ::: ::: CONCLUSIONS ::: In this study we explored the relationship between residue exposure and protein subcellular location. We developed a new algorithm for subcellular location prediction that uses residue exposure signatures. Our algorithm uses a novel approach to address the multiclass classification problem. The algorithm is implemented as web server 'NYCE' and can be accessed at http://cbdm.mdc-berlin.de/~amer/nyce.
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paper_title: Structure based function prediction of proteins using fragment library frequency vectors

paper_content:

The function of the protein is primarily dictated by its structure. Therefore it is far more logical to find the functional clues of the protein in its overall 3-dimensional fold or its global structure. In this paper, we have developed a novel Support Vector Machines (SVM) based prediction model for functional classification and prediction of proteins using features extracted from its global structure based on fragment libraries. Fragment libraries have been previously used for abintio modelling of proteins and protein structure comparisons. The query protein structure is broken down into a collection of short contiguous backbone fragments and this collection is discretized using a library of fragments. The input feature vector is frequency vector that counts the number of each library fragment in the collection of fragments by all-to-all fragment comparisons. SVM models were trained and optimised for obtaining the best 10-fold Cross validation accuracy for classification. As an example, this method was applied for prediction and classification of Cell Adhesion molecules (CAMs). Thirty-four different fragment libraries with sizes ranging from 4 to 400 and fragment lengths ranging from 4 to 12 were used for obtaining the best prediction model. The best 10-fold CV accuracy of 95.25% was obtained for library of 400 fragments of length 10. An accuracy of 87.5% was obtained on an unseen test dataset consisting of 20 CAMs and 20 NonCAMs. This shows that protein structure can be accurately and uniquely described using 400 representative fragments of length 10.
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paper_title: Using functional domain composition to predict enzyme family classes.

paper_content:

According to their main EC (Enzyme Commission) numbers, enzymes are classified into the following 6 main classes: oxidoreductases, transferases, hydrolases, lyases, isomerases, and ligases. A new method has been developed to predict the enzymatic attribute of proteins by introducing the functional domain composition to formulate a given protein sequence. The advantage by doing so is that both the sequence-order-related features and the function-related features are naturally incorporated in the predictor. As a demonstration, the jackknife cross-validation test was performed on a dataset that consists of proteins with only less than 20% sequence identity to each other in order to get rid of any homologous bias. The overall success rate thus obtained was 85% in identifying the enzyme family classes (including the identification of nonenzyme protein sequences as well). The success rate is significantly higher than those obtained by the other methods on such a stringent dataset. This indicates that using the functional domain composition to represent protein samples for statistical prediction is indeed very promising, and will become a powerful tool in bioinformatics and proteomics.
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paper_title: Using pseudo-amino acid composition and support vector machine to predict protein structural class.

paper_content:

As a result of genome and other sequencing projects, the gap between the number of known protein sequences and the number of known protein structural classes is widening rapidly. In order to narrow this gap, it is vitally important to develop a computational prediction method for fast and accurately determining the protein structural class. In this paper, a novel predictor is developed for predicting protein structural class. It is featured by employing a support vector machine learning system and using a different pseudo-amino acid composition (PseAA), which was introduced to, to some extent, take into account the sequence-order effects to represent protein samples. As a demonstration, the jackknife cross-validation test was performed on a working dataset that contains 204 non-homologous proteins. The predicted results are very encouraging, indicating that the current predictor featured with the PseAA may play an important complementary role to the elegant covariant discriminant predictor and other existing algorithms.
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paper_title: Efficiency analysis of KNN and minimum distance-based classifiers in enzyme family prediction

paper_content:

Nearly all enzymes are proteins. They are the biological catalysts that accelerate the function of cellular reactions. Because of different characteristics of reaction tasks, they split into six classes: oxidoreductases (EC-1), transferases (EC-2), hydrolases (EC-3), lyases (EC-4), isomerases (EC-5), ligases (EC-6). Prediction of enzyme classes is of great importance in identifying which enzyme class is a member of a protein. Since the enzyme sequences increase day by day, contrary to experimental analysis in prediction of enzyme classes for a newly found enzyme sequence, providing from data mining techniques becomes very useful and time-saving. In this paper, two kinds of simple minimum distance-based classifier methods have been proposed. These methods and known K-nearest neighbor (KNN) classification algorithm have been performed in order to classify enzymes according to their amino acid composition. Performance measurements and elapsed time to execute algorithms have been compared. In addition, equality of two proposed approaches under special condition has been proved in order to be a guide for researchers.
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paper_title: A novel method for predicting protein subcellular localization based on pseudo amino acid composition

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In this paper, a novel approach, ELM-PCA, is introduced for the first time to predict protein subcellular localization. Firstly, Protein Samples are represented by the pseudo amino acid composition (PseAAC). Secondly, the principal component analysis (PCA) is employed to extract essential features. Finally, the Elman Recurrent Neural Network (RNN) is used as a classifier to identify the protein sequences. The results demonstrate that the proposed approach is effective and practical.
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paper_title: Accurate prediction of enzyme subfamily class using an adaptive fuzzy k-nearest neighbor method

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Amphiphilic pseudo-amino acid composition (Am-Pse-AAC) with extra sequence-order information is a useful feature for representing enzymes. This study first utilizes the k-nearest neighbor (k-NN) rule to analyze the distribution of enzymes in the Am-Pse-AAC feature space. This analysis indicates the distributions of multiple classes of enzymes are highly overlapped. To cope with the overlap problem, this study proposes an efficient non-parametric classifier for predicting enzyme subfamily class using an adaptive fuzzy r-nearest neighbor (AFK-NN) method, where k and a fuzzy strength parameter m are adaptively specified. The fuzzy membership values of a query sample Q are dynamically determined according to the position of Q and its weighted distances to the k nearest neighbors. Using the same enzymes of the oxidoreductases family for comparisons, the prediction accuracy of AFK-NN is 76.6%, which is better than those of Support Vector Machine (73.6%), the decision tree method C5.0 (75.4%) and the existing covariant-discriminate algorithm (70.6%) using a jackknife test. To evaluate the generalization ability of AFK-NN, the datasets for all six families of entirely sequenced enzymes are established from the newly updated SWISS-PROT and ENZYME database. The accuracy of AFK-NN on the new large-scale dataset of oxidoreductases family is 83.3%, and the mean accuracy of the six families is 92.1%.
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paper_title: Prediction protein structural classes with pseudo-amino acid composition: approximate entropy and hydrophobicity pattern.

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Compared with the conventional amino acid (AA) composition, the pseudo-amino acid (PseAA) composition as originally introduced for protein subcellular location prediction can incorporate much more information of a protein sequence, so as to remarkably enhance the power of using a discrete model to predict various attributes of a protein. In this study, based on the concept of PseAA composition, the approximate entropy and hydrophobicity pattern of a protein sequence are used to characterize the PseAA components. Also, the immune genetic algorithm (IGA) is applied to search the optimal weight factors in generating the PseAA composition. Thus, for a given protein sequence sample, a 27-D (dimensional) PseAA composition is generated as its descriptor. The fuzzy K nearest neighbors (FKNN) classifier is adopted as the prediction engine. The results thus obtained in predicting protein structural classification are quite encouraging, indicating that the current approach may also be used to improve the prediction quality of other protein attributes, or at least can play a complimentary role to the existing methods in the relevant areas. Our algorithm is written in Matlab that is available by contacting the corresponding author.
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paper_title: Predicting protein submitochondria locations by combining different descriptors into the general form of Chou’s pseudo amino acid composition

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Knowledge of the submitochondria location of protein is integral to understanding its function and a necessity in the proteomics era. In this work, a new submitochondria data set is constructed, and an approach for predicting protein submitochondria locations is proposed by combining the amino acid composition, dipeptide composition, reduced physicochemical properties, gene ontology, evolutionary information, and pseudo-average chemical shift. The overall prediction accuracy is 93.57% for the submitochondria location and 97.79% for the three membrane protein types in the mitochondria inner membrane using the algorithm of the increment of diversity combined with the support vector machine. The performance of the pseudo-average chemical shift is excellent. For contrast, the method is also used to predict submitochondria locations in the data set constructed by Du and Li; an accuracy of 94.95% is obtained by our method, which is better than that of other existing methods.
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paper_title: Using the concept of Chou's pseudo amino acid composition to predict enzyme family classes: an approach with support vector machine based on discrete wavelet transform.

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The early determination of family for a newly found enzyme molecule becomes important because it is directly related to the detail information about which specific target it acts on, as well as to its catalytic process and biological function. Unfortunately, it is still a hard work to distinguish enzyme classes by experiments. With an enormous amount of protein sequences uncovered in the genome research, it is both challenging and indispensable to develop an automatic method for fast and reliably classifying the enzyme family. Using the concept of Chou's pseudo amino acid composition, we developed a new method that coupled discrete wavelet transform with support vector machine based on the amino acid hydrophobicity to predict enzyme family. The overall success rate obtained by the 10-cross-validation for the identification of the six enzyme families was 91.9%, indicating the current method could be an effective and promising high-throughput method in the enzyme research.
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paper_title: Using Chou's amphiphilic pseudo-amino acid composition and support vector machine for prediction of enzyme subfamily classes

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Abstract With the rapid increment of protein sequence data, it is indispensable to develop automated and reliable predictive methods for protein function annotation. One approach for facilitating protein function prediction is to classify proteins into functional families from primary sequence. Being the most important group of all proteins, the accurate prediction for enzyme family classes and subfamily classes is closely related to their biological functions. In this paper, for the prediction of enzyme subfamily classes, the Chou's amphiphilic pseudo-amino acid composition [Chou, K.C., 2005. Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes. Bioinformatics 21, 10–19] has been adopted to represent the protein samples for training the ‘ one-versus-rest’ support vector machine. As a demonstration, the jackknife test was performed on the dataset that contains 2640 oxidoreductase sequences classified into 16 subfamily classes [Chou, K.C., Elrod, D.W., 2003. Prediction of enzyme family classes. J. Proteome Res. 2, 183–190]. The overall accuracy thus obtained was 80.87%. The significant enhancement in the accuracy indicates that the current method might play a complementary role to the exiting methods.
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paper_title: Support vector machine prediction of enzyme function with conjoint triad feature and hierarchical context

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BackgroundEnzymes are known as the largest class of proteins and their functions are usually annotated by the Enzyme Commission (EC), which uses a hierarchy structure, i.e., four numbers separated by periods, to classify the function of enzymes. Automatically categorizing enzyme into the EC hierarchy is crucial to understand its specific molecular mechanism.ResultsIn this paper, we introduce two key improvements in predicting enzyme function within the machine learning framework. One is to introduce the efficient sequence encoding methods for representing given proteins. The second one is to develop a structure-based prediction method with low computational complexity. In particular, we propose to use the conjoint triad feature (CTF) to represent the given protein sequences by considering not only the composition of amino acids but also the neighbor relationships in the sequence. Then we develop a support vector machine (SVM)-based method, named as SVMHL (SVM for hierarchy labels), to output enzyme function by fully considering the hierarchical structure of EC. The experimental results show that our SVMHL with the CTF outperforms SVMHL with the amino acid composition (AAC) feature both in predictive accuracy and Matthew’s correlation coefficient (MCC). In addition, SVMHL with the CTF obtains the accuracy and MCC ranging from 81% to 98% and 0. 82 to 0. 98 when predicting the first three EC digits on a low-homologous enzyme dataset. We further demonstrate that our method outperforms the methods which do not take account of hierarchical relationship among enzyme categories and alternative methods which incorporate prior knowledge about inter-class relationships.ConclusionsOur structure-based prediction model, SVMHL with the CTF, reduces the computational complexity and outperforms the alternative approaches in enzyme function prediction. Therefore our new method will be a useful tool for enzyme function prediction community.
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paper_title: Predicting enzyme class from protein structure using Bayesian classification

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Predicting enzyme class from protein structure param- eters is a challenging problem in protein analysis. We developed a method to predict enzyme class that combines the strengths of statistical and data-mining methods. This method has a strong mathematical founda- tion and is simple to implement, achieving an accuracy of 45%. A com- parison with the methods found in the literature designed to predict en- zyme class showed that our method outperforms the existing methods.
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paper_title: De Novo protein subcellular localization prediction by N-to-1 neural networks

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Knowledge of the subcellular location of a protein provides valuable information about its function and possible interaction with other proteins. In the post-genomic era, fast and accurate predictors of subcellular location are required if this abundance of sequence data is to be fully exploited. We have developed a subcellular localization predictor (SCL pred) which predicts the location of a protein into four classes for animals and fungi and five classes for plants (secretory pathway, cytoplasm, nucleus, mitochondrion and chloroplast) using high throughput machine learning techniques trained on large non-redundant sets of protein sequences. The algorithm powering SCL pred is a novel Neural Network (N-to-1 Neural Network, or N1-NN) which is capable of mapping whole sequences into single properties (a functional class, in this work) without resorting to predefined transformations, but rather by adaptively compressing the sequence into a hidden feature vector. We benchmark SCL pred against other publicly available predictors using two benchmarks including a new subset of Swiss-Prot release 57. We show that SCL pred compares favourably to the other state-of-the-art predictors. Moreover, the N1-NN algorithm is fully general and may be applied to a host of problems of similar shape, that is, in which a whole sequence needs to be mapped into a fixed-size array of properties, and the adaptive compression it operates may even shed light on the space of protein sequences. The predictive systems described in this paper are publicly available at http://distill.ucd.ie/distill/.
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paper_title: Prediction of Detailed Enzyme Functions and Identification of Specificity Determining Residues by Random Forests

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Determining enzyme functions is essential for a thorough understanding of cellular processes. Although many prediction methods have been developed, it remains a significant challenge to predict enzyme functions at the fourth-digit level of the Enzyme Commission numbers. Functional specificity of enzymes often changes drastically by mutations of a small number of residues and therefore, information about these critical residues can potentially help discriminate detailed functions. However, because these residues must be identified by mutagenesis experiments, the available information is limited, and the lack of experimentally verified specificity determining residues (SDRs) has hindered the development of detailed function prediction methods and computational identification of SDRs. Here we present a novel method for predicting enzyme functions by random forests, EFPrf, along with a set of putative SDRs, the random forests derived SDRs (rf-SDRs). EFPrf consists of a set of binary predictors for enzymes in each CATH superfamily and the rf-SDRs are the residue positions corresponding to the most highly contributing attributes obtained from each predictor. EFPrf showed a precision of 0.98 and a recall of 0.89 in a cross-validated benchmark assessment. The rf-SDRs included many residues, whose importance for specificity had been validated experimentally. The analysis of the rf-SDRs revealed both a general tendency that functionally diverged superfamilies tend to include more active site residues in their rf-SDRs than in less diverged superfamilies, and superfamily-specific conservation patterns of each functional residue. EFPrf and the rf-SDRs will be an effective tool for annotating enzyme functions and for understanding how enzyme functions have diverged within each superfamily.
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paper_title: Whole-proteome prediction of protein function via graph-theoretic analysis of interaction maps

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Motivation: Determining protein function is one of the most important problems in the post-genomic era. For the typical proteome, there are no functional annotations for one-third or more of its proteins. Recent high-throughput experiments have determined proteome-scale protein physical interaction maps for several organisms. These physical interactions are complemented by an abundance of data about other types of functional relationships between proteins, including genetic interactions, knowledge about co-expression and shared evolutionary history. Taken together, these pairwise linkages can be used to build whole-proteome protein interaction maps. ::: ::: Results: We develop a network-flow based algorithm, FunctionalFlow, that exploits the underlying structure of protein interaction maps in order to predict protein function. In cross-validation testing on the yeast proteome, we show that FunctionalFlow has improved performance over previous methods in predicting the function of proteins with few (or no) annotated protein neighbors. By comparing several methods that use protein interaction maps to predict protein function, we demonstrate that FunctionalFlow performs well because it takes advantage of both network topology and some measure of locality. Finally, we show that performance can be improved substantially as we consider multiple data sources and use them to create weighted interaction networks. ::: ::: Availability: http://compbio.cs.princeton.edu/function ::: ::: Contact: msingh@princeton.edu
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paper_title: Function-Function correlated multi-label protein function prediction over interaction networks

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Many previous computational methods for protein function prediction make prediction one function at a time, fundamentally, which is equivalent to assume the functional categories of proteins to be isolated. However, biological processes are highly correlated and usually intertwined together to happen at the same time, therefore it would be beneficial to consider protein function prediction as one indivisible task and treat all the functional categories as an integral and correlated prediction target. By leveraging the function-function correlations, it is expected to achieve improved overall predictive accuracy. To this end, we develop a novel network based protein function prediction approach, under the framework of multi-label classification in machine learning, to utilize the function-function correlations. Besides formulating the function-function correlations in the optimization objective explicitly, we also exploit them as part of the pairwise protein-protein similarities implicitly. The algorithm is built upon the Green's function over a graph, which not only employs the global topology of a network but also captures its local structural information. We evaluate the proposed approach on Saccharomyces cerevisiae species. The encouraging experimental results demonstrate the effectiveness of the proposed method.
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paper_title: A support vector machine approach for detecting gene-gene interaction

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Although genetic factors play an important role in most human diseases, multiple genes or genes and environmental factors may influence individual risk. In order to understand the underlying biological mechanisms of complex diseases, it is important to understand the complex relationships that control the process. In this paper, we consider different perspectives, from each optimization, complexity analysis, and algorithmic design, which allows us to describe a reasonable and applicable computational framework for detecting gene-gene interactions. Accordingly, support vector machine and combinatorial optimization techniques (local search and genetic algorithm) were tailored to fit within this framework. Although the proposed approach is computationally expensive, our results indicate this is a promising tool for the identification and characterization of high order gene-gene and gene-environment interactions. We have demonstrated several advantages of this method, including the strong power for classification, less concern for overfitting, and the ability to handle unbalanced data and achieve more stable models. We would like to make the support vector machine and combinatorial optimization techniques more accessible to genetic epidemiologists, and to promote the use and extension of these powerful approaches. Genet. Epidemiol. 2008. © 2007 Wiley-Liss, Inc.
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paper_title: Prediction of Protein Functions from Protein Interaction Networks: A Naïve Bayes Approach

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Predicting protein functions is one of most challenging problems in bioinformatics. Among several approaches, such as analyzing phylogenetic profiles, homologous protein sequences or gene expression patterns, methods based on protein interaction data are very promising. We propose here a novel method using Naive Bayes which takes advantage of protein interaction network topology to improve low-recall predictions. Our method is tested on proteins from the Human Protein Reference Database (HPRD) and on the yeast proteins from the BioGRID and compared with other state-of-the-art approaches. Analyses of the results, using several methods that include ROC analyses, indicate that our method predicts protein functions with significantly higher recall without lowering precision.
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paper_title: Prediction of Protein Function Using Protein-Protein Interaction Data

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Assigning functions to novel proteins is one of the most important problems in the post-genomic era. Several approaches have been applied to this problem, including analyzing gene expression patterns, phylogenetic profiles, protein fusions and protein-protein interactions. We develop a novel approach that applies the theory of Markov random fields to infer a protein's functions using protein-protein interaction data and the functional annotations of its interaction protein partners. For each function of interest and a protein, we predict the probability that the protein has that function using Bayesian approaches. Unlike in other available approaches for protein annotation where a protein has or does not have a function of interest, we give a probability for having the function. This probability indicates how confident we are about the prediction. We apply our method to predict cellular functions (43 categories including a category "others") for yeast proteins defined in the Yeast Proteome Database (YPD), using the protein-protein interaction data from the Munich Information Center for Protein Sequences (MIPS, http://mips.gsf.de). We show that our approach outperforms other available methods for function prediction based on protein interaction data.
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paper_title: Going the Distance for Protein Function Prediction: A New Distance Metric for Protein Interaction Networks

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In protein-protein interaction (PPI) networks, functional similarity is often inferred based on the function of directly interacting proteins, or more generally, some notion of interaction network proximity among proteins in a local neighborhood. Prior methods typically measure proximity as the shortest-path distance in the network, but this has only a limited ability to capture fine-grained neighborhood distinctions, because most proteins are close to each other, and there are many ties in proximity. We introduce diffusion state distance (DSD), a new metric based on a graph diffusion property, designed to capture finer-grained distinctions in proximity for transfer of functional annotation in PPI networks. We present a tool that, when input a PPI network, will output the DSD distances between every pair of proteins. We show that replacing the shortest-path metric by DSD improves the performance of classical function prediction methods across the board.
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paper_title: Towards the identification of protein complexes and functional modules by integrating PPI network and gene expression data

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BackgroundIdentification of protein complexes and functional modules from protein-protein interaction (PPI) networks is crucial to understanding the principles of cellular organization and predicting protein functions. In the past few years, many computational methods have been proposed. However, most of them considered the PPI networks as static graphs and overlooked the dynamics inherent within these networks. Moreover, few of them can distinguish between protein complexes and functional modules.ResultsIn this paper, a new framework is proposed to distinguish between protein complexes and functional modules by integrating gene expression data into protein-protein interaction (PPI) data. A series of time-sequenced subnetworks (TSNs) is constructed according to the time that the interactions were activated. The algorithm TSN-PCD was then developed to identify protein complexes from these TSNs. As protein complexes are significantly related to functional modules, a new algorithm DFM-CIN is proposed to discover functional modules based on the identified complexes. The experimental results show that the combination of temporal gene expression data with PPI data contributes to identifying protein complexes more precisely. A quantitative comparison based on f-measure reveals that our algorithm TSN-PCD outperforms the other previous protein complex discovery algorithms. Furthermore, we evaluate the identified functional modules by using “Biological Process” annotated in GO (Gene Ontology). The validation shows that the identified functional modules are statistically significant in terms of “Biological Process”. More importantly, the relationship between protein complexes and functional modules are studied.ConclusionsThe proposed framework based on the integration of PPI data and gene expression data makes it possible to identify protein complexes and functional modules more effectively. Moveover, the proposed new framework and algorithms can distinguish between protein complexes and functional modules. Our findings suggest that functional modules are closely related to protein complexes and a functional module may consist of one or multiple protein complexes. The program is available at http://netlab.csu.edu.cn/bioinfomatics/limin/DFM-CIN/index.html.
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paper_title: Protein function prediction by collective classification with explicit and implicit edges in protein-protein interaction networks

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BACKGROUND ::: Protein function prediction is an important problem in the post-genomic era. Recent advances in experimental biology have enabled the production of vast amounts of protein-protein interaction (PPI) data. Thus, using PPI data to functionally annotate proteins has been extensively studied. However, most existing network-based approaches do not work well when annotation and interaction information is inadequate in the networks. ::: ::: ::: RESULTS ::: In this paper, we proposed a new method that combines PPI information and protein sequence information to boost the prediction performance based on collective classification. Our method divides function prediction into two phases: First, the original PPI network is enriched by adding a number of edges that are inferred from protein sequence information. We call the added edges implicit edges, and the existing ones explicit edges correspondingly. Second, a collective classification algorithm is employed on the new network to predict protein function. ::: ::: ::: CONCLUSIONS ::: We conducted extensive experiments on two real, publicly available PPI datasets. Compared to four existing protein function prediction approaches, our method performs better in many situations, which shows that adding implicit edges can indeed improve the prediction performance. Furthermore, the experimental results also indicate that our method is significantly better than the compared approaches in sparsely-labeled networks, and it is robust to the change of the proportion of annotated proteins.
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paper_title: An ensemble framework for clustering protein-protein interaction networks

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Motivation: Protein–Protein Interaction (PPI) networks are believed to be important sources of information related to biological processes and complex metabolic functions of the cell. The presence of biologically relevant functional modules in these networks has been theorized by many researchers. However, the application of traditional clustering algorithms for extracting these modules has not been successful, largely due to the presence of noisy false positive interactions as well as specific topological challenges in the network. ::: ::: Results: In this article, we propose an ensemble clustering framework to address this problem. For base clustering, we introduce two topology-based distance metrics to counteract the effects of noise. We develop a PCA-based consensus clustering technique, designed to reduce the dimensionality of the consensus problem and yield informative clusters. We also develop a soft consensus clustering variant to assign multifaceted proteins to multiple functional groups. We conduct an empirical evaluation of different consensus techniques using topology-based, information theoretic and domain-specific validation metrics and show that our approaches can provide significant benefits over other state-of-the-art approaches. Our analysis of the consensus clusters obtained demonstrates that ensemble clustering can (a) produce improved biologically significant functional groupings; and (b) facilitate soft clustering by discovering multiple functional associations for proteins. ::: ::: Contact: srini@cse.ohio-state.edu ::: ::: Supplementary information: Supplementary data are available at Bioinformatics online.
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paper_title: Association analysis-based transformations for protein interaction networks: a function prediction case study

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Protein interaction networks are one of the most promising types of biological data for the discovery of functional modules and the prediction of individual protein functions. However, it is known that these networks are both incomplete and inaccurate, i.e., they have spurious edges and lackbiologically valid edges. One way to handle this problem is by transforming the original interaction graph into new graphs that remove spurious edges, add biologically valid ones, and assign reliability scores to the edges constituting the final network. We investigate currently existing methods, as well as propose a robust association analysis-based method for this task. This method is based on the concept of h-confidence, which is a measure that can be used to extract groups of objects having high similarity with each other. Experimental evaluation on several protein interaction data sets show that hyperclique-based transformations enhance the performance of standard function prediction algorithms significantly, and thus have merit.
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paper_title: NETAL: a new graph-based method for global alignment of protein–protein interaction networks

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Motivation: The interactions among proteins and the resulting networks of such interactions have a central role in cell biology. Aligning these networks gives us important information, such as conserved complexes and evolutionary relationships. Although there have been several publications on the global alignment of protein networks; however, none of proposed methods are able to produce a highly conserved and meaningful alignment. Moreover, time complexity of current algorithms makes them impossible to use for multiple alignment of several large networks together. Results: We present a novel algorithm for the global alignment of protein–protein interaction networks. It uses a greedy method, based on the alignment scoring matrix, which is derived from both biological and topological information of input networks to find the best global network alignment. NETAL outperforms other global alignment methods in terms of several measurements, such as Edge Correctness, Largest Common Connected Subgraphs and the number of common Gene Ontology terms between aligned proteins. As the running time of NETAL is much less than other available methods, NETAL can be easily expanded to multiple alignment algorithm. Furthermore, NETAL overpowers all other existing algorithms in term of performance so that the short running time of NETAL allowed us to implement it as the first server for global alignment of protein–protein interaction networks. Availability: Binaries supported on linux are freely available for download at http://www.bioinf.cs.ipm.ir/software/netal. Contact: sh.arab@modares.ac.ir Supplementary information: Supplementary data are available at Bioinformatics online.
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paper_title: Exploiting indirect neighbours and topological weight to predict protein function from protein-protein interactions

paper_content:

Most approaches in predicting protein function from protein-protein interaction data utilize the observation that a protein often share functions with proteins that interacts with it (its level-1 neighbours). However, proteins that interact with the same proteins (i.e. level-2 neighbours) may also have a greater likelihood of sharing similar physical or biochemical characteristics. We speculate that two separate forms of functional association accounts for such a phenomenon, and a protein is likely to share functions with its level-1 and/or level-2 neighbours. We are interested to find out how significant is functional association between level-2 neighbours and how they can be exploited for protein function prediction. ::: ::: We made a statistical study on recent interaction data and observed that functional association between level-2 neighbours is clearly observable. A substantial number of proteins are observed to share functions with level-2 neighbours but not with level-1 neighbours. We develop an algorithm that predicts the functions of a protein in two steps: (1) assign a weight to each of its level-1 and level-2 neighbours by estimating its functional similarity with the protein using the local topology of the interaction network as well as the reliability of experimental sources; (2) scoring each function based on its weighted frequency in these neighbours. Using leave-one-out cross validation, we compare the performance of our method against that of several other existing approaches and show that our method performs well.
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paper_title: Using enhanced genetic programming techniques for evolving classifiers in the context of medical diagnosis

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There are several data based methods in the field of artificial intelligence which are nowadays frequently used for analyzing classification problems in the context of medical applications. As we show in this paper, the application of enhanced evolutionary computation techniques to classification problems has the potential to evolve classifiers of even higher quality than those trained by standard machine learning methods. On the basis of three medical benchmark classification problems, namely the Wisconsin and the Thyroid data sets taken from the UCI repository as well as the Melanoma data set prepared by members of the Department of Dermatology of the Medical University Vienna, we document that the enhanced genetic programming based approach presented here is able to produce better results than linear modeling methods, artificial neural networks, kNN classification and also standard genetic programming approaches.
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paper_title: Systematic gene function prediction from gene expression data by using a fuzzy nearest-cluster method

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BackgroundQuantitative simultaneous monitoring of the expression levels of thousands of genes under various experimental conditions is now possible using microarray experiments. However, there are still gaps toward whole-genome functional annotation of genes using the gene expression data.ResultsIn this paper, we propose a novel technique called Fuzzy Nearest Clusters for genome-wide functional annotation of unclassified genes. The technique consists of two steps: an initial hierarchical clustering step to detect homogeneous co-expressed gene subgroups or clusters in each possibly heterogeneous functional class; followed by a classification step to predict the functional roles of the unclassified genes based on their corresponding similarities to the detected functional clusters.ConclusionOur experimental results with yeast gene expression data showed that the proposed method can accurately predict the genes' functions, even those with multiple functional roles, and the prediction performance is most independent of the underlying heterogeneity of the complex functional classes, as compared to the other conventional gene function prediction approaches.
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paper_title: Analysis of gene expression data using self-organizing maps

paper_content:

DNA microarray technologies together with rapidly increasing genomic sequence information is leading to an explosion in available gene expression data. Currently there is a great need for efficient methods to analyze and visualize these massive data sets. A self-organizing map (SOM) is an unsupervised neural network learning algorithm which has been successfully used for the analysis and organization of large data files. We have here applied the SOM algorithm to analyze published data of yeast gene expression and show that SOM is an excellent tool for the analysis and visualization of gene expression profiles.
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paper_title: Classification of Gene Expression Data by Majority Voting Genetic Programming Classifier

paper_content:

Recently, genetic programming (GP) has been applied to the classification of gene expression data. In its typical implementation, using training data, a single rule or a single set of rules is evolved with GP, and then it is applied to test data to get generalized test accuracy. However, in most cases, the generalized test accuracy is not higher. In this paper, we propose a majority voting technique for prediction of the labels of test samples. Instead of a single rule or a single set of rules, we evolve multiple rules with GP and then apply those rules to test samples to determine their labels by using the majority voting technique. We demonstrate the effectiveness of our proposed method by performing different types of experiments on two microarray data sets.
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paper_title: Incorporating heterogeneous biological data sources in clustering gene expression data

paper_content:

In this paper, a similarity measure between genes with protein-protein interactions is pro-posed. The chip-chip data are converted into the same form of gene expression data with pear-son correlation as its similarity measure. On the basis of the similarity measures of protein- protein interaction data and chip-chip data, the combined dissimilarity measure is defined. The combined distance measure is introduced into K-means method, which can be considered as an improved K-means method. The improved K-means method and other three clustering methods are evaluated by a real dataset. Per-formance of these methods is assessed by a prediction accuracy analysis through known gene annotations. Our results show that the improved K-means method outperforms other clustering methods. The performance of the improved K-means method is also tested by varying the tuning coefficients of the combined dissimilarity measure. The results show that it is very helpful and meaningful to incorporate het-erogeneous data sources in clustering gene expression data, and those coefficients for the genome-wide or completed data sources should be given larger values when constructing the combined dissimilarity measure.
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paper_title: Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation.

paper_content:

Array technologies have made it straightforward to monitor simultaneously the expression pattern of thousands of genes. The challenge now is to interpret such massive data sets. The first step is to extract the fundamental patterns of gene expression inherent in the data. This paper describes the application of self-organizing maps, a type of mathematical cluster analysis that is particularly well suited for recognizing and classifying features in complex, multidimensional data. The method has been implemented in a publicly available computer package, GENECLUSTER, that performs the analytical calculations and provides easy data visualization. To illustrate the value of such analysis, the approach is applied to hematopoietic differentiation in four well studied models (HL-60, U937, Jurkat, and NB4 cells). Expression patterns of some 6,000 human genes were assayed, and an online database was created. GENECLUSTER was used to organize the genes into biologically relevant clusters that suggest novel hypotheses about hematopoietic differentiation-for example, highlighting certain genes and pathways involved in "differentiation therapy" used in the treatment of acute promyelocytic leukemia.
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paper_title: Discriminative local subspaces in gene expression data for effective gene function prediction

paper_content:

Motivation: Massive amounts of genome-wide gene expression data have become available, motivating the development of computational approaches that leverage this information to predict gene function. Among successful approaches, supervised machine learning methods, such as Support Vector Machines (SVMs), have shown superior prediction accuracy. However, these methods lack the simple biological intuition provided by co-expression networks (CNs), limiting their practical usefulness. ::: ::: Results: In this work, we present Discriminative Local Subspaces (DLS), a novel method that combines supervised machine learning and co-expression techniques with the goal of systematically predict genes involved in specific biological processes of interest. Unlike traditional CNs, DLS uses the knowledge available in Gene Ontology (GO) to generate informative training sets that guide the discovery of expression signatures: expression patterns that are discriminative for genes involved in the biological process of interest. By linking genes co-expressed with these signatures, DLS is able to construct a discriminative CN that links both, known and previously uncharacterized genes, for the selected biological process. This article focuses on the algorithm behind DLS and shows its predictive power using an Arabidopsis thaliana dataset and a representative set of 101 GO terms from the Biological Process Ontology. Our results show that DLS has a superior average accuracy than both SVMs and CNs. Thus, DLS is able to provide the prediction accuracy of supervised learning methods while maintaining the intuitive understanding of CNs. ::: ::: Availability: A MATLAB® implementation of DLS is available at http://virtualplant.bio.puc.cl/cgi-bin/Lab/tools.cgi ::: ::: Contact: [email protected] ::: ::: Supplementary Information: Supplementary data are available at http://bioinformatics.mpimp-golm.mpg.de/.
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paper_title: A genetic programming-based approach to the classification of multiclass microarray datasets

paper_content:

Motivation: Feature selection approaches have been widely applied to deal with the small sample size problem in the analysis of micro-array datasets. For the multiclass problem, the proposed methods are based on the idea of selecting a gene subset to distinguish all classes. However, it will be more effective to solve a multiclass problem by splitting it into a set of two-class problems and solving each problem with a respective classification system. ::: ::: Results: We propose a genetic programming (GP)-based approach to analyze multiclass microarray datasets. Unlike the traditional GP, the individual proposed in this article consists of a set of small-scale ensembles, named as sub-ensemble (denoted by SE). Each SE consists of a set of trees. In application, a multiclass problem is divided into a set of two-class problems, each of which is tackled by a SE first. The SEs tackling the respective two-class problems are combined to construct a GP individual, so each individual can deal with a multiclass problem directly. Effective methods are proposed to solve the problems arising in the fusion of SEs, and a greedy algorithm is designed to keep high diversity in SEs. This GP is tested in five datasets. The results show that the proposed method effectively implements the feature selection and classification tasks. ::: ::: Contact:lkhqz@163.com; khliu1977@gmail.com ::: ::: Supplementary information: Supplementary data are available at Bioinformatics online.
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paper_title: Ensemble Genetic Programming for Classifying Gene Expression Data

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Ensemble is a representative technique for improving classification performance by combining a set of classifiers. It is required to maintain the diversity among base classifiers for effective ensemble. Conventional ensemble approaches construct various classifiers by estimating the similarity on the output patterns of them, and combine them with several fusion methods. Since they measure the similarity indirectly, it is restricted to evaluate the precise diversity among base classifiers. In this paper, we propose an ensemble method that estimates the similarity between classification rules by matching in representation-level. A set of comprehensive and precise rules is obtained by genetic programming. After evaluating the diversity, a fusion method makes the final decision with a subset of diverse classification rules. The proposed method is applied to cancer classificaiton using gene expression profiles, which requires high accuracy and reliability. Especially, the experiments on popular cancer datasets have demonstrated the usefulness of the proposed method.
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paper_title: Gene expression A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis

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Motivation: Cancer diagnosis is one of the most important emerging clinical applications of gene expression microarray technology. We are seeking to develop a computer system for powerful and reliable cancer diagnostic model creation based on microarray data. To keep a realistic perspective on clinical applications we focus on multicategory diagnosis. To equip the system with the optimum combination of classifier, gene selection and cross-validation methods, we performed a systematic and comprehensive evaluation of several major algorithms for multicategory classification, several gene selection methods, multiple ensemble classifier methods and two cross-validation designs using 11 datasets spanning 74 diagnostic categories and 41 cancer types and 12 normal tissue types. ::: ::: Results: Multicategory support vector machines (MC-SVMs) are the most effective classifiers in performing accurate cancer diagnosis from gene expression data. The MC-SVM techniques by Crammer and Singer, Weston and Watkins and one-versus-rest were found to be the best methods in this domain. MC-SVMs outperform other popular machine learning algorithms, such as k-nearest neighbors, backpropagation and probabilistic neural networks, often to a remarkable degree. Gene selection techniques can significantly improve the classification performance of both MC-SVMs and other non-SVM learning algorithms. Ensemble classifiers do not generally improve performance of the best non-ensemble models. These results guided the construction of a software system GEMS (Gene Expression Model Selector) that automates high-quality model construction and enforces sound optimization and performance estimation procedures. This is the first such system to be informed by a rigorous comparative analysis of the available algorithms and datasets. ::: ::: Availability: The software system GEMS is available for download from http://www.gems-system.org for non-commercial use. ::: ::: Contact: alexander.statnikov@vanderbilt.edu
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paper_title: Self-organizing maps in mining gene expression data

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Abstract Modern DNA microarray technology provides means of measuring gene expression patterns of the whole genome of simple organisms at once. Exploratory analysis of these large-scale expression datasets is becoming vital to extracting functional information from the measurements. We demonstrate how self-organizing maps (SOM) can be applied to exploratory analysis of gene expression data from a yeast DNA microarray database in order to very rapidly find gene families with similar expression patterns. SOM not only enabled quickly selecting the gene families identified in previous work, but it facilitated identifying additional genes with similar expression patterns. Identifying new families of genes also appears to be possible as demonstrated by additional clusters of genes discovered from the data. Moreover, further insight into the primary pattern variations that discriminate between the families became explicit.
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paper_title: An integrated probabilistic model for functional prediction of proteins

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We develop an integrated probabilistic model to combine protein physical interactions, genetic interactions, highly correlated gene expression networks, protein complex data, and domain structures of individual proteins to predict protein functions. The model is an extension of our previous model for protein function prediction based on Markovian random field theory. The model is flexible in that other protein pairwise relationship information and features of individual proteins can be easily incorporated. Two features distinguish the integrated approach from other available methods for protein function prediction. One is that the integrated approach uses all available sources of information with different weights for different sources of data. It is a global approach that takes the whole network into consideration. The second feature is that the posterior probability that a protein has the function of interest is assigned. The posterior probability indicates how confident we are about assigning the function to the protein. We apply our integrated approach to predict functions of yeast proteins based upon MIPS protein function classifications and upon the interaction networks based on MIPS physical and genetic interactions, gene expression profiles, tandem affinity purification (TAP) protein complex data, and protein domain information. We study the recall and precision of the integrated approach using different sources of information by the leave-one-out approach. In contrast to using MIPS physical interactions only, the integrated approach combining all of the information increases the recall from 57% to 87% when the precision is set at 57%-an increase of 30%.
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paper_title: Hypergraph and protein function prediction with gene expression data

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Most network-based protein (or gene) function prediction methods are based on the assumption that the labels of two adjacent proteins in the network are likely to be the same. However, assuming the pairwise relationship between proteins or genes is not complete, the information a group of genes that show very similar patterns of expression and tend to have similar functions (i.e. the functional modules) is missed. The natural way overcoming the information loss of the above assumption is to represent the gene expression data as the hypergraph. Thus, in this paper, the three un-normalized, random walk, and symmetric normalized hypergraph Laplacian based semi-supervised learning methods applied to hypergraph constructed from the gene expression data in order to predict the functions of yeast proteins are introduced. Experiment results show that the average accuracy performance measures of these three hypergraph Laplacian based semi-supervised learning methods are the same. However, their average accuracy performance measures of these three methods are much greater than the average accuracy performance measures of un-normalized graph Laplacian based semi-supervised learning method (i.e. the baseline method of this paper) applied to gene co-expression network created from the gene expression data.
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paper_title: Analysis of Gene Expression Data with Pathway Scores

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We present a new approach for the evaluation of gene expression data. The basic idea is to generate biologically possible pathways and to score them with respect to gene expression measurements. We suggest sample scoring functions for different problem specifications. We assess the significance of the scores for the investigated pathways by comparison to a number of scores for random pathways. We show that simple scoring functions can assign statistically significant scores to biologically relevant pathways. This suggests that the combination of appropriate scoring functions with the systematic generation of pathways can be used in order to select the most interesting pathways based on gene expression measurements.
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paper_title: A pathway analysis method for genome‐wide association studies

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For genome-wide association studies, we propose a new method for identifying significant biological pathways. In this approach, we aggregate data across single-nucleotide polymorphisms to obtain summary measures at the gene level. We then use a hierarchical Bayesian model, which takes the gene-level summary measures as data, in order to evaluate the relevance of each pathway to an outcome of interest (e.g., disease status). Although shifting the focus of analysis from individual genes to pathways has proven to improve the statistical power and provide more robust results, such methods tend to eliminate a large number of genes whose pathways are unknown. For these genes, we propose to use a Bayesian multinomial logit model to predict the associated pathways by using the genes with known pathways as the training data. Our hierarchical Bayesian model takes the uncertainty regarding the pathway predictions into account while assessing the significance of pathways. We apply our method to two independent studies on type 2 diabetes and show that the overlap between the results from the two studies is statistically significant. We also evaluate our approach on the basis of simulated data. Copyright © 2012 John Wiley & Sons, Ltd.
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paper_title: Machine learning methods for metabolic pathway prediction

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BackgroundA key challenge in systems biology is the reconstruction of an organism's metabolic network from its genome sequence. One strategy for addressing this problem is to predict which metabolic pathways, from a reference database of known pathways, are present in the organism, based on the annotated genome of the organism.ResultsTo quantitatively validate methods for pathway prediction, we developed a large "gold standard" dataset of 5,610 pathway instances known to be present or absent in curated metabolic pathway databases for six organisms. We defined a collection of 123 pathway features, whose information content we evaluated with respect to the gold standard. Feature data were used as input to an extensive collection of machine learning (ML) methods, including naïve Bayes, decision trees, and logistic regression, together with feature selection and ensemble methods. We compared the ML methods to the previous PathoLogic algorithm for pathway prediction using the gold standard dataset. We found that ML-based prediction methods can match the performance of the PathoLogic algorithm. PathoLogic achieved an accuracy of 91% and an F-measure of 0.786. The ML-based prediction methods achieved accuracy as high as 91.2% and F-measure as high as 0.787. The ML-based methods output a probability for each predicted pathway, whereas PathoLogic does not, which provides more information to the user and facilitates filtering of predicted pathways.ConclusionsML methods for pathway prediction perform as well as existing methods, and have qualitative advantages in terms of extensibility, tunability, and explainability. More advanced prediction methods and/or more sophisticated input features may improve the performance of ML methods. However, pathway prediction performance appears to be limited largely by the ability to correctly match enzymes to the reactions they catalyze based on genome annotations.
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paper_title: Building pathway clusters from Random Forests classification using class votes

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BackgroundRecent years have seen the development of various pathway-based methods for the analysis of microarray gene expression data. These approaches have the potential to bring biological insights into microarray studies. A variety of methods have been proposed to construct networks using gene expression data. Because individual pathways do not act in isolation, it is important to understand how different pathways coordinate to perform cellular functions. However, there are no published methods describing how to build pathway clusters that are closely related to traits of interest.ResultsWe propose to build pathway clusters from pathway-based classification methods. The proposed methods allow researchers to identify clusters of pathways sharing similar functions. These pathways may or may not share genes. As an illustration, our approach is applied to three human breast cancer microarray data sets. We found that our methods yielded consistent and interpretable results for these three data sets. We further investigated one of the pathway clusters found using PubMatrix. We found that informative genes in the pathway clusters do have more publications with keywords, like estrogen receptor, compared with informative genes in other top pathways. In addition, using the shortest path analysis in GeneGo's MetaCore and Human Protein Reference Database, we were able to identify the links which connect the pathways without shared genes within the pathway cluster.ConclusionOur proposed pathway clustering methods allow bioinformaticians and biologists to investigate how informative genes within pathways are related to each other and understand possible crosstalk between pathways in a cluster. Therefore, building pathway clusters may lead to a better understanding of molecular mechanisms affecting a trait of interest, and help generate further biological hypotheses from gene expression data.
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paper_title: A two-stage machine learning approach for pathway analysis

paper_content:

Analysis of gene expression data has emerged as an important approach to discover active pathways related to biological phenotypes. Previous pathway analysis methods use all genes in a pathway for linking it to a particular phenotype. Using only a subset of informative genes, however, could better classify samples. Here, we propose a two-stage machine learning approach for pathway analysis. During the first stage, informative genes that can represent a pathway are selected using feature selection methods. These “representative genes” are mostly associated with the phenotype of interest. In the second stage, pathways are ranked based on their “representative genes” using classification methods. We applied our two-stage approach on three gene expression datasets. The results indicate our method does outperform methods that consider every gene in a pathway.
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paper_title: Incorporating prior knowledge of gene functional groups into regularized discriminant analysis of microarray data

paper_content:

Motivation: Discriminant analysis for high-dimensional and low-sample-sized data has become a hot research topic in bioinformatics, mainly motivated by its importance and challenge in applications to tumor classifications for high-dimensional microarray data. Two of the popular methods are the nearest shrunken centroids, also called predictive analysis of microarray (PAM), and shrunken centroids regularized discriminant analysis (SCRDA). Both methods are modifications to the classic linear discriminant analysis (LDA) in two aspects tailored to high-dimensional and low-sample-sized data: one is the regularization of the covariance matrix, and the other is variable selection through shrinkage. In spite of their usefulness, there are potential limitations with each method. The main concern is that both PAM and SCRDA are possibly too extreme: the covariance matrix in the former is restricted to be diagonal while in the latter there is barely any restriction. Based on the biology of gene functions and given the feature of the data, it may be beneficial to estimate the covariance matrix as an intermediate between the two; furthermore, more effective shrinkage schemes may be possible. ::: ::: Results: We propose modified LDA methods to integrate biological knowledge of gene functions (or variable groups) into classification of microarray data. Instead of simply treating all the genes independently or imposing no restriction on the correlations among the genes, we group the genes according to their biological functions extracted from existing biological knowledge or data, and propose regularized covariance estimators that encourages between-group gene independence and within-group gene correlations while maintaining the flexibility of any general covariance structure. Furthermore, we propose a shrinkage scheme on groups of genes that tends to retain or remove a whole group of the genes altogether, in contrast to the standard shrinkage on individual genes. We show that one of the proposed methods performed better than PAM and SCRDA in a simulation study and several real data examples. ::: ::: Contact: weip@biostat.umn.edu
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paper_title: Improving gene set analysis of microarray data by SAM-GS

paper_content:

BackgroundGene-set analysis evaluates the expression of biological pathways, or a priori defined gene sets, rather than that of individual genes, in association with a binary phenotype, and is of great biologic interest in many DNA microarray studies. Gene Set Enrichment Analysis (GSEA) has been applied widely as a tool for gene-set analyses. We describe here some critical problems with GSEA and propose an alternative method by extending the individual-gene analysis method, Significance Analysis of Microarray (SAM), to gene-set analyses (SAM-GS).ResultsUsing a mouse microarray dataset with simulated gene sets, we illustrate that GSEA gives statistical significance to gene sets that have no gene associated with the phenotype (null gene sets), and has very low power to detect gene sets in which half the genes are moderately or strongly associated with the phenotype (truly-associated gene sets). SAM-GS, on the other hand, performs very well. The two methods are also compared in the analyses of three real microarray datasets and relevant pathways, the diverging results of which clearly show advantages of SAM-GS over GSEA, both statistically and biologically. In a microarray study for identifying biological pathways whose gene expressions are associated with p53 mutation in cancer cell lines, we found biologically relevant performance differences between the two methods. Specifically, there are 31 additional pathways identified as significant by SAM-GS over GSEA, that are associated with the presence vs. absence of p53. Of the 31 gene sets, 11 actually involve p53 directly as a member. A further 6 gene sets directly involve the extrinsic and intrinsic apoptosis pathways, 3 involve the cell-cycle machinery, and 3 involve cytokines and/or JAK/STAT signaling. Each of these 12 gene sets, then, is in a direct, well-established relationship with aspects of p53 signaling. Of the remaining 8 gene sets, 6 have plausible, if less well established, links with p53.ConclusionWe conclude that GSEA has important limitations as a gene-set analysis approach for microarray experiments for identifying biological pathways associated with a binary phenotype. As an alternative statistically-sound method, we propose SAM-GS. A free Excel Add-In for performing SAM-GS is available for public use.
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Title: A Survey of Computational Intelligence Techniques in Protein Function Prediction

Section 1: Introduction
Description 1: This section introduces the importance and challenges of protein function prediction in bioinformatics and discusses traditional experimental methods and their limitations.

Section 2: An Overview of Computational Intelligence Techniques in Protein Function Prediction by Using Sequence and Structure
Description 2: This section presents a comprehensive review of various computational intelligence techniques used for protein function prediction based on sequence and structural data.

Section 3: Computational Intelligence Techniques in Prediction of Binding Sites
Description 3: This section elaborates on the computational methods used to predict DNA and RNA binding sites in proteins, highlighting different machine learning approaches and their efficacy.

Section 4: Computational Intelligence Techniques in Prediction of Subcellular Localization
Description 4: This section describes the methods employed to predict the subcellular localization of proteins using computational intelligence techniques.

Section 5: Computational Intelligence Techniques in Prediction of Signal Peptides
Description 5: This section discusses approaches for predicting signal peptides and their cleavage sites using computational intelligence methods.

Section 6: Computational Intelligence Techniques in Prediction of Catalytic Residues
Description 6: This section addresses the prediction of catalytic residues in enzymes, including various computational intelligence techniques used.

Section 7: Computational Intelligence Techniques in Prediction of Nuclear/G-Protein Coupled Receptor
Description 7: This section covers methods for predicting nuclear receptors and G-protein coupled receptors subfamilies using computational intelligence approaches.

Section 8: Computational Intelligence Techniques in Prediction of Membrane Protein
Description 8: This section reviews computational techniques used to predict membrane proteins and their types.

Section 9: Computational Intelligence Techniques in Prediction of Enzyme Function/Family
Description 9: This section discusses the prediction of enzyme functions and families using various computational intelligence techniques.

Section 10: Computational Intelligence Techniques in Protein Functions Prediction by Protein Interaction Network
Description 10: This section explores methods that utilize protein-protein interaction networks for protein function prediction.

Section 11: Computational Intelligence Techniques in Protein Functions Prediction by Gene Expression Data
Description 11: This section delves into computational techniques used for predicting protein functions by analyzing gene expression data.

Section 12: Computational Intelligence Techniques in Pathway Analysis from Gene Expression Data
Description 12: This section examines pathways analysis from gene expression data using computational intelligence techniques.

Section 13: Case Study for Protein Function Prediction by Using Sequence Derived Properties
Description 13: This section presents a comparative analysis of computational intelligence methods for protein function prediction using sequence-derived properties.

Section 14: Conclusion
Description 14: This section provides a summary of the survey's findings and suggests future directions for improving protein function prediction using computational intelligence techniques.