A review of statistical methods for prediction of proteolytic cleavage
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paper_title: A new method for predicting signal sequence cleavage sites

paper_content:

A new method for identifying secretory signal sequences and for predicting the site of cleavage between a signal sequence and the mature exported protein is described. The predictive accuracy is estimated to be around 75-80% for both prokaryotic and eukaryotic proteins.
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paper_title: HIVcleave: a web-server for predicting human immunodeficiency virus protease cleavage sites in proteins.

paper_content:

According to the ‘‘distorted key theory’’ [K.C. Chou, Analytical Biochemistry, 233 (1996) 1–14], the information of cleavage sites of proteins by HIV (human immunodeficiency virus) protease is very useful for finding effective inhibitors against HIV, the culprit of AIDS (acquired immunodeficiency syndrome). To meet the increasing need in this regard, a web-server called HIVcleave was established at http://chou.med.harvard.edu/bioinf/HIV/. In this note we provide a step-to-step guide for how to use HIVcleave to identify the cleavage sites of a query protein sequence by HIV-1 and HIV-2 proteases, respectively.
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paper_title: Genetic variation in the gene encoding calpain-10 is associated with type 2 diabetes mellitus

paper_content:

Type 2 or non-insulin-dependent diabetes mellitus (NIDDM) is the most common form of diabetes worldwide, affecting approximately 4% of the world's adult population. It is multifactorial in origin with both genetic and environmental factors contributing to its development. A genome-wide screen for type 2 diabetes genes carried out in Mexican Americans localized a susceptibility gene, designated NIDDM1, to chromosome 2. Here we describe the positional cloning of a gene located in the NIDDM1 region that shows association with type 2 diabetes in Mexican Americans and a Northern European population from the Botnia region of Finland. This putative diabetes-susceptibility gene encodes a ubiquitously expressed member of the calpain-like cysteine protease family, calpain-10 (CAPN10). This finding suggests a novel pathway that may contribute to the development of type 2 diabetes.
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paper_title: Why Neural Networks Should Not Be Used for HIV-1 Protease Cleavage Site Prediction

paper_content:

Summary: Several papers have been published where nonlinear machine learning algorithms, e.g. artificial neural networks, support vector machines and decision trees, have been used to model the specificity of the HIV-1 protease and extract specificity rules. We show that the dataset used in these studies is linearly separable and that it is a misuse of nonlinear classifiers to apply them to this problem. The best solution on this dataset is achieved using a linear classifier like the simple perceptron or the linear support vector machine, and it is straightforward to extract rules from these linear models. We identify key residues in peptides that are efficiently cleaved by the HIV-1 protease and list the most prominent rules, relating them to experimental results for the HIV-1 protease. ::: ::: Motivation: Understanding HIV-1 protease specificity is important when designing HIV inhibitors and several different machine learning algorithms have been applied to the problem. However, little progress has been made in understanding the specificity because nonlinear and overly complex models have been used. ::: ::: Results: We show that the problem is much easier than what has previously been reported and that linear classifiers like the simple perceptron or linear support vector machines are at least as good predictors as nonlinear algorithms. We also show how sets of specificity rules can be generated from the resulting linear classifiers. ::: ::: Availability: The datasets used are available at http://www.hh.se/staff/bioinf/
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paper_title: Calpain-dependent proteolysis of NF2 protein: Involvement in schwannomas and meningiomas

paper_content:

The neurofibromatosis type 2 (NF2) protein, known as merlin or schwannomin, is a tumor suppressor, and the NF2 gene has been found to be mutated in the majority of schwannomas and meningiomas, including both sporadically occurring and familial NF2 cases. Although the development of these tumors depends on the loss of merlin, the presence of tumors lacking detectable NF2 mutations suggests different mechanisms for inactivating merlin. Recent studies have demonstrated cleavage of merlin by calpain, a calcium-dependent neutral cysteine protease, and marked activation of the calpain system resulting in the degradation of merlin in these tumors. Increased turnover of merlin by calpain in some schwannomas and meningiomas exemplifies tumorigenesis linked to the calpain-mediated proteolytic pathway.
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paper_title: Calpains and Their Multiple Roles in Diabetes Mellitus

paper_content:

: Type 2 diabetes mellitus (T2DM) can lead to death without treatment and it has been predicted that the condition will affect 215 million people worldwide by 2010. T2DM is a multifactorial disorder whose precise genetic causes and biochemical defects have not been fully elucidated, but at both levels, calpains appear to play a role. Positional cloning studies mapped T2DM susceptibility to CAPN10, the gene encoding the intracellular cysteine protease, calpain 10. Further studies have shown a number of noncoding polymorphisms in CAPN10 to be functionally associated with T2DM while the identification of coding polymorphisms, suggested that mutant calpain 10 proteins may also contribute to the disease. Here we review recent studies, which in addition to the latter enzyme, have linked calpain 5, calpain 3, and its splice variants, calpain 2 and calpain 1 to T2DM-related metabolic pathways along with T2DM-associated phenotypes, such as obesity and impaired insulin secretion, and T2DM-related complications, such as epithelial dysfunction and diabetic cataract.
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paper_title: Calpain proteases in cell adhesion and motility.

paper_content:

Cell adhesion and its role during cell spreading and motility are central to normal development and homeostasis, including its effects on immune response and wound repair and tissue regeneration. Disruption of cell adhesion impacts not only the healing process but promotes tumor invasion and metastasis. A family of intracellular, limited proteases, the calpains, has recently been shown to be a key molecular control point in attachment of cells to the surrounding matrix. Herein, the two main and ubiquitously expressed calpain isoforms will be introduced as to their modes of regulation and the current status of research will be discussed as to how these calpains might function in the biophysical process of adhesion and biological cellular responses of spreading and motility.
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paper_title: Calpain Cleavage Prediction Using Multiple Kernel Learning

paper_content:

Calpain, an intracellular Ca²⁺-dependent cysteine protease, is known to play a role in a wide range of metabolic pathways through limited proteolysis of its substrates. However, only a limited number of these substrates are currently known, with the exact mechanism of substrate recognition and cleavage by calpain still largely unknown. While previous research has successfully applied standard machine-learning algorithms to accurately predict substrate cleavage by other similar types of proteases, their approach does not extend well to calpain, possibly due to its particular mode of proteolytic action and limited amount of experimental data. Through the use of Multiple Kernel Learning, a recent extension to the classic Support Vector Machine framework, we were able to train complex models based on rich, heterogeneous feature sets, leading to significantly improved prediction quality (6% over highest AUC score produced by state-of-the-art methods). In addition to producing a stronger machine-learning model for the prediction of calpain cleavage, we were able to highlight the importance and role of each feature of substrate sequences in defining specificity: primary sequence, secondary structure and solvent accessibility. Most notably, we showed there existed significant specificity differences across calpain sub-types, despite previous assumption to the contrary. Prediction accuracy was further successfully validated using, as an unbiased test set, mutated sequences of calpastatin (endogenous inhibitor of calpain) modified to no longer block calpain's proteolytic action. An online implementation of our prediction tool is available at http://calpain.org.
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paper_title: Neural network prediction of the HIV-1 protease cleavage sites.

paper_content:

Abstract A back propagation neural network method has been developed to study the pattern of polypeptides than can be cleaved by the HIV-1 protease. This method can incorporate many characteristics of the peptides, such as hydrophobicity, β-sheet and α-helix propensities. Mutations can also be applied to probe the most important factors that influence the cleavage.
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paper_title: Redesigning trypsin: alteration of substrate specificity.

paper_content:

A general method for modifying eukaryotic genes by site-specific mutagenesis and subsequent expression in mammalian cells was developed to study the relation between structure and function of the proteolytic enzyme trypsin. Glycine residues at positions 216 and 226 in the binding cavity of trypsin were replaced by alanine residues, resulting in three trypsin mutants. Computer graphic analysis suggested that these substitutions would differentially affect arginine and lysine substrate binding of the enzyme. Although the mutant enzymes were reduced in catalytic rate, they showed enhanced substrate specificity relative to the native enzyme. This increased specificity was achieved by the unexpected differential effects on the catalytic activity toward arginine and lysine substrates. Mutants containing alanine at position 226 exhibited an altered conformation that may be converted to a trypsin-like structure upon binding of a substrate analog.
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paper_title: Mutations in the proteolytic enzyme calpain 3 cause limb-girdle muscular dystrophy type 2A

paper_content:

Abstract Limb-girdle muscular dystrophies (LGMDs) are a group of inherited diseases whose genetic etiology has yet to be elucidated. The autosomal recessive forms (LGMD2) constitute a genetically heterogeneous group with LGMD2A mapping to chromosome 15815.1–821.1. The gene encoding the muscle-specific calcium-activated neutral protease 3 (CANP3) large subunit is located in this region. This cysteine protease belongs to the family of intracellular calpains. Fifteen nonsense, splice site, frameshift, or missense calpain mutations cosegregate with the disease in LGMD2A families, six of which were found within La Reunion island patients. A digenic inheritance model is proposed to account for the unexpected presence of multiple independent mutations in this small inbred population. Finally, these results demonstrate an enzymatic rather than a structural protein defect causing a muscular dystrophy, a defect that may have regulatory consequences, perhaps in signal transduction.
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paper_title: FUNCTIONAL DEFECTS OF A MUSCLE-SPECIFIC CALPAIN, P94, CAUSED BY MUTATIONS ASSOCIATED WITH LIMB-GIRDLE MUSCULAR DYSTROPHY TYPE 2A

paper_content:

Next Section Abstract p94 (calpain3), a muscle-specific member of the calpain family, has been shown to be responsible for limb-girdle muscular dystrophy type 2A (LGMD2A), a form of autosomal recessive and progressive neuromuscular disorder. To elucidate the molecular mechanism of LGMD2A, we constructed nine p94 missense point mutants found in LGMD2A and analyzed their p94 unique properties. All mutants completely or almost completely lose the proteolytic activity against a potential substrate, fodrin. However, some of the mutants still possess autolytic activity and/or connectin/titin binding ability, indicating these properties are not necessary for the LGMD2A phenotypes. These results provide strong evidence that LGMD2A results from the loss of proteolysis of substrates by p94, suggesting a novel molecular mechanism leading to muscular dystrophies.
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paper_title: Prediction of caspase cleavage sites using Bayesian bio-basis function neural networks

paper_content:

Motivation: Apoptosis has drawn the attention of researchers because of its importance in treating some diseases through finding a proper way to block or slow down the apoptosis process. Having understood that caspase cleavage is the key to apoptosis, we find novel methods or algorithms are essential for studying the specificity of caspase cleavage activity and this helps the effective drug design. As bio-basis function neural networks have proven to outperform some conventional neural learning algorithms, there is a motivation, in this study, to investigate the application of bio-basis function neural networks for the prediction of caspase cleavage sites. ::: ::: Results: Thirteen protein sequences with experimentally determined caspase cleavage sites were downloaded from NCBI. Bayesian bio-basis function neural networks are investigated and the comparisons with single-layer perceptrons, multilayer perceptrons, the original bio-basis function neural networks and support vector machines are given. The impact of the sliding window size used to generate sub-sequences for modelling on prediction accuracy is studied. The results show that the Bayesian bio-basis function neural network with two Gaussian distributions for model parameters (weights) performed the best and the highest prediction accuracy is 97.15 ± 1.13%. ::: ::: Availability: The package of Bayesian bio-basis function neural network can be obtained by request to the author. ::: ::: Contact: [email protected]
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paper_title: SVM-based prediction of caspase substrate cleavage sites

paper_content:

BackgroundCaspases belong to a class of cysteine proteases which function as critical effectors in apoptosis and inflammation by cleaving substrates immediately after unique sites. Prediction of such cleavage sites will complement structural and functional studies on substrates cleavage as well as discovery of new substrates. Recently, different computational methods have been developed to predict the cleavage sites of caspase substrates with varying degrees of success. As the support vector machines (SVM) algorithm has been shown to be useful in several biological classification problems, we have implemented an SVM-based method to investigate its applicability to this domain.ResultsA set of unique caspase substrates cleavage sites were obtained from literature and used for evaluating the SVM method. Datasets containing (i) the tetrapeptide cleavage sites, (ii) the tetrapeptide cleavage sites, augmented by two adjacent residues, P1' and P2' amino acids and (iii) the tetrapeptide cleavage sites with ten additional upstream and downstream flanking sequences (where available) were tested. The SVM method achieved an accuracy ranging from 81.25% to 97.92% on independent test sets. The SVM method successfully predicted the cleavage of a novel caspase substrate and its mutants.ConclusionThis study presents an SVM approach for predicting caspase substrate cleavage sites based on the cleavage sites and the downstream and upstream flanking sequences. The method shows an improvement over existing methods and may be useful for predicting hitherto undiscovered cleavage sites.
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paper_title: Substrate profiling of cysteine proteases using a combinatorial peptide library identifies functionally unique specificities.

paper_content:

The substrate specificities of papain-like cysteine proteases (clan CA, family C1) papain, bromelain, and human cathepsins L, V, K, S, F, B, and five proteases of parasitic origin were studied using a completely diversified positional scanning synthetic combinatorial library. A bifunctional coumarin fluorophore was used that facilitated synthesis of the library and individual peptide substrates. The library has a total of 160,000 tetrapeptide substrate sequences completely randomizing each of the P1, P2, P3, and P4 positions with 20 amino acids. A microtiter plate assay format permitted a rapid determination of the specificity profile of each enzyme. Individual peptide substrates were then synthesized and tested for a quantitative determination of the specificity of the human cathepsins. Despite the conserved three-dimensional structure and similar substrate specificity of the enzymes studied, distinct amino acid preferences that differentiate each enzyme were identified. The specificities of cathepsins K and S partially match the cleavage site sequences in their physiological substrates. Capitalizing on its unique preference for proline and glycine at the P2 and P3 positions, respectively, selective substrates and a substrate-based inhibitor were developed for cathepsin K. A cluster analysis of the proteases based on the complete specificity profile provided a functional characterization distinct from standard sequence analysis. This approach provides useful information for developing selective chemical probes to study protease-related pathologies and physiologies.
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paper_title: Artificial neural network model for predicting HIV protease cleavage sites in protein

paper_content:

Knowledge of the polyprotein cleavage sites by HIV protease will refine our understanding of its specificity, and the information thus acquired will be useful for designing specific and efficient HIV protease inhibitors. The search for inhibitors of HIV protease will be greatly expedited if one can find an accurate, robust, and rapid method for predicting the cleavage sites in proteins by HIV protease. In this paper, Kohonen's self-organization model, which uses typical artificial neural networks, is applied to predict the cleavability of oligopeptides by proteases with multiple and extended specificity subsites. We selected HIV-1 protease as the subject of study. We chose 299 oligopeptides for the training set, and another 63 oligopeptides for the test set. Because of its high rate of correct prediction (58/63 = 92.06%) and stronger fault-tolerant ability, the neural network method should be a useful technique for finding effective inhibitors of HIV protease, which is one of the targets in designing potential drugs against AIDS. The principle of the artificial neural network method can also be applied to analyzing the specificity of any multisubsite enzyme.
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paper_title: CutDB: a proteolytic event database

paper_content:

Beyond the well-known role of proteolytic machinery in protein degradation and turnover, many specialized proteases play a key role in various regulatory processes. Thousands of highly specific proteolytic events are associated with normal and pathological conditions, including bacterial and viral infections. However, the information about individual proteolytic events is dispersed over multiple publications and is not easily available for large-scale analysis. CutDB is one of the first systematic efforts to build an easily accessible collection of documented proteolytic events for natural proteins in vivo or in vitro. A CutDB entry is defined by a unique combination of these three attributes: protease, protein substrate and cleavage site. Currently, CutDB integrates 3070 proteolytic events for 470 different proteases captured from public archives (such as MEROPS and HPRD) and publications. CutDB supports various types of data searches and displays, including clickable network diagrams. Most importantly, CutDB is a community annotation resource based on a Wikipedia approach, providing a convenient user interface to input new data online. A recent contribution of 568 proteolytic events by several experts in the field of matrix metallopeptidases suggests that this approach will significantly accelerate the development of CutDB content. CutDB is publicly available at http://cutdb.burnham.org.
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paper_title: MEROPS: the peptidase database

paper_content:

Peptidases (proteolytic enzymes) are of great relevance to biology, medicine and biotechnology. This practical importance creates a need for an integrated source of information about them, and also about their natural inhibitors. The MEROPS database (http://merops.sanger.ac.uk) aims to fill this need. The organizational principle of the database is a hierarchical classification in which homologous sets of the proteins of interest are grouped in families and the homologous families are grouped in clans. Each peptidase, family and clan has a unique identifier. The database has recently been expanded to include the protein inhibitors of peptidases, and these are classified in much the same way as the peptidases. Forms of information recently added include new links to other databases, summary alignments for peptidase clans, displays to show the distribution of peptidases and inhibitors among organisms, substrate cleavage sites and indexes for expressed sequence tag libraries containing peptidases. A new way of making hyperlinks to the database has been devised and a BlastP search of our library of peptidase and inhibitor sequences has been added.
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paper_title: 202 CaMPDB: A RESOURCE FOR CALPAIN AND MODULATORY PROTEOLYSIS

paper_content:

While the importance of modulatory proteolysis in research has steadily increased, knowledge on this process has remained largely disorganized, with the nature and role of entities composing modulatory proteolysis still uncertain. We built CaMPDB, a resource on modulatory proteolysis, with a focus on calpain, a well-studied intracellular protease which regulates substrate functions by proteolytic processing. CaMPDB contains sequences of calpains, substrates and inhibitors as well as substrate cleavage sites, collected from the literature. Some cleavage efficiencies were evaluated by biochemical experiments and a cleavage site prediction tool is provided to assist biologists in understanding calpain-mediated cellular processes. CaMPDB is freely accessible at http://calpain.org.
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paper_title: A unique specificity of a calcium activated neutral protease indicated in histone hydrolysis.

paper_content:

Calf thymus histones were found to be susceptible to a calcium-activated neutral protease [CANP: EC 3.4.22.17] which required a high concentration of calcium ions for its activity (mCANP). The susceptibilities of histones were in the order of relative degradation rate: H2B, H2A, and H3. The major peptide fragments released by CANP from H2A, H2B, and H3 were isolated and the cleavage sites were determined. Examination of amino acid sequences and environmental features around the cleavage site as well as kinetic analysis of the degradation process led us to the following conclusions about the mode of substrate recognition of mCANP: 1) The cleavage sites in histones could not be interpreted in terms of the primary structure around them. Thus, it seems unlikely that the specificity of CANP solely depends on its recognition of any specific amino acid residues or sequences. 2) The susceptible bonds were never located in the midst of either a hydrophobic or hydrophilic alignment of amino acid residues but in the vicinity of the boundary between hydrophilic and hydrophobic clusters. 3) Once a peptide fragment was generated by the proteolytic degradation, no further cleavage occurred even if the peptide still contained a bond corresponding to what was susceptible to CANP in an intact histone. This observation was interpreted to mean that CANP may recognize a certain higher order structure of its substrates.
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paper_title: Predicting the secondary structure of globular proteins using neural networks models

paper_content:

We present a new method for predicting the secondary structure of globular proteins based on non-linear neural network models. Network models learn from existing protein structures how to predict the secondary structure of local sequences of amino acids. The average success rate of our method on a testing set of proteins non-homologous with the corresponding training set was 64.3% on three types of secondary structure (alpha-helix, beta-sheet, and coil), with correlation coefficients of C alpha = 0.41, C beta = 0.31 and Ccoil = 0.41. These quality indices are all higher than those of previous methods. The prediction accuracy for the first 25 residues of the N-terminal sequence was significantly better. We conclude from computational experiments on real and artificial structures that no method based solely on local information in the protein sequence is likely to produce significantly better results for non-homologous proteins. The performance of our method of homologous proteins is much better than for non-homologous proteins, but is not as good as simply assuming that homologous sequences have identical structures.
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paper_title: Bio-support vector machines for computational proteomics

paper_content:

MOTIVATION ::: One of the most important issues in computational proteomics is to produce a prediction model for the classification or annotation of biological function of novel protein sequences. In order to improve the prediction accuracy, much attention has been paid to the improvement of the performance of the algorithms used, few is for solving the fundamental issue, namely, amino acid encoding as most existing pattern recognition algorithms are unable to recognize amino acids in protein sequences. Importantly, the most commonly used amino acid encoding method has the flaw that leads to large computational cost and recognition bias. ::: ::: ::: RESULTS ::: By replacing kernel functions of support vector machines (SVMs) with amino acid similarity measurement matrices, we have modified SVMs, a new type of pattern recognition algorithm for analysing protein sequences, particularly for proteolytic cleavage site prediction. We refer to the modified SVMs as bio-support vector machine. When applied to the prediction of HIV protease cleavage sites, the new method has shown a remarkable advantage in reducing the model complexity and enhancing the model robustness.
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paper_title: Applying support vector machines to imbalanced datasets

paper_content:

Support Vector Machines (SVM) have been extensively studied and have shown remarkable success in many applications. However the success of SVM is very limited when it is applied to the problem of learning from imbalanced datasets in which negative instances heavily outnumber the positive instances (e.g. in gene profiling and detecting credit card fraud). This paper discusses the factors behind this failure and explains why the common strategy of undersampling the training data may not be the best choice for SVM. We then propose an algorithm for overcoming these problems which is based on a variant of the SMOTE algorithm by Chawla et al, combined with Veropoulos et al's different error costs algorithm. We compare the performance of our algorithm against these two algorithms, along with undersampling and regular SVM and show that our algorithm outperforms all of them.
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paper_title: A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems

paper_content:

The area under the ROC curve, or the equivalent Gini index, is a widely used measure of performance of supervised classification rules. It has the attractive property that it side-steps the need to specify the costs of the different kinds of misclassification. However, the simple form is only applicable to the case of two classes. We extend the definition to the case of more than two classes by averaging pairwise comparisons. This measure reduces to the standard form in the two class case. We compare its properties with the standard measure of proportion correct and an alternative definition of proportion correct based on pairwise comparison of classes for a simple artificial case and illustrate its application on eight data sets. On the data sets we examined, the measures produced similar, but not identical results, reflecting the different aspects of performance that they were measuring. Like the area under the ROC curve, the measure we propose is useful in those many situations where it is impossible to give costs for the different kinds of misclassification.
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paper_title: UniProtKB/Swiss-Prot

paper_content:

The Swiss Institute of Bioinformatics (SIB), the European Bioinformatics Institute (EBI), and the Protein Information Resource (PIR) form the Universal Protein Resource (UniProt) consortium. Its main goal is to provide the scientific community with a central resource for protein sequences and functional information. The UniProt consortium maintains the UniProt KnowledgeBase (UniProtKB) and several supplementary databases including the UniProt Reference Clusters (UniRef) and the UniProt Archive (UniParc). (1) UniProtKB is a comprehensive protein sequence knowledgebase that consists of two sections: UniProtKB/Swiss-Prot, which contains manually annotated entries, and UniProtKB/TrEMBL, which contains computer-annotated entries. UniProtKB/Swiss-Prot entries contain information curated by biologists and provide users with cross-links to about 100 external databases and with access to additional information or tools. (2) The UniRef databases (UniRef100, UniRef90, and UniRef50) define clusters of protein sequences that share 100, 90, or 50% identity. (3) The UniParc database stores and maps all publicly available protein sequence data, including obsolete data excluded from UniProtKB. The UniProt databases can be accessed online (http://www.uniprot.org/) or downloaded in several formats (ftp://ftp.uniprot.org/pub). New releases are published every 2 weeks. The purpose of this chapter is to present a guided tour of a UniProtKB/Swiss-Prot entry, paying particular attention to the specificities of plant protein annotation. We will also present some of the tools and databases that are linked to each entry.
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paper_title: Determination of protease cleavage site motifs using mixture-based oriented peptide libraries

paper_content:

The number of known proteases is increasing at a tremendous rate as a consequence of genome sequencing projects. Although one can guess at the functions of these novel enzymes by considering sequence homology to known proteases, there is a need for new tools to rapidly provide functional information on large numbers of proteins. We describe a method for determining the cleavage site specificity of proteolytic enzymes that involves pooled sequencing of peptide library mixtures. The method was used to determine cleavage site motifs for six enzymes in the matrix metalloprotease (MMP) family. The results were validated by comparison with previous literature and by analyzing the cleavage of individually synthesized peptide substrates. The library data led us to identify the proteoglycan neurocan as a novel MMP-2 substrate. Our results indicate that a small set of libraries can be used to quickly profile an expanding protease family, providing information applicable to the design of inhibitors and to the identification of protein substrates.
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paper_title: CaSPredictor: a new computer-based tool for caspase substrate prediction

paper_content:

Motivation:In vitro studies have shown that the most remarkable catalytic features of caspases, a family of cysteineproteases, are their stringent specificity to Asp (D) in the S1 subsite and at least four amino acids to the left of scissile bound. However, there is little information about the substrate recognition patterns in vivo. The prediction and characterization of proteolytic cleavage sites in natural substrates could be useful for uncovering these structural relationships. ::: ::: Results: PEST-like sequences rich in the amino acids Ser (S), Thr (T), Pro (P), Glu or Asp (E/D), including Asn (N) and Gln (Q) are adjacent structural/sequential elements in the majority of cleavage site regions of the natural caspase substrates described in the literature, supporting its possible implication in the substrate selection by caspases. We developed CaSPredictor, a software which incorporated a PEST-like index and the position-dependent amino acid matrices for prediction of caspase cleavage sites in individual proteins and protein datasets. The program predicted successfully 81% (111/137) of the cleavage sites in experimentally verified caspase substrates not annotated in its internal data file. Its accuracy and confidence was estimated as 80% using ROC methodology. The program was much more efficient in predicting caspase substrates when compared with PeptideCutter and PEPS software. Finally, the program detected potential cleavage sites in the primary sequences of 1644 proteins in a dataset containing 9986 protein entries. ::: ::: Availability: Requests for software should be made to Dr Jose E. Belizario ::: ::: Contact: [email protected] ::: ::: Supplementary information: Supplementary information is available for academic users at site http://icb.usp.br/~farmaco/Jose/CaSpredictorfiles
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paper_title: The Folding Type of a Protein Is Relevant to the Amino Acid Composition

paper_content:

The folding types of 135 proteins, the three-dimensional structures of which are known, were analyzed in terms of the amino acid composition. The amino acid composition of a protein was expressed as a point in a multidimensional space spanned with 20 axes, on which the corresponding contents of 20 amino acids in the protein were represented. The distribution pattern of proteins in this composition space was examined in relation to five folding types, alpha, beta, alpha/beta, alpha + beta, and irregular type. The results show that amino acid compositions of the alpha, beta, and alpha/beta types are located in different regions in the composition space, thus allowing distinct separation of proteins depending on the folding types. The points representing proteins of the alpha + beta and irregular types, however, are widely scattered in the space, and the existing regions overlap with those of the other folding types. A simple method of utilizing the "distance" in the space was found to be convenient for classification of proteins into the five folding types. The assignment of the folding type with this method gave an accuracy of 70% in the coincidence with the experimental data.
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paper_title: Using substitution probabilities to improve position-specific scoring matrices

paper_content:

Each column of amino acids in a multiple alignment of protein sequences can be represented as a vector of 20 amino acid counts. For alignment and searching applications, the count vector is an imperfect representation of a position, because the observed sequences are an incomplete sample of the full set of related sequences. One general solution to this problem is to model unobserved sequences by adding artificial 'pseudo-counts' to the observed counts. We introduce a simple method for computing pseudo-counts that combines the diversity observed in each alignment position with amino acid substitution probabilities. In extensive empirical tests, this position-based method out-performed other pseudo-count methods and was a substantial improvement over the traditional average score method used for constructing profiles.
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paper_title: A cumulative specificity model for proteases from human immunodeficiency virus types 1 and 2, inferred from statistical analysis of an extended substrate data base.

paper_content:

Statistical analysis of an expanded data base of regions in viral polyproteins and in non-viral proteins that are sensitive to hydrolysis by the protease from human immunodeficiency virus (HIV) type 1 has generated a model which characterizes the substrate specificity of this retroviral enzyme. The model leads to an algorithm for predicting protease-susceptible sites from primary structure. Amino acids in each of the sites from P4 to P4' are tabulated for 40 protein substrates, and the frequency of occurrence for each residue is compared to the natural abundance of that amino acid in a selected data set of globular proteins. The results suggest that the highest stringency for particular amino acid residues is at the P2, P1, and P2' positions of the substrate. The broad specificity of the HIV-1 protease appears to be a consequence of its being able to bind productively substrates in which interactions with only a few Pi or Pi' side-chains need be optimized. The analysis, extended to 22 protein segments cleaved by the HIV-2 protease, delineates marked differences in specificity from that of the HIV-1 enzyme.
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paper_title: Position-based sequence weights

paper_content:

Sequence weighting methods have been used to reduce redundancy and emphasize diversity in multiple sequence alignment and searching applications. Each of these methods is based on a notion of distance between a sequence and an ancestral or generalized sequence. We describe a different approach, which bases weights on the diversity observed at each position in the alignment, rather than on a sequence distance measure. These position-based weights make minimal assumptions, are simple to compute, and perform well in comprehensive evaluations.
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paper_title: A vector projection method for predicting the specificity of GalNAc-transferase

paper_content:

The specificity of UDP-Gal-NAc:polypeptide N-acetylgalactosaminytransferase (GalNAc-transferase) is consistent with the existence of an extended site composed of nine subsites, denoted by P4, P3, P2, P1, P0, P1′, P2′, P3′, and P4′, where the acceptor at P0 is being either Ser or Thr. To predict whether a peptide will react with the enzyme to form a Ser- or Thr-conjugated glycopeptide, a vector projection method is proposed which uses a training set of amino acid sequences surrounding 90 Ser and 106 Thr O-glycosylation sites extracted from the National Biomedical Research Foundation Protein Database. The model postulates independent interactions of the 9 amino acid moieties with their respective binding sites. The high ratio of correct predictions vs. total predictions for the data in both the training and the testing sets indicates that the method is self-consistent and efficient. It provides a rapid means for predicting O-glycosylation and designing effective inhibitors of GalNAc-transferase. © 1995 Wiley-Liss, Inc.
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paper_title: Substrate profiling of cysteine proteases using a combinatorial peptide library identifies functionally unique specificities.

paper_content:

The substrate specificities of papain-like cysteine proteases (clan CA, family C1) papain, bromelain, and human cathepsins L, V, K, S, F, B, and five proteases of parasitic origin were studied using a completely diversified positional scanning synthetic combinatorial library. A bifunctional coumarin fluorophore was used that facilitated synthesis of the library and individual peptide substrates. The library has a total of 160,000 tetrapeptide substrate sequences completely randomizing each of the P1, P2, P3, and P4 positions with 20 amino acids. A microtiter plate assay format permitted a rapid determination of the specificity profile of each enzyme. Individual peptide substrates were then synthesized and tested for a quantitative determination of the specificity of the human cathepsins. Despite the conserved three-dimensional structure and similar substrate specificity of the enzymes studied, distinct amino acid preferences that differentiate each enzyme were identified. The specificities of cathepsins K and S partially match the cleavage site sequences in their physiological substrates. Capitalizing on its unique preference for proline and glycine at the P2 and P3 positions, respectively, selective substrates and a substrate-based inhibitor were developed for cathepsin K. A cluster analysis of the proteases based on the complete specificity profile provided a functional characterization distinct from standard sequence analysis. This approach provides useful information for developing selective chemical probes to study protease-related pathologies and physiologies.
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paper_title: Sequence Logos: A New Way to Display Consensus Sequences

paper_content:

A graphical method is presented for displaying the patterns in a set of aligned sequences. The characters representing the sequence are stacked on top of each other for each position in the aligned sequences. The height of each letter is made proportional to its frequency, and the letters are sorted so the most common one is on top. The height of the entire stack is then adjusted to signify the information content of the sequences at that position. From these 'sequence logos', one can determine not only the consensus sequence but also the relative frequency of bases and the information content (measured in bits) at every position in a site or sequence. The logo displays both significant residues and subtle sequence patterns.
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paper_title: Why Neural Networks Should Not Be Used for HIV-1 Protease Cleavage Site Prediction

paper_content:

Summary: Several papers have been published where nonlinear machine learning algorithms, e.g. artificial neural networks, support vector machines and decision trees, have been used to model the specificity of the HIV-1 protease and extract specificity rules. We show that the dataset used in these studies is linearly separable and that it is a misuse of nonlinear classifiers to apply them to this problem. The best solution on this dataset is achieved using a linear classifier like the simple perceptron or the linear support vector machine, and it is straightforward to extract rules from these linear models. We identify key residues in peptides that are efficiently cleaved by the HIV-1 protease and list the most prominent rules, relating them to experimental results for the HIV-1 protease. ::: ::: Motivation: Understanding HIV-1 protease specificity is important when designing HIV inhibitors and several different machine learning algorithms have been applied to the problem. However, little progress has been made in understanding the specificity because nonlinear and overly complex models have been used. ::: ::: Results: We show that the problem is much easier than what has previously been reported and that linear classifiers like the simple perceptron or linear support vector machines are at least as good predictors as nonlinear algorithms. We also show how sets of specificity rules can be generated from the resulting linear classifiers. ::: ::: Availability: The datasets used are available at http://www.hh.se/staff/bioinf/
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paper_title: Prediction of proprotein convertase cleavage sites.

paper_content:

Many secretory proteins and peptides are synthesized as inactive precursors that in addition to signal peptide cleavage undergo post-translational processing to become biologically active polypeptides. Precursors are usually cleaved at sites composed of single or paired basic amino acid residues by members of the subtilisin/kexin-like proprotein convertase (PC) family. In mammals, seven members have been identified, with furin being the one first discovered and best characterized. Recently, the involvement of furin in diseases ranging from Alzheimer's disease and cancer to anthrax and Ebola fever has created additional focus on proprotein processing. We have developed a method for prediction of cleavage sites for PCs based on artificial neural networks. Two different types of neural networks have been constructed: a furin-specific network based on experimental results derived from the literature, and a general PC-specific network trained on data from the Swiss-Prot protein database. The method predicts cleavage sites in independent sequences with a sensitivity of 95% for the furin neural network and 62% for the general PC network. The ProP method is made publicly available at http://www.cbs.dtu.dk/services/ProP.
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paper_title: Approximation by superpositions of a sigmoidal function

paper_content:

In this paper we demonstrate that finite linear combinations of compositions of a fixed, univariate function and a set of affine functionals can uniformly approximate any continuous function ofn real variables with support in the unit hypercube; only mild conditions are imposed on the univariate function. Our results settle an open question about representability in the class of single hidden layer neural networks. In particular, we show that arbitrary decision regions can be arbitrarily well approximated by continuous feedforward neural networks with only a single internal, hidden layer and any continuous sigmoidal nonlinearity. The paper discusses approximation properties of other possible types of nonlinearities that might be implemented by artificial neural networks.
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paper_title: Neural network studies. 1. Comparison of overfitting and overtraining

paper_content:

The application of feed forward back propagation artificial neural networks with one hidden layer (ANN) to perform the equivalent of multiple linear regression (MLR) has been examined using artificial structured data sets and real literature data. The predictive ability of the networks has been estimated using a training/ test set protocol. The results have shown advantages of ANN over MLR analysis. The ANNs do not require high order terms or indicator variables to establish complex structure-activity relationships. Overfitting does not have any influence on network prediction ability when overtraining is avoided by cross-validation. Application of ANN ensembles has allowed the avoidance of chance correlations and satisfactory predictions of new data have been obtained for a wide range of numbers of neurons in the hidden layer.
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paper_title: Bio-basis function neural network for prediction of protease cleavage sites in proteins

paper_content:

The prediction of protease cleavage sites in proteins is critical to effective drug design. One of the important issues in constructing an accurate and efficient predictor is how to present nonnumerical amino acids to a model effectively. As this issue has not yet been paid full attention and is closely related to model efficiency and accuracy, we present a novel neural learning algorithm aimed at improving the prediction accuracy and reducing the time involved in training. The algorithm is developed based on the conventional radial basis function neural networks (RBFNNs) and is referred to as a bio-basis function neural network (BBFNN). The basic principle is to replace the radial basis function used in RBFNNs by a novel bio-basis function. Each bio-basis is a feature dimension in a numerical feature space, to which a nonnumerical sequence space is mapped for analysis. The bio-basis function is designed using an amino acid mutation matrix verified in biology. Thus, the biological content in protein sequences can be maximally utilized for accurate modeling. Mutual information (MI) is used to select the most informative bio-bases and an ensemble method is used to enhance a decision-making process, hence, improving the prediction accuracy further. The algorithm has been successfully verified in two case studies, namely the prediction of Human Immunodeficiency Virus (HIV) protease cleavage sites and trypsin cleavage sites in proteins.
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paper_title: On the Sequential Determinants of Calpain Cleavage

paper_content:

Abstract The structural clues of substrate recognition by calpain are incompletely understood. In this study, 106 cleavage sites in substrate proteins compiled from the literature have been analyzed to dissect the signal for calpain cleavage and also to enable the design of an ideal calpain substrate and interfere with calpain action via site-directed mutagenesis. In general, our data underline the importance of the primary structure of the substrate around the scissile bond in the recognition process. Significant amino acid preferences were found to extend over 11 residues around the scissile bond, from P4 to P7′. In compliance with earlier data, preferred residues in the P2 position are Leu, Thr, and Val, and in P1 Lys, Tyr, and Arg. In position P1 ′, small hydrophilic residues, Ser and to a lesser extent Thr and Ala, occur most often. Pro dominates the region flanking the P2-P1′ segment, i.e. positions P3 and P2′-P4′; most notable is its occurrence 5.59 times above chance in P3′. Intriguingly, the segment C-terminal to the cleavage site resembles the consensus inhibitory region of calpastatin, the specific inhibitor of the enzyme. Further, the position of the scissile bond correlates with certain sequential attributes, such as secondary structure and PEST score, which, along with the amino acid preferences, suggests that calpain cleaves within rather disordered segments of proteins. The amino acid preferences were confirmed by site-directed mutagenesis of the autolysis sites of Drosophila calpain B; when amino acids at key positions were changed to less preferred ones, autolytic cleavage shifted to other, adjacent sites. Based on these preferences, a new fluorogenic calpain substrate, DABCYLTPLKSPPPSPR-EDANS, was designed and synthesized. In the case of μ- and m-calpain, this substrate is kinetically superior to commercially available ones, and it can be used for the in vivo assessment of the activity of these ubiquitous mammalian calpains.
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paper_title: Toward Computer-Based Cleavage Site Prediction of Cysteine Endopeptidases

paper_content:

Identification of relevant substrates is essential for elucidation of in vivo functions of peptidases. The recent availability of the complete genome sequences of many eukaryotic organisms holds the promise of identifyingspecific peptidase substrates by systematic proteome analyses in combination with computer-based screening of genome databases. Currently available proteomics and bioinformatics tools are not sufficient for reliable endopeptidase substrate predictions. To address these shortcomings the bioinformatics tool 'PEPS' (Prediction of Endopeptidase Substrates) has been developed and is presented here. PEPS uses individual rule-based endopeptidase cleavage site scoring matrices (CSSM). The efficiency of PEPS in predicting putative caspase 3, cathepsin B and cathepsin L cleavage sites is demonstrated in comparison to established algorithms. Mortalin, a member of the heat shock protein family HSP70, was identified by PEPS as a putative cathepsin L substrate. Comparative proteome analyses of cathepsin L-deficient and wild-type mouse fibroblasts showed that mortalin is enriched in the absence of cathepsin L. These results indicate that CSSM/PEPS can correctly predict relevant peptidase substrates.
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paper_title: Neuro-fuzzy Prediction of Biological Activity and Rule Extraction for HIV-1 Protease Inhibitors

paper_content:

A fuzzy neural network (FNN) and multiple linear regression (MLR) were used to predict biological activities of 26 newly designed HIV-1 protease potential inhibitory compounds. Molecular descriptors of 151 known inhibitors were used to train and test the FNN and to develop MLR models. The predictive ability of these two models was investigated and compared. We found the predictive ability of the FNN to be generally superior to that of MLR. The fuzzy IF/THEN rules were extracted from the trained network. These rules map chemical structure descriptors to predicted inhibitory values. The obtained rules can be used to analyze the influence of descriptors. Our results indicate that FNN and fuzzy IF/THEN rules are powerful modeling tools for QSAR studies.
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paper_title: Pripper: prediction of caspase cleavage sites from whole proteomes

paper_content:

BackgroundCaspases are a family of proteases that have central functions in programmed cell death (apoptosis) and inflammation. Caspases mediate their effects through aspartate-specific cleavage of their target proteins, and at present almost 400 caspase substrates are known. There are several methods developed to predict caspase cleavage sites from individual proteins, but currently none of them can be used to predict caspase cleavage sites from multiple proteins or entire proteomes, or to use several classifiers in combination. The possibility to create a database from predicted caspase cleavage products for the whole genome could significantly aid in identifying novel caspase targets from tandem mass spectrometry based proteomic experiments.ResultsThree different pattern recognition classifiers were developed for predicting caspase cleavage sites from protein sequences. Evaluation of the classifiers with quality measures indicated that all of the three classifiers performed well in predicting caspase cleavage sites, and when combining different classifiers the accuracy increased further. A new tool, Pripper, was developed to utilize the classifiers and predict the caspase cut sites from an arbitrary number of input sequences. A database was constructed with the developed tool, and it was used to identify caspase target proteins from tandem mass spectrometry data from two different proteomic experiments. Both known caspase cleavage products as well as novel cleavage products were identified using the database demonstrating the usefulness of the tool. Pripper is not restricted to predicting only caspase cut sites, but it gives the possibility to scan protein sequences for any given motif(s) and predict cut sites once a suitable cut site prediction model for any other protease has been developed. Pripper is freely available and can be downloaded from http://users.utu.fi/mijopi/Pripper.ConclusionsWe have developed Pripper, a tool for reading an arbitrary number of proteins in FASTA format, predicting their caspase cleavage sites and outputting the cleaved sequences to a new FASTA format sequence file. We show that Pripper is a valuable tool in identifying novel caspase target proteins from modern proteomics experiments.
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paper_title: C4.5: Programs for Machine Learning

paper_content:

From the Publisher: ::: Classifier systems play a major role in machine learning and knowledge-based systems, and Ross Quinlan's work on ID3 and C4.5 is widely acknowledged to have made some of the most significant contributions to their development. This book is a complete guide to the C4.5 system as implemented in C for the UNIX environment. It contains a comprehensive guide to the system's use , the source code (about 8,800 lines), and implementation notes. The source code and sample datasets are also available on a 3.5-inch floppy diskette for a Sun workstation. ::: ::: C4.5 starts with large sets of cases belonging to known classes. The cases, described by any mixture of nominal and numeric properties, are scrutinized for patterns that allow the classes to be reliably discriminated. These patterns are then expressed as models, in the form of decision trees or sets of if-then rules, that can be used to classify new cases, with emphasis on making the models understandable as well as accurate. The system has been applied successfully to tasks involving tens of thousands of cases described by hundreds of properties. The book starts from simple core learning methods and shows how they can be elaborated and extended to deal with typical problems such as missing data and over hitting. Advantages and disadvantages of the C4.5 approach are discussed and illustrated with several case studies. ::: ::: This book and software should be of interest to developers of classification-based intelligent systems and to students in machine learning and expert systems courses.
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paper_title: Bio-support vector machines for computational proteomics

paper_content:

MOTIVATION ::: One of the most important issues in computational proteomics is to produce a prediction model for the classification or annotation of biological function of novel protein sequences. In order to improve the prediction accuracy, much attention has been paid to the improvement of the performance of the algorithms used, few is for solving the fundamental issue, namely, amino acid encoding as most existing pattern recognition algorithms are unable to recognize amino acids in protein sequences. Importantly, the most commonly used amino acid encoding method has the flaw that leads to large computational cost and recognition bias. ::: ::: ::: RESULTS ::: By replacing kernel functions of support vector machines (SVMs) with amino acid similarity measurement matrices, we have modified SVMs, a new type of pattern recognition algorithm for analysing protein sequences, particularly for proteolytic cleavage site prediction. We refer to the modified SVMs as bio-support vector machine. When applied to the prediction of HIV protease cleavage sites, the new method has shown a remarkable advantage in reducing the model complexity and enhancing the model robustness.
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paper_title: Specificity rule discovery in HIV-1 protease cleavage site analysis

paper_content:

Several machine learning algorithms have recently been applied to modeling the specificity of HIV-1 protease. The problem is challenging because of the three issues as follows: (1) datasets with high dimensionality and small number of samples could misguide classification modeling and its interpretation; (2) symbolic interpretation is desirable because it provides us insight to the specificity in the form of human-understandable rules, and thus helps us to design effective HIV inhibitors; (3) the interpretation should take into account complexity or dependency between positions in sequences. Therefore, it is neccessary to investigate multivariate and feature-selective methods to model the specificity and to extract rules from the model. We have tested extensively various machine learning methods, and we have found that the combination of neural networks and decompositional approach can generate a set of effective rules. By validation to experimental results for the HIV-1 protease, the specificity rules outperform the ones generated by frequency-based, univariate or black-box methods.
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paper_title: A profile hidden Markov model for signal peptides generated by HMMER

paper_content:

SUMMARY ::: Although the HMMER package is widely used to produce profile hidden Markov models (profile HMMs) for protein domains, it has been difficult to create a profile HMM for signal peptides. Here we describe an approach for building a complex model of eukaryotic signal peptides by the standard HMMER package. Signal peptide prediction with this model gives a 95.6% sensitivity and 95.7% specificity. ::: ::: ::: AVAILABILITY ::: The profile HMM for signal peptides, data sets, and the scripts for analyzing data are available for non-commercial use at http://share.gene.com/.
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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: Profile Hidden Markov Models

paper_content:

Summary : The recent literature on profile hidden Markov model (profile HMM) methods and software is reviewed. Profile HMMs turn a multiple sequence alignment into a position-specific scoring system suitable for searching databases for remotely homologous sequences. Profile HMM analyses complement standard pairwise comparison methods for large-scale sequence analysis. Several software implementations and two large libraries of profile HMMs of common protein domains are available, HMM methods performed comparably to threading methods in the CASP2 structure prediction exercise. Contact: eddy@genetics.wustl.edu.
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paper_title: Efficient and Accurate Lp-Norm Multiple Kernel Learning

paper_content:

Learning linear combinations of multiple kernels is an appealing strategy when the right choice of features is unknown. Previous approaches to multiple kernel learning (MKL) promote sparse kernel combinations to support interpretability. Unfortunately, l1-norm MKL is hardly observed to outperform trivial baselines in practical applications. To allow for robust kernel mixtures, we generalize MKL to arbitrary lp-norms. We devise new insights on the connection between several existing MKL formulations and develop two efficient interleaved optimization strategies for arbitrary p > 1. Empirically, we demonstrate that the interleaved optimization strategies are much faster compared to the traditionally used wrapper approaches. Finally, we apply lp-norm MKL to real-world problems from computational biology, showing that non-sparse MKL achieves accuracies that go beyond the state-of-the-art.
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paper_title: Calpain Cleavage Prediction Using Multiple Kernel Learning

paper_content:

Calpain, an intracellular Ca²⁺-dependent cysteine protease, is known to play a role in a wide range of metabolic pathways through limited proteolysis of its substrates. However, only a limited number of these substrates are currently known, with the exact mechanism of substrate recognition and cleavage by calpain still largely unknown. While previous research has successfully applied standard machine-learning algorithms to accurately predict substrate cleavage by other similar types of proteases, their approach does not extend well to calpain, possibly due to its particular mode of proteolytic action and limited amount of experimental data. Through the use of Multiple Kernel Learning, a recent extension to the classic Support Vector Machine framework, we were able to train complex models based on rich, heterogeneous feature sets, leading to significantly improved prediction quality (6% over highest AUC score produced by state-of-the-art methods). In addition to producing a stronger machine-learning model for the prediction of calpain cleavage, we were able to highlight the importance and role of each feature of substrate sequences in defining specificity: primary sequence, secondary structure and solvent accessibility. Most notably, we showed there existed significant specificity differences across calpain sub-types, despite previous assumption to the contrary. Prediction accuracy was further successfully validated using, as an unbiased test set, mutated sequences of calpastatin (endogenous inhibitor of calpain) modified to no longer block calpain's proteolytic action. An online implementation of our prediction tool is available at http://calpain.org.
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paper_title: Choosing Multiple Parameters for Support Vector Machines

paper_content:

The problem of automatically tuning multiple parameters for pattern recognition Support Vector Machines (SVMs) is considered. This is done by minimizing some estimates of the generalization error of SVMs using a gradient descent algorithm over the set of parameters. Usual methods for choosing parameters, based on exhaustive search become intractable as soon as the number of parameters exceeds two. Some experimental results assess the feasibility of our approach for a large number of parameters (more than 100) and demonstrate an improvement of generalization performance.
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paper_title: Cascleave: towards more accurate prediction of caspase substrate cleavage sites

paper_content:

MOTIVATION ::: The caspase family of cysteine proteases play essential roles in key biological processes such as programmed cell death, differentiation, proliferation, necrosis and inflammation. The complete repertoire of caspase substrates remains to be fully characterized. Accordingly, systematic computational screening studies of caspase substrate cleavage sites may provide insight into the substrate specificity of caspases and further facilitating the discovery of putative novel substrates. ::: ::: ::: RESULTS ::: In this article we develop an approach (termed Cascleave) to predict both classical (i.e. following a P(1) Asp) and non-typical caspase cleavage sites. When using local sequence-derived profiles, Cascleave successfully predicted 82.2% of the known substrate cleavage sites, with a Matthews correlation coefficient (MCC) of 0.667. We found that prediction performance could be further improved by incorporating information such as predicted solvent accessibility and whether a cleavage sequence lies in a region that is most likely natively unstructured. Novel bi-profile Bayesian signatures were found to significantly improve the prediction performance and yielded the best performance with an overall accuracy of 87.6% and a MCC of 0.747, which is higher accuracy than published methods that essentially rely on amino acid sequence alone. It is anticipated that Cascleave will be a powerful tool for predicting novel substrate cleavage sites of caspases and shedding new insights on the unknown caspase-substrate interactivity relationship. ::: ::: ::: AVAILABILITY ::: http://sunflower.kuicr.kyoto-u.ac.jp/ approximately sjn/Cascleave/ ::: ::: ::: CONTACT ::: jiangning.song@med.monash.edu.au; takutsu@kuicr.kyoto-u.ac.jp; james; whisstock@med.monash.edu.au ::: ::: ::: SUPPLEMENTARY INFORMATION ::: Supplementary data are available at Bioinformatics online.
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paper_title: Prediction of protease substrates using sequence and structure features

paper_content:

Motivation:Granzyme B (GrB) and caspases cleave specific protein substrates to induce apoptosis in virally infected and neoplastic cells. While substrates for both types of proteases have been determined experimentally, there are many more yet to be discovered in humans and other metazoans. Here, we present a bioinformatics method based on support vector machine (SVM) learning that identifies sequence and structural features important for protease recognition of substrate peptides and then uses these features to predict novel substrates. Our approach can act as a convenient hypothesis generator, guiding future experiments by high-confidence identification of peptide-protein partners. ::: ::: Results:The method is benchmarked on the known substrates of both protease types, including our literature-curated GrB substrate set (GrBah). On these benchmark sets, the method outperforms a number of other methods that consider sequence only, predicting at a 0.87 true positive rate (TPR) and a 0.13 false positive rate (FPR) for caspase substrates, and a 0.79 TPR and a 0.21 FPR for GrB substrates. The method is then applied to ~25 000 proteins in the human proteome to generate a ranked list of predicted substrates of each protease type. Two of these predictions, AIF-1 and SMN1, were selected for further experimental analysis, and each was validated as a GrB substrate. ::: ::: Availability: All predictions for both protease types are publically available at http://salilab.org/peptide. A web server is at the same site that allows a user to train new SVM models to make predictions for any protein that recognizes specific oligopeptide ligands. ::: ::: Contact:craik@cgl.ucsf.edu; sali@salilab.org ::: ::: Supplementary information:Supplementary data are available at Bioinformatics online
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paper_title: Kernel methods for predicting protein-protein interactions

paper_content:

Motivation: Despite advances in high-throughput methods for discovering protein--protein interactions, the interaction networks of even well-studied model organisms are sketchy at best, highlighting the continued need for computational methods to help direct experimentalists in the search for novel interactions. ::: ::: Results: We present a kernel method for predicting protein--protein interactions using a combination of data sources, including protein sequences, Gene Ontology annotations, local properties of the network, and homologous interactions in other species. Whereas protein kernels proposed in the literature provide a similarity between single proteins, prediction of interactions requires a kernel between pairs of proteins. We propose a pairwise kernel that converts a kernel between single proteins into a kernel between pairs of proteins, and we illustrate the kernel's effectiveness in conjunction with a support vector machine classifier. Furthermore, we obtain improved performance by combining several sequence-based kernels based on k-mer frequency, motif and domain content and by further augmenting the pairwise sequence kernel with features that are based on other sources of data. ::: ::: We apply our method to predict physical interactions in yeast using data from the BIND database. At a false positive rate of 1% the classifier retrieves close to 80% of a set of trusted interactions. We thus demonstrate the ability of our method to make accurate predictions despite the sizeable fraction of false positives that are known to exist in interaction databases. ::: ::: Availability: The classification experiments were performed using PyML available at http://pyml.sourceforge.net. Data are available at: http://noble.gs.washington.edu/proj/sppi ::: ::: Contact: asa@gs.washington.edu
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paper_title: Regularization Strategies and Empirical Bayesian Learning for MKL

paper_content:

Multiple kernel learning (MKL), structured sparsity, and multi-task learning have recently received considerable attention. In this paper, we show how different MKL algorithms can be understood as applications of either regularization on the kernel weights or block-norm-based regularization, which is more common in structured sparsity and multi-task learning. We show that these two regularization strategies can be systematically mapped to each other through a concave conjugate operation. When the kernel-weight-based regularizer is separable into components, we can naturally consider a generative probabilistic model behind MKL. Based on this model, we propose learning algorithms for the kernel weights through the maximization of marginal likelihood. We show through numerical experiments that $\ell_2$-norm MKL and Elastic-net MKL achieve comparable accuracy to uniform kernel combination. Although uniform kernel combination might be preferable from its simplicity, $\ell_2$-norm MKL and Elastic-net MKL can learn the usefulness of the information sources represented as kernels. In particular, Elastic-net MKL achieves sparsity in the kernel weights.
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paper_title: SVM-based prediction of caspase substrate cleavage sites

paper_content:

BackgroundCaspases belong to a class of cysteine proteases which function as critical effectors in apoptosis and inflammation by cleaving substrates immediately after unique sites. Prediction of such cleavage sites will complement structural and functional studies on substrates cleavage as well as discovery of new substrates. Recently, different computational methods have been developed to predict the cleavage sites of caspase substrates with varying degrees of success. As the support vector machines (SVM) algorithm has been shown to be useful in several biological classification problems, we have implemented an SVM-based method to investigate its applicability to this domain.ResultsA set of unique caspase substrates cleavage sites were obtained from literature and used for evaluating the SVM method. Datasets containing (i) the tetrapeptide cleavage sites, (ii) the tetrapeptide cleavage sites, augmented by two adjacent residues, P1' and P2' amino acids and (iii) the tetrapeptide cleavage sites with ten additional upstream and downstream flanking sequences (where available) were tested. The SVM method achieved an accuracy ranging from 81.25% to 97.92% on independent test sets. The SVM method successfully predicted the cleavage of a novel caspase substrate and its mutants.ConclusionThis study presents an SVM approach for predicting caspase substrate cleavage sites based on the cleavage sites and the downstream and upstream flanking sequences. The method shows an improvement over existing methods and may be useful for predicting hitherto undiscovered cleavage sites.
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paper_title: Bio-support vector machines for computational proteomics

paper_content:

MOTIVATION ::: One of the most important issues in computational proteomics is to produce a prediction model for the classification or annotation of biological function of novel protein sequences. In order to improve the prediction accuracy, much attention has been paid to the improvement of the performance of the algorithms used, few is for solving the fundamental issue, namely, amino acid encoding as most existing pattern recognition algorithms are unable to recognize amino acids in protein sequences. Importantly, the most commonly used amino acid encoding method has the flaw that leads to large computational cost and recognition bias. ::: ::: ::: RESULTS ::: By replacing kernel functions of support vector machines (SVMs) with amino acid similarity measurement matrices, we have modified SVMs, a new type of pattern recognition algorithm for analysing protein sequences, particularly for proteolytic cleavage site prediction. We refer to the modified SVMs as bio-support vector machine. When applied to the prediction of HIV protease cleavage sites, the new method has shown a remarkable advantage in reducing the model complexity and enhancing the model robustness.
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paper_title: The Spectrum Kernel: A String Kernel for SVM Protein Classification

paper_content:

We introduce a new sequence-similarity kernel, the spectrum kernel, for use with support vector machines (SVMs) in a discriminative approach to the protein classification problem. Our kernel is conceptually simple and efficient to compute and, in experiments on the SCOP database, performs well in comparison with state-of-the-art methods for homology detection. Moreover, our method produces an SVM classifier that allows linear time classification of test sequences. Our experiments provide evidence that string-based kernels, in conjunction with SVMs, could offer a viable and computationally efficient alternative to other methods of protein classification and homology detection.
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paper_title: A statistical framework for genomic data fusion

paper_content:

Motivation: During the past decade, the new focus on genomics has highlighted a particular challenge: to integrate the different views of the genome that are provided by various types of experimental data. ::: ::: Results: This paper describes a computational framework for integrating and drawing inferences from a collection of genome-wide measurements. Each dataset is represented via a kernel function, which defines generalized similarity relationships between pairs of entities, such as genes or proteins. The kernel representation is both flexible and efficient, and can be applied to many different types of data. Furthermore, kernel functions derived from different types of data can be combined in a straightforward fashion. Recent advances in the theory of kernel methods have provided efficient algorithms to perform such combinations in a way that minimizes a statistical loss function. These methods exploit semidefinite programming techniques to reduce the problem of finding optimizing kernel combinations to a convex optimization problem. Computational experiments performed using yeast genome-wide datasets, including amino acid sequences, hydropathy profiles, gene expression data and known protein--protein interactions, demonstrate the utility of this approach. A statistical learning algorithm trained from all of these data to recognize particular classes of proteins---membrane proteins and ribosomal proteins---performs significantly better than the same algorithm trained on any single type of data. ::: ::: Availability: Supplementary data at http://noble.gs.washington.edu/proj/sdp-svm
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paper_title: Pripper: prediction of caspase cleavage sites from whole proteomes

paper_content:

BackgroundCaspases are a family of proteases that have central functions in programmed cell death (apoptosis) and inflammation. Caspases mediate their effects through aspartate-specific cleavage of their target proteins, and at present almost 400 caspase substrates are known. There are several methods developed to predict caspase cleavage sites from individual proteins, but currently none of them can be used to predict caspase cleavage sites from multiple proteins or entire proteomes, or to use several classifiers in combination. The possibility to create a database from predicted caspase cleavage products for the whole genome could significantly aid in identifying novel caspase targets from tandem mass spectrometry based proteomic experiments.ResultsThree different pattern recognition classifiers were developed for predicting caspase cleavage sites from protein sequences. Evaluation of the classifiers with quality measures indicated that all of the three classifiers performed well in predicting caspase cleavage sites, and when combining different classifiers the accuracy increased further. A new tool, Pripper, was developed to utilize the classifiers and predict the caspase cut sites from an arbitrary number of input sequences. A database was constructed with the developed tool, and it was used to identify caspase target proteins from tandem mass spectrometry data from two different proteomic experiments. Both known caspase cleavage products as well as novel cleavage products were identified using the database demonstrating the usefulness of the tool. Pripper is not restricted to predicting only caspase cut sites, but it gives the possibility to scan protein sequences for any given motif(s) and predict cut sites once a suitable cut site prediction model for any other protease has been developed. Pripper is freely available and can be downloaded from http://users.utu.fi/mijopi/Pripper.ConclusionsWe have developed Pripper, a tool for reading an arbitrary number of proteins in FASTA format, predicting their caspase cleavage sites and outputting the cleaved sequences to a new FASTA format sequence file. We show that Pripper is a valuable tool in identifying novel caspase target proteins from modern proteomics experiments.
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paper_title: GPS-CCD: A Novel Computational Program for the Prediction of Calpain Cleavage Sites

paper_content:

As one of the most essential post-translational modifications (PTMs) of proteins, proteolysis, especially calpain-mediated cleavage, plays an important role in many biological processes, including cell death/apoptosis, cytoskeletal remodeling, and the cell cycle. Experimental identification of calpain targets with bona fide cleavage sites is fundamental for dissecting the molecular mechanisms and biological roles of calpain cleavage. In contrast to time-consuming and labor-intensive experimental approaches, computational prediction of calpain cleavage sites might more cheaply and readily provide useful information for further experimental investigation. In this work, we constructed a novel software package of GPS-CCD (Calpain Cleavage Detector) for the prediction of calpain cleavage sites, with an accuracy of 89.98%, sensitivity of 60.87% and specificity of 90.07%. With this software, we annotated potential calpain cleavage sites for hundreds of calpain substrates, for which the exact cleavage sites had not been previously determined. In this regard, GPS-CCD 1.0 is considered to be a useful tool for experimentalists. The online service and local packages of GPS-CCD 1.0 were implemented in JAVA and are freely available at: http://ccd.biocuckoo.org/.
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paper_title: Why Neural Networks Should Not Be Used for HIV-1 Protease Cleavage Site Prediction

paper_content:

Summary: Several papers have been published where nonlinear machine learning algorithms, e.g. artificial neural networks, support vector machines and decision trees, have been used to model the specificity of the HIV-1 protease and extract specificity rules. We show that the dataset used in these studies is linearly separable and that it is a misuse of nonlinear classifiers to apply them to this problem. The best solution on this dataset is achieved using a linear classifier like the simple perceptron or the linear support vector machine, and it is straightforward to extract rules from these linear models. We identify key residues in peptides that are efficiently cleaved by the HIV-1 protease and list the most prominent rules, relating them to experimental results for the HIV-1 protease. ::: ::: Motivation: Understanding HIV-1 protease specificity is important when designing HIV inhibitors and several different machine learning algorithms have been applied to the problem. However, little progress has been made in understanding the specificity because nonlinear and overly complex models have been used. ::: ::: Results: We show that the problem is much easier than what has previously been reported and that linear classifiers like the simple perceptron or linear support vector machines are at least as good predictors as nonlinear algorithms. We also show how sets of specificity rules can be generated from the resulting linear classifiers. ::: ::: Availability: The datasets used are available at http://www.hh.se/staff/bioinf/
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paper_title: Calpain Cleavage Prediction Using Multiple Kernel Learning

paper_content:

Calpain, an intracellular Ca²⁺-dependent cysteine protease, is known to play a role in a wide range of metabolic pathways through limited proteolysis of its substrates. However, only a limited number of these substrates are currently known, with the exact mechanism of substrate recognition and cleavage by calpain still largely unknown. While previous research has successfully applied standard machine-learning algorithms to accurately predict substrate cleavage by other similar types of proteases, their approach does not extend well to calpain, possibly due to its particular mode of proteolytic action and limited amount of experimental data. Through the use of Multiple Kernel Learning, a recent extension to the classic Support Vector Machine framework, we were able to train complex models based on rich, heterogeneous feature sets, leading to significantly improved prediction quality (6% over highest AUC score produced by state-of-the-art methods). In addition to producing a stronger machine-learning model for the prediction of calpain cleavage, we were able to highlight the importance and role of each feature of substrate sequences in defining specificity: primary sequence, secondary structure and solvent accessibility. Most notably, we showed there existed significant specificity differences across calpain sub-types, despite previous assumption to the contrary. Prediction accuracy was further successfully validated using, as an unbiased test set, mutated sequences of calpastatin (endogenous inhibitor of calpain) modified to no longer block calpain's proteolytic action. An online implementation of our prediction tool is available at http://calpain.org.
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paper_title: Pripper: prediction of caspase cleavage sites from whole proteomes

paper_content:

BackgroundCaspases are a family of proteases that have central functions in programmed cell death (apoptosis) and inflammation. Caspases mediate their effects through aspartate-specific cleavage of their target proteins, and at present almost 400 caspase substrates are known. There are several methods developed to predict caspase cleavage sites from individual proteins, but currently none of them can be used to predict caspase cleavage sites from multiple proteins or entire proteomes, or to use several classifiers in combination. The possibility to create a database from predicted caspase cleavage products for the whole genome could significantly aid in identifying novel caspase targets from tandem mass spectrometry based proteomic experiments.ResultsThree different pattern recognition classifiers were developed for predicting caspase cleavage sites from protein sequences. Evaluation of the classifiers with quality measures indicated that all of the three classifiers performed well in predicting caspase cleavage sites, and when combining different classifiers the accuracy increased further. A new tool, Pripper, was developed to utilize the classifiers and predict the caspase cut sites from an arbitrary number of input sequences. A database was constructed with the developed tool, and it was used to identify caspase target proteins from tandem mass spectrometry data from two different proteomic experiments. Both known caspase cleavage products as well as novel cleavage products were identified using the database demonstrating the usefulness of the tool. Pripper is not restricted to predicting only caspase cut sites, but it gives the possibility to scan protein sequences for any given motif(s) and predict cut sites once a suitable cut site prediction model for any other protease has been developed. Pripper is freely available and can be downloaded from http://users.utu.fi/mijopi/Pripper.ConclusionsWe have developed Pripper, a tool for reading an arbitrary number of proteins in FASTA format, predicting their caspase cleavage sites and outputting the cleaved sequences to a new FASTA format sequence file. We show that Pripper is a valuable tool in identifying novel caspase target proteins from modern proteomics experiments.
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paper_title: Cascleave: towards more accurate prediction of caspase substrate cleavage sites

paper_content:

MOTIVATION ::: The caspase family of cysteine proteases play essential roles in key biological processes such as programmed cell death, differentiation, proliferation, necrosis and inflammation. The complete repertoire of caspase substrates remains to be fully characterized. Accordingly, systematic computational screening studies of caspase substrate cleavage sites may provide insight into the substrate specificity of caspases and further facilitating the discovery of putative novel substrates. ::: ::: ::: RESULTS ::: In this article we develop an approach (termed Cascleave) to predict both classical (i.e. following a P(1) Asp) and non-typical caspase cleavage sites. When using local sequence-derived profiles, Cascleave successfully predicted 82.2% of the known substrate cleavage sites, with a Matthews correlation coefficient (MCC) of 0.667. We found that prediction performance could be further improved by incorporating information such as predicted solvent accessibility and whether a cleavage sequence lies in a region that is most likely natively unstructured. Novel bi-profile Bayesian signatures were found to significantly improve the prediction performance and yielded the best performance with an overall accuracy of 87.6% and a MCC of 0.747, which is higher accuracy than published methods that essentially rely on amino acid sequence alone. It is anticipated that Cascleave will be a powerful tool for predicting novel substrate cleavage sites of caspases and shedding new insights on the unknown caspase-substrate interactivity relationship. ::: ::: ::: AVAILABILITY ::: http://sunflower.kuicr.kyoto-u.ac.jp/ approximately sjn/Cascleave/ ::: ::: ::: CONTACT ::: jiangning.song@med.monash.edu.au; takutsu@kuicr.kyoto-u.ac.jp; james; whisstock@med.monash.edu.au ::: ::: ::: SUPPLEMENTARY INFORMATION ::: Supplementary data are available at Bioinformatics online.
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paper_title: Artificial neural network model for predicting HIV protease cleavage sites in protein

paper_content:

Knowledge of the polyprotein cleavage sites by HIV protease will refine our understanding of its specificity, and the information thus acquired will be useful for designing specific and efficient HIV protease inhibitors. The search for inhibitors of HIV protease will be greatly expedited if one can find an accurate, robust, and rapid method for predicting the cleavage sites in proteins by HIV protease. In this paper, Kohonen's self-organization model, which uses typical artificial neural networks, is applied to predict the cleavability of oligopeptides by proteases with multiple and extended specificity subsites. We selected HIV-1 protease as the subject of study. We chose 299 oligopeptides for the training set, and another 63 oligopeptides for the test set. Because of its high rate of correct prediction (58/63 = 92.06%) and stronger fault-tolerant ability, the neural network method should be a useful technique for finding effective inhibitors of HIV protease, which is one of the targets in designing potential drugs against AIDS. The principle of the artificial neural network method can also be applied to analyzing the specificity of any multisubsite enzyme.
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paper_title: Penalized feature selection and classification in bioinformatics

paper_content:

In bioinformatics studies, supervised classification with high-dimensional input variables is frequently encountered. Examples routinely arise in genomic, epigenetic and proteomic studies. Feature selection can be employed along with classifier construction to avoid over-fitting, to generate more reliable classifier and to provide more insights into the underlying causal relationships. In this article, we provide a review of several recently developed penalized feature selection and classification techniques—which belong to the family of embedded feature selection methods—for bioinformatics studies with high-dimensional input. Classification objective functions, penalty functions and computational algorithms are discussed. Our goal is to make interested researchers aware of these feature selection and classification methods that are applicable to high-dimensional bioinformatics data.
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paper_title: Assessment of Proteasomal Cleavage Probabilities from Kinetic Analysis of Time-dependent Product Formation

paper_content:

Proteasomes are multicatalytic cellular protease complexes that degrade intracellular proteins into smaller peptides. Proteasomal in vitro digests have revealed that the various peptide bonds of a given substrate are cleaved in a highly selective manner. Regarding the key role of proteasomes as the main supplier of antigenic peptides for MHC class I-mediated antigen presentation, it is important to know to what extent these preferences for specific peptide bonds may vary among proteasomes of different cellular origin and of different subunit composition. Here, we quantify such cleavage rates by means of a kinetic proteasome model that relates the time-dependent changes of the amount of any generated peptide to the rates with which this peptide can be either generated from longer precursor peptides or degraded into smaller successor peptides. Numerical values for these rates are estimated by minimizing the distance between simulated and measured time-courses. The proposed method is applied to kinetic data obtained by combining HPLC fractionation and mass spectrometry (MS) to trace the degradation of two model peptides (pp89-25mer and LLO-27mer) by either the constitutive (T2) or immunoproteasome (T2.27). To convert the intensity of the MS signals into the respective peptide amounts, we use two methods leading to similar results: experimental calibration curves and theoretically determined linear scaling functions based on a novel approach using mass conservation rules. Comparison of the cleavage probabilities and procession rates obtained for the two types of proteasomes reveals that the striking differences between the time-dependent peptide profiles can be accounted for mainly by a generally higher turnover rate of the immunoproteasome. For the pp89-25mer, there is no significant change of the cleavage probabilities for any of the ten observed cleavage sites. For the LLO-27mer, there appears to be a significant change in the cleavage probabilities for four of the nine observed cleavage sites when switching from the constitutive to the immunoproteasome.
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<format>
Title: A Review of Statistical Methods for Prediction of Proteolytic Cleavage

Section 1: INTRODUCTION
Description 1: Write about the importance of understanding proteolytic cleavage in biology, the role of computational methods in advancing this understanding, and an overview of the review's focus on different prediction techniques.

Section 2: BACKGROUND Proteolytic enzymes
Description 2: Discuss the various proteolytic enzymes, their mechanisms, substrate specificity, and historical context of computational prediction models.

Section 3: Data availability
Description 3: Address the importance of data availability for supervised learning methods and provide information on relevant databases such as MEROPS, CutDB, and CaMPDB.

Section 4: GENERAL APPROACH Problem setting
Description 4: Define the general problem of predicting proteolytic cleavage, including problem formulation and common decision problems in prediction.

Section 5: Practical issues in supervised learning
Description 5: Discuss critical issues in applying supervised learning to cleavage prediction, including choice of features, vector encoding, data set imbalance, and evaluation metrics.

Section 6: PREDICTION METHODS Position-based matrices
Description 6: Elaborate on the use of position-specific scoring matrices (PSSM) and related methods to predict cleavage sites.

Section 7: Artificial neural networks
Description 7: Provide an overview of the application of artificial neural networks (ANN) in cleavage prediction, their strengths, weaknesses, and historical significance.

Section 8: Decision trees and rule extraction methods
Description 8: Describe the use of decision trees and rule extraction methods in cleavage prediction, focusing on their interpretability.

Section 9: Hidden Markov models
Description 9: Explain the implementation and benefits of hidden Markov models (HMM) for sequence-related predictions and their application in proteolytic cleavage prediction.

Section 10: Kernel methods
Description 10: Discuss the advantages of using support vector machines (SVM) and kernel methods in cleavage prediction, including various kernel types and their applications.

Section 11: Other methods
Description 11: Mention additional methods that have been applied to cleavage prediction, such as hierarchical clustering and integrative voting algorithms.

Section 12: DISCUSSION
Description 12: Summarize the relative performance of different methods, comparing evaluation metrics, and discussing practical considerations like computational complexity.

Section 13: FUTURE DIRECTIONS
Description 13: Outline new developments and potential future research directions in proteolytic cleavage prediction, including the application of sparse learning techniques and probabilistic models.

Section 14: Key Points
Description 14: Provide key takeaway messages about choosing appropriate algorithms based on protease type, balancing accuracy, readability, flexibility, and presenting illustrative examples of implementations.
</format>
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