A survey on prediction of specificity-determining sites in proteins
---
paper_title: In silico discovery of enzyme-substrate specificity-determining residue clusters.

paper_content:

The binding between an enzyme and its substrate is highly specific, despite the fact that many different enzymes show significant sequence and structure similarity. There must be, then, substrate specificity-determining residues that enable different enzymes to recognize their unique substrates. We reason that a coordinated, not independent, action of both conserved and non-conserved residues determine enzymatic activity and specificity. Here, we present a surface patch ranking (SPR) method for in silico discovery of substrate specificity-determining residue clusters by exploring both sequence conservation and correlated mutations. As case studies we apply SPR to several highly homologous enzymatic protein pairs, such as guanylyl versus adenylyl cyclases, lactate versus malate dehydrogenases, and trypsin versus chymotrypsin. Without using experimental data, we predict several single and multi-residue clusters that are consistent with previous mutagenesis experimental results. Most single-residue clusters are directly involved in enzyme–substrate interactions, whereas multi-residue clusters are vital for domain–domain and regulator–enzyme interactions, indicating their complementary role in specificity determination. These results demonstrate that SPR may help the selection of target residues for mutagenesis experiments and, thus, focus rational drug design, protein engineering, and functional annotation to the relevant regions of a protein.
---
paper_title: High Resolution Crystal Structures of Human Rab5a and Five Mutants with Substitutions in the Catalytically Important Phosphate-Binding Loop

paper_content:

Abstract GTPase domain crystal structures of Rab5a wild type and five variants with mutations in the phosphate-binding loop are reported here at resolutions up to 1.5 A. Of particular interest, the A30P mutant was crystallized in complexes with GDP, GDP+AlF3, and authentic GTP, respectively. The other variant crystals were obtained in complexes with a non-hydrolyzable GTP analog, GppNHp. All structures were solved in the same crystal form, providing an unusual opportunity to compare structures of small GTPases with different catalytic rates. The A30P mutant exhibits dramatically reduced GTPase activity and forms a GTP-bound complex stable enough for crystallographic analysis. Importantly, the A30P structure with bound GDP plus AlF3has been solved in the absence of a GTPase-activating protein, and it may resemble that of a transition state intermediate. Conformational changes are observed between the GTP-bound form and the transition state intermediate, mainly in the switch II region containing the catalytic Gln79 residue and independent of A30P mutation-induced local alterations in the P-loop. The structures suggest an important catalytic role for a P-loop backbone amide group, which is eliminated in the A30P mutant, and support the notion that the transition state of GTPase-mediated GTP hydrolysis is of considerable dissociative character.
---
paper_title: Prediction of functional specificity determinants from protein sequences using log-likelihood ratios

paper_content:

Motivation: A number of methods have been developed to predict functional specificity determinants in protein families based on sequence information. Most of these methods rely on pre-defined functional subgroups. Manual subgroup definition is difficult because of the limited number of experimentally characterized subfamilies with differing specificity, while automatic subgroup partitioning using computational tools is a non-trivial task and does not always yield ideal results. ::: ::: Results: We propose a new approach SPEL (specificity positions by evolutionary likelihood) to detect positions that are likely to be functional specificity determinants. SPEL, which does not require subgroup definition, takes a multiple sequence alignment of a protein family as the only input, and assigns a P-value to every position in the alignment. Positions with low P-values are likely to be important for functional specificity. An evolutionary tree is reconstructed during the calculation, and P-value estimation is based on a random model that involves evolutionary simulations. Evolutionary log-likelihood is chosen as a measure of amino acid distribution at a position. To illustrate the performance of the method, we carried out a detailed analysis of two protein families (LacI/PurR and G protein α subunit), and compared our method with two existing methods (evolutionary trace and mutual information based). All three methods were also compared on a set of protein families with known ligand-bound structures. ::: ::: Availability: SPEL is freely available for non-commercial use. Its pre-compiled versions for several platforms and alignments used in this work are available at ftp://iole.swmed.edu/pub/SPEL/ ::: ::: Contact:grishin@chop.swmed.edu. ::: ::: Supplementary information: Supplementary materials are available at ftp:/iole.swmed.edu/pub/SPEL/
---
paper_title: SPEER-SERVER: a web server for prediction of protein specificity determining sites

paper_content:

Sites that show specific conservation patterns within subsets of proteins in a protein family are likely to be involved in the development of functional specificity. These sites, generally termed specificity determining sites (SDS), might play a crucial role in binding to a specific substrate or proteins. Identification of SDS through experimental techniques is a slow, difficult and tedious job. Hence, it is very important to develop efficient computational methods that can more expediently identify SDS. Herein, we present Specificity prediction using amino acids’ Properties, Entropy and Evolution Rate (SPEER)-SERVER, a web server that predicts SDS by analyzing quantitative measures of the conservation patterns of protein sites based on their physico-chemical properties and the heterogeneity of evolutionary changes between and within the protein subfamilies. This web server provides an improved representation of results, adds useful input and output options and integrates a wide range of analysis and data visualization tools when compared with the original standalone version of the SPEER algorithm. Extensive benchmarking finds that SPEER-SERVER exhibits sensitivity and precision performance that, on average, meets or exceeds that of other currently available methods. SPEER-SERVER is available at http://www.hpppi.iicb.res.in/ss/.
---
paper_title: The Rab GTPase family

paper_content:

SUMMARY ::: The Rab family is part of the Ras superfamily of small GTPases. There are at least 60 Rab genes in the human genome, and a number of Rab GTPases are conserved from yeast to humans. The different Rab GTPases are localized to the cytosolic face of specific intracellular membranes, where they function as regulators of distinct steps in membrane traffic pathways. In the GTP-bound form, the Rab GTPases recruit specific sets of effector proteins onto membranes. Through their effectors, Rab GTPases regulate vesicle formation, actin- and tubulin-dependent vesicle movement, and membrane fusion.
---
paper_title: Using evolutionary information to find specificity-determining and co-evolving residues.

paper_content:

Intricate networks of protein interactions rely on the ability of a protein to recognize its targets: other proteins, ligands, and sites on DNA and RNA. To recognize other molecules, it was suggested that a protein uses a small set of specificity-determining residues (SDRs). How can one find these residues in proteins and distinguish them from other functionally important amino acids? A number of bioinformatics methods to predict SDRs have been developed in recent years. These methods use genomic information and multiple sequence alignments to identify positions exhibiting a specific pattern of conservation and variability. The challenge is to delineate the evolutionary pattern of SDRs from that of the active site residues and the residues responsible for formation of the protein's structure. The phylogenetic history of a protein family makes such analysis particularly hard. Here we present two methods for finding the SDRs and the co-evolving residues (CERs) in proteins. We use a Monte Carlo approach for statistical inference, allowing us to reveal specific evolutionary patterns of SDRs and CERs. We apply these methods to study specific recognition in the bacterial two-component system and in the class Ia aminoacyl-tRNA synthetases. Our results agree well with structural information and the experimental analyses of these systems. Our results point at the complex and distinct patterns characteristic of the evolution of specificity in these systems.
---
paper_title: An evolutionary trace method defines binding surfaces common to protein families

paper_content:

X-ray or NMR structures of proteins are often derived without their ligands, and even when the structure of a full complex is available, the area of contact that is functionally and energetically significant may be a specialized subset of the geometric interface deduced from the spatial proximity between ligands. Thus, even after a structure is solved, it remains a major theoretical and experimental goal to localize protein functional interfaces and understand the role of their constituent residues. The evolutionary trace method is a systematic, transparent and novel predictive technique that identifies active sites and functional interfaces in proteins with known structure. It is based on the extraction of functionally important residues from sequence conservation patterns in homologous proteins, and on their mapping onto the protein surface to generate clusters identifying functional interfaces. The SH2 and SH3 modular signaling domains and the DNA binding domain of the nuclear hormone receptors provide tests for the accuracy and validity of our method. In each case, the evolutionary trace delineates the functional epitope and identifies residues critical to binding specificity. Based on mutational evolutionary analysis and on the structural homology of protein families, this simple and versatile approach should help focus site-directed mutagenesis studies of structure-function relationships in macromolecules, as well as studies of specificity in molecular recognition. More generally, it provides an evolutionary perspective for judging the functional or structural role of each residue in protein structure.
---
paper_title: RB: Analysis and prediction of functional sub-types from protein sequence alignments

paper_content:

The increasing number and diversity of protein sequence families requires new methods to define and predict details regarding function. Here, we present a method for analysis and prediction of functional sub-types from multiple protein sequence alignments. Given an alignment and set of proteins grouped into sub-types according to some definition of function, such as enzymatic specificity, the method identifies positions that are indicative of functional differences by comparison of sub-type specific sequence profiles, and analysis of positional entropy in the alignment. Alignment positions with significantly high positional relative entropy correlate with those known to be involved in defining sub-types for nucleotidyl cyclases, protein kinases, lactate/malate dehydrogenases and trypsin-like serine proteases. We highlight new positions for these proteins that suggest additional experiments to elucidate the basis of specificity. The method is also able to predict sub-type for unclassified sequences. We assess several variations on a prediction method, and compare them to simple sequence comparisons. For assessment, we remove close homologues to the sequence for which a prediction is to be made (by a sequence identity above a threshold). This simulates situations where a protein is known to belong to a protein family, but is not a close relative of another protein of known sub-type. Considering the four families above, and a sequence identity threshold of 30 %, our best method gives an accuracy of 96 % compared to 80 % obtained for sequence similarity and 74 % for BLAST. We describe the derivation of a set of sub-type groupings derived from an automated parsing of alignments from PFAM and the SWISSPROT database, and use this to perform a large-scale assessment. The best method gives an average accuracy of 94 % compared to 68 % for sequence similarity and 79 % for BLAST. We discuss implications for experimental design, genome annotation and the prediction of protein function and protein intra-residue distances.
---
paper_title: Automatic methods for predicting functionally important residues

paper_content:

Sequence analysis is often the first guide for the prediction of residues in a protein family that may have functional significance. A few methods have been proposed which use the division of protein families into subfamilies in the search for those positions that could have some functional significance for the whole family, but at the same time which exhibit the specificity of each subfamily (“Tree-determinant residues”). However, there are still many unsolved questions like the best division of a protein family into subfamilies, or the accurate detection of sequence variation patterns characteristic of different subfamilies. Here we present a systematic study in a significant number of protein families, testing the statistical meaning of the Tree-determinant residues predicted by three different methods that represent the range of available approaches. The first method takes as a starting point a phylogenetic representation of a protein family and, following the principle of Relative Entropy from Information Theory, automatically searches for the optimal division of the family into subfamilies. The second method looks for positions whose mutational behavior is reminiscent of the mutational behavior of the full-length proteins, by directly comparing the corresponding distance matrices. The third method is an automation of the analysis of distribution of sequences and amino acid positions in the corresponding multidimensional spaces using a vector-based principal component analysis. These three methods have been tested on two non-redundant lists of protein families: one composed by proteins that bind a variety of ligand groups, and the other composed by proteins with annotated functionally relevant sites. In most cases, the residues predicted by the three methods show a clear tendency to be close to bound ligands of biological relevance and to those amino acids described as participants in key aspects of protein function. These three automatic methods provide a wide range of possibilities for biologists to analyze their families of interest, in a similar way to the one presented here for the family of proteins related with ras-p21.
---
paper_title: Predicting functional divergence in protein evolution by site-specific rate shifts.

paper_content:

Most modern tools that analyze protein evolution allow individual sites to mutate at constant rates over the history of the protein family. However, Walter Fitch observed in the 1970s that, if a protein changes its function, the mutability of individual sites might also change. This observation is captured in the "non-homogeneous gamma model", which extracts functional information from gene families by examining the different rates at which individual sites evolve. This model has recently been coupled with structural and molecular biology to identify sites that are likely to be involved in changing function within the gene family. Applying this to multiple gene families highlights the widespread divergence of functional behavior among proteins to generate paralogs and orthologs.
---
paper_title: Supervised multivariate analysis of sequence groups to identify specificity determining residues

paper_content:

BackgroundProteins that evolve from a common ancestor can change functionality over time, and it is important to be able identify residues that cause this change. In this paper we show how a supervised multivariate statistical method, Between Group Analysis (BGA), can be used to identify these residues from families of proteins with different substrate specifities using multiple sequence alignments.ResultsWe demonstrate the usefulness of this method on three different test cases. Two of these test cases, the Lactate/Malate dehydrogenase family and Nucleotidyl Cyclases, consist of two functional groups. The other family, Serine Proteases consists of three groups. BGA was used to analyse and visualise these three families using two different encoding schemes for the amino acids.ConclusionThis overall combination of methods in this paper is powerful and flexible while being computationally very fast and simple. BGA is especially useful because it can be used to analyse any number of functional classes. In the examples we used in this paper, we have only used 2 or 3 classes for demonstration purposes but any number can be used and visualised.
---
paper_title: Protein sequence alignments: a strategy for the hierarchical analysis of residue conservation

paper_content:

An algorithm is described for the systematic characterization of the physico-chemical properties seen at each position in a multiple protein sequence alignment. The new algorithm allows questions important in the design of mutagenesis experiments to be quickly answered since positions in the alignment that show unusual or interesting residue substitution patterns may be rapidly identified. The strategy is based on a flexible set-based description of amino acid properties, which is used to define the conservation between any group of amino acids. Sequences in the alignment are gathered into subgroups on the basis of sequence similarity, functional, evolutionary or other criteria. All pairs of subgroups are then compared to highlight positions that confer the unique features of each subgroup. The algorithm is encoded in the computer program AMAS (Analysis of Multiply Aligned Sequences) which provides a textual summary of the analysis and an annotated (boxed, shaded and/or coloured) multiple sequence alignment. The algorithm is illustrated by application to an alignment of 67 SH2 domains where patterns of conserved hydrophobic residues that constitute the protein core are highlighted. The analysis of charge conservation across annexin domains identifies the locations at which conserved charges change sign. The algorithm simplifies the analysis of multiple sequence data by condensing the mass of information present, and thus allows the rapid identification of substitutions of structural and functional importance.
---
paper_title: Functional specificity lies within the properties and evolutionary changes of amino acids.

paper_content:

The rapid increase in the amount of protein sequence data has created a need for automated identification of sites that determine functional specificity among related subfamilies of proteins. A significant fraction of subfamily specific sites are only marginally conserved, which makes it extremely challenging to detect those amino acid changes that lead to functional diversification. To address this critical problem we developed a method named SPEER (specificity prediction using amino acids' properties, entropy and evolution rate) to distinguish specificity determining sites from others. SPEER encodes the conservation patterns of amino acid types using their physico-chemical properties and the heterogeneity of evolutionary changes between and within the subfamilies. To test the method, we compiled a test set containing 13 protein families with known specificity determining sites. Extensive benchmarking by comparing the performance of SPEER with other specificity site prediction algorithms has shown that it performs better in predicting several categories of subfamily specific sites.
---
paper_title: Characterization and prediction of residues determining protein functional specificity

paper_content:

Motivation: Within a homologous protein family, proteins may be grouped into subtypes that share specific functions that are not common to the entire family. Often, the amino acids present in a small number of sequence positions determine each protein's particular function-al specificity. Knowledge of these specificity determining positions (SDPs) aids in protein function prediction, drug design and experimental analysis. A number of sequence-based computational methods have been introduced for identifying SDPs; however, their further development and evaluation have been hindered by the limited number of known experimentally determined SDPs. ::: ::: Results: We combine several bioinformatics resources to automate a process, typically undertaken manually, to build a dataset of SDPs. The resulting large dataset, which consists of SDPs in enzymes, enables us to characterize SDPs in terms of their physicochemical and evolution-ary properties. It also facilitates the large-scale evaluation of sequence-based SDP prediction methods. We present a simple sequence-based SDP prediction method, GroupSim, and show that, surprisingly, it is competitive with a representative set of current methods. We also describe ConsWin, a heuristic that considers sequence conservation of neighboring amino acids, and demonstrate that it improves the performance of all methods tested on our large dataset of enzyme SDPs. ::: ::: Availability: Datasets and GroupSim code are available online at http://compbio.cs.princeton.edu/specificity/ ::: ::: Contact: msingh@cs.princeton.edu ::: ::: Supplementary information:Supplementary data are available at Bioinformatics online.
---
paper_title: Maximum-Likelihood Approach for Gene Family Evolution Under Functional Divergence

paper_content:

According to the observed alignment pattern (i.e., amino acid configuration), we studied two basic types of functional divergence of a protein family. Type I functional divergence after gene duplication results in altered functional constraints (i.e., different evolutionary rate) between duplicate genes, whereas type II results in no altered functional constraints but radical change in amino acid property between them (e.g., charge, hydrophobicity, etc.). Two statistical approaches, i.e., the subtree likelihood and the whole-tree likelihood, were developed for estimating the coefficients of (type I or type II) functional divergence. Numerical algorithms for obtaining maximum-likelihood estimates are also provided. Moreover, a posterior-based site-specific profile is implemented to predict critical amino acid residues that are responsible for type I and/or type II functional divergence after gene duplication. We compared the current likelihood with a fast method developed previously by examples; both show similar results. For handling altered functional constraints (type I functional divergence) in the large gene family with many member genes (clusters), which appears to be a normal case in postgenomics, the subtree likelihood provides a solution that is computationally feasible and robust against the uncertainty of the phylogeny. The cost of this feasibility is the approximation when frequencies of amino acids are very skewed. The potential bias and correction are discussed.
---
paper_title: Separation of phylogenetic and functional associations in biological sequences by using the parametric bootstrap.

paper_content:

Quantitative analyses of biological sequences generally proceed under the assumption that individual DNA or protein sequence elements vary independently. However, this assumption is not biologically realistic because sequence elements often vary in a concerted manner resulting from common ancestry and structural or functional constraints. We calculated intersite associations among aligned protein sequences by using mutual information. To discriminate associations resulting from common ancestry from those resulting from structural or functional constraints, we used a parametric bootstrap algorithm to construct replicate data sets. These data are expected to have intersite associations resulting solely from phylogeny. By comparing the distribution of our association statistic for the replicate data against that calculated for empirical data, we were able to assign a probability that two sites covaried resulting from structural or functional constraint rather than phylogeny. We tested our method by using an alignment of 237 basic helix-loop-helix (bHLH) protein domains. Comparison of our results against a solved three-dimensional structure confirmed the identification of several sites important to function and structure of the bHLH domain. This analytical procedure has broad utility as a first step in the identification of sites that are important to biological macromolecular structure and function when a solved structure is unavailable.
---
paper_title: Sequence analysis Multi-RELIEF : a method to recognize specificity determining residues from multiple sequence alignments using a Machine-Learning approach for feature weighting

paper_content:

Motivation: Identification of residues that account for protein function specificity is crucial, not only for understanding the nature of functional specificity, but also for protein engineering experiments aimed at switching the specificity of an enzyme, regulator or transporter. Available algorithms generally use multiple sequence alignments to identify residue positions conserved within subfamilies but divergent in between. However, many biological examples show a much subtler picture than simple intra-group conservation versus inter-group divergence. ::: ::: Results: We present multi-RELIEF, a novel approach for identifying specificity residues that is based on RELIEF, a state-of-the-art Machine-Learning technique for feature weighting. It estimates the expected ‘local’ functional specificity of residues from an alignment divided in multiple classes. Optionally, 3D structure information is exploited by increasing the weight of residues that have high-weight neighbors. Using ROC curves over a large body of experimental reference data, we show that (a) multi-RELIEF identifies specificity residues for the seven test sets used, (b) incorporating structural information improves prediction for specificity of interaction with small molecules and (c) comparison of multi-RELIEF with four other state-of-the-art algorithms indicates its robustness and best overall performance. ::: ::: Availability: A web-server implementation of multi-RELIEF is available at www.ibi.vu.nl/programs/multirelief. Matlab source code of the algorithm and data sets are available on request for academic use. ::: ::: Contact: [email protected] ::: ::: Supplementary information: Supplemenmtary data are available at Bioinformatics online
---
paper_title: Multi-Harmony: detecting functional specificity from sequence alignment

paper_content:

Many protein families contain sub-families with functional specialization, such as binding different ligands or being involved in different protein-protein interactions. A small number of amino acids generally determine functional specificity. The identification of these residues can aid the understanding of protein function and help finding targets for experimental analysis. Here, we present multi-Harmony, an interactive web sever for detecting sub-type-specific sites in proteins starting from a multiple sequence alignment. Combining our Sequence Harmony (SH) and multi-Relief (mR) methods in one web server allows simultaneous analysis and comparison of specificity residues; furthermore, both methods have been significantly improved and extended. SH has been extended to cope with more than two sub-groups. mR has been changed from a sampling implementation to a deterministic one, making it more consistent and user friendly. For both methods Z-scores are reported. The multi-Harmony web server produces a dynamic output page, which includes interactive connections to the Jalview and Jmol applets, thereby allowing interactive analysis of the results. Multi-Harmony is available at http://www.ibi.vu.nl/ programs/shmrwww.
---
paper_title: Emerging methods in protein co-evolution

paper_content:

Functional interactions between proteins and within proteins results in co-evolutionary signatures in amino acid sequences that serve as clues to various forms of interdependence. This Review discusses the principles and distinctions of the large range of computational tools to analyse protein co-evolution and the biological insight that they are providing.
---
paper_title: Classification of protein families and detection of the determinant residues with an improved self-organizing map

paper_content:

Using a SOM (self-organizing map) we can classify sequences within a protein family into subgroups that generally correspond to biological subcategories. These maps tend to show sequence similarity as proximity in the map. Combining maps generated at different levels of resolution, the structure of relations in protein families can be captured that could not otherwise be represented in a single map. The underlying representation of maps enables us to retrieve characteristic sequence patterns for individual subgroups of sequences. Such patterns tend to correspond to functionally important regions. We present a modified SOM algorithm that includes a convergence test that dynamically controls the learning parameters to adapt them to the learning set instead of being fixed and externally optimized by trial and error. Given the variability of protein family size and distribution, the addition of this features is necessary. The method is successfully tested with a number of families. The rab family of small GTPases is used to illustrate the performance of the method.
---
paper_title: The structure of human neuronal Rab6B in the active and inactive form

paper_content:

The Rab small G-protein family plays important roles in eukaryotes as regulators of vesicle traffic. In Rab proteins, the hydrolysis of GTP to GDP is coupled with association with and dissociation from membranes. Conformational changes related to their different nucleotide states determine their effector specificity. The crystal structure of human neuronal Rab6B was solved in its 'inactive' (with bound MgGDP) and 'active' (MgGTP gamma S-bound) forms to 2.3 and 1.8 angstrom, respectively. Both crystallized in space group P2(1)2(1)2(1), with similar unit-cell parameters, allowing the comparison of both structures without packing artifacts. Conformational changes between the inactive GDP and active GTP-like state are observed mainly in the switch I and switch II regions, confirming their role as a molecular switch. Compared with other Rab proteins, additional changes are observed in the Rab6 subfamily-specific RabSF3 region that might contribute to the specificity of Rab6 for its different effector proteins.
---
paper_title: [29] Identification of functional residues and secondary structure from protein multiple sequence alignment

paper_content:

Publisher Summary This chapter describes a strategy for the hierarchical analysis of residue conservation and the identification of functional residues and secondary structure from protein multiple sequence alignment. Hierarchical methods of alignment cope with large numbers of sequences and give reasonably accurate alignments. Having generated the alignment, the problem is to find out what it can tell us about the protein family. Interpretation of alignments can be, particularly difficult when there are large numbers of sequences to examine. The method allows the residue-specific similarities and differences in physicochemical properties among groups of sequences to be identified quickly. The method also highlights conserved positions across a complete alignment and, thus, can help to identify patterns characteristic of regular secondary structures. The chapter also discusses a procedure for applying these patterns in secondary structure prediction and evaluates their predictive power in six blind secondary-structure predictions.
---
paper_title: Phylogeny-independent detection of functional residues

paper_content:

Motivation: Current projects for the massive characterization of proteomes are generating protein sequences and structures with unknown function. The difficulty of experimentally determining functionally important sites calls for the development of computational methods. The first techniques, based on the search for fully conserved positions in multiple sequence alignments (MSAs), were followed by methods for locating family-dependent conserved positions. These rely on the functional classification implicit in the alignment for locating these positions related with functional specificity. The next obvious step, still scarcely explored, is to detect these positions using a functional classification different from the one implicit in the sequence relationships between the proteins. Here, we present two new methods for locating functional positions which can incorporate an arbitrary external functional classification which may or may not coincide with the one implicit in the MSA. The Xdet method is able to use a functional classification with an associated hierarchy or similarity between functions to locate positions related to that classification. The MCdet method uses multivariate statistical analysis to locate positions responsible for each one of the functions within a multifunctional family. ::: ::: Results: We applied the methods to different cases, illustrating scenarios where there is a disagreement between the functional and the phylogenetic relationships, and demonstrated their usefulness for the phylogeny-independent prediction of functional positions. ::: ::: Availability: All computer programs and datasets used in this work are available from the authors for academic use. ::: ::: Contact: [email protected] ::: ::: Supplementary information: Supplementary data are available at http://pdg.cnb.uam.es/pazos/Xdet_MCdet_Add/
---
paper_title: Predicting functional residues of protein sequence alignments as a feature selection task

paper_content:

Determining which residues within a multiple alignment of protein sequences are most responsible for protein function is a difficult and important task in bioinformatics. Here, we show that this task is an application of the standard Feature Selection (FS) problem. We show the comparison of standard FS techniques with more specialised algorithms on a range of data sets backed by experimental evidence, and find that some standard algorithms perform as well as specialised ones. We also discuss how considering the discriminating power of combinations of residue positions, rather than the power of each position individually, has the potential to improve the performance of such algorithms.
---
paper_title: Using multiple interdependency to separate functional from phylogenetic correlations in protein alignments

paper_content:

Motivation: Multiple sequence alignments of homologous proteins are useful for inferring their phylogenetic history and to reveal functionally important regions in the proteins. Functional constraints may lead to co-variation of two or more amino acids in the sequence, such that a substitution at one site is accompanied by compensatory substitutions at another site. It is not sufficient to find the statistical correlations between sites in the alignment because these may be the result of several undetermined causes. In particular, phylogenetic clustering will lead to many strong correlations. Result: Ap rocedure is developed to detect statistical correlations stemming from functional interaction by removing the strong phylogenetic signal that leads to the correlations of each site with many others in the sequence. Our method relies upon the accuracy of the alignment but it does not require any assumptions about the phylogeny or the substitution process. The effectiveness of the method wa sv erified using computer simulations and then applied to predict functional interactions between amino acids in the Pfam database of alignments. Availability: The program and supplementary figures tables are available from the site http://www.uhnres.utoronto.
---
paper_title: Spial: analysis of subtype-specific features in multiple sequence alignments of proteins

paper_content:

Motivation: Spial (Specificity in alignments) is a tool for the comparative analysis of two alignments of evolutionarily related sequences that differ in their function, such as two receptor subtypes. It highlights functionally important residues that are either specific to one of the two alignments or conserved across both alignments. It permits visualization of this information in three complementary ways: by colour-coding alignment positions, by sequence logos and optionally by colour-coding the residues of a protein structure provided by the user. This can aid in the detection of residues that are involved in the subtype-specific interaction with a ligand, other proteins or nucleic acids. Spial may also be used to detect residues that may be post-translationally modified in one of the two sets of sequences.
---
paper_title: An evolutionary trace method defines binding surfaces common to protein families

paper_content:

X-ray or NMR structures of proteins are often derived without their ligands, and even when the structure of a full complex is available, the area of contact that is functionally and energetically significant may be a specialized subset of the geometric interface deduced from the spatial proximity between ligands. Thus, even after a structure is solved, it remains a major theoretical and experimental goal to localize protein functional interfaces and understand the role of their constituent residues. The evolutionary trace method is a systematic, transparent and novel predictive technique that identifies active sites and functional interfaces in proteins with known structure. It is based on the extraction of functionally important residues from sequence conservation patterns in homologous proteins, and on their mapping onto the protein surface to generate clusters identifying functional interfaces. The SH2 and SH3 modular signaling domains and the DNA binding domain of the nuclear hormone receptors provide tests for the accuracy and validity of our method. In each case, the evolutionary trace delineates the functional epitope and identifies residues critical to binding specificity. Based on mutational evolutionary analysis and on the structural homology of protein families, this simple and versatile approach should help focus site-directed mutagenesis studies of structure-function relationships in macromolecules, as well as studies of specificity in molecular recognition. More generally, it provides an evolutionary perspective for judging the functional or structural role of each residue in protein structure.
---
paper_title: RB: Analysis and prediction of functional sub-types from protein sequence alignments

paper_content:

The increasing number and diversity of protein sequence families requires new methods to define and predict details regarding function. Here, we present a method for analysis and prediction of functional sub-types from multiple protein sequence alignments. Given an alignment and set of proteins grouped into sub-types according to some definition of function, such as enzymatic specificity, the method identifies positions that are indicative of functional differences by comparison of sub-type specific sequence profiles, and analysis of positional entropy in the alignment. Alignment positions with significantly high positional relative entropy correlate with those known to be involved in defining sub-types for nucleotidyl cyclases, protein kinases, lactate/malate dehydrogenases and trypsin-like serine proteases. We highlight new positions for these proteins that suggest additional experiments to elucidate the basis of specificity. The method is also able to predict sub-type for unclassified sequences. We assess several variations on a prediction method, and compare them to simple sequence comparisons. For assessment, we remove close homologues to the sequence for which a prediction is to be made (by a sequence identity above a threshold). This simulates situations where a protein is known to belong to a protein family, but is not a close relative of another protein of known sub-type. Considering the four families above, and a sequence identity threshold of 30 %, our best method gives an accuracy of 96 % compared to 80 % obtained for sequence similarity and 74 % for BLAST. We describe the derivation of a set of sub-type groupings derived from an automated parsing of alignments from PFAM and the SWISSPROT database, and use this to perform a large-scale assessment. The best method gives an average accuracy of 94 % compared to 68 % for sequence similarity and 79 % for BLAST. We discuss implications for experimental design, genome annotation and the prediction of protein function and protein intra-residue distances.
---
paper_title: Automatic methods for predicting functionally important residues

paper_content:

Sequence analysis is often the first guide for the prediction of residues in a protein family that may have functional significance. A few methods have been proposed which use the division of protein families into subfamilies in the search for those positions that could have some functional significance for the whole family, but at the same time which exhibit the specificity of each subfamily (“Tree-determinant residues”). However, there are still many unsolved questions like the best division of a protein family into subfamilies, or the accurate detection of sequence variation patterns characteristic of different subfamilies. Here we present a systematic study in a significant number of protein families, testing the statistical meaning of the Tree-determinant residues predicted by three different methods that represent the range of available approaches. The first method takes as a starting point a phylogenetic representation of a protein family and, following the principle of Relative Entropy from Information Theory, automatically searches for the optimal division of the family into subfamilies. The second method looks for positions whose mutational behavior is reminiscent of the mutational behavior of the full-length proteins, by directly comparing the corresponding distance matrices. The third method is an automation of the analysis of distribution of sequences and amino acid positions in the corresponding multidimensional spaces using a vector-based principal component analysis. These three methods have been tested on two non-redundant lists of protein families: one composed by proteins that bind a variety of ligand groups, and the other composed by proteins with annotated functionally relevant sites. In most cases, the residues predicted by the three methods show a clear tendency to be close to bound ligands of biological relevance and to those amino acids described as participants in key aspects of protein function. These three automatic methods provide a wide range of possibilities for biologists to analyze their families of interest, in a similar way to the one presented here for the family of proteins related with ras-p21.
---
paper_title: Predicting functional divergence in protein evolution by site-specific rate shifts.

paper_content:

Most modern tools that analyze protein evolution allow individual sites to mutate at constant rates over the history of the protein family. However, Walter Fitch observed in the 1970s that, if a protein changes its function, the mutability of individual sites might also change. This observation is captured in the "non-homogeneous gamma model", which extracts functional information from gene families by examining the different rates at which individual sites evolve. This model has recently been coupled with structural and molecular biology to identify sites that are likely to be involved in changing function within the gene family. Applying this to multiple gene families highlights the widespread divergence of functional behavior among proteins to generate paralogs and orthologs.
---
paper_title: Protein sequence alignments: a strategy for the hierarchical analysis of residue conservation

paper_content:

An algorithm is described for the systematic characterization of the physico-chemical properties seen at each position in a multiple protein sequence alignment. The new algorithm allows questions important in the design of mutagenesis experiments to be quickly answered since positions in the alignment that show unusual or interesting residue substitution patterns may be rapidly identified. The strategy is based on a flexible set-based description of amino acid properties, which is used to define the conservation between any group of amino acids. Sequences in the alignment are gathered into subgroups on the basis of sequence similarity, functional, evolutionary or other criteria. All pairs of subgroups are then compared to highlight positions that confer the unique features of each subgroup. The algorithm is encoded in the computer program AMAS (Analysis of Multiply Aligned Sequences) which provides a textual summary of the analysis and an annotated (boxed, shaded and/or coloured) multiple sequence alignment. The algorithm is illustrated by application to an alignment of 67 SH2 domains where patterns of conserved hydrophobic residues that constitute the protein core are highlighted. The analysis of charge conservation across annexin domains identifies the locations at which conserved charges change sign. The algorithm simplifies the analysis of multiple sequence data by condensing the mass of information present, and thus allows the rapid identification of substitutions of structural and functional importance.
---
paper_title: Functional specificity lies within the properties and evolutionary changes of amino acids.

paper_content:

The rapid increase in the amount of protein sequence data has created a need for automated identification of sites that determine functional specificity among related subfamilies of proteins. A significant fraction of subfamily specific sites are only marginally conserved, which makes it extremely challenging to detect those amino acid changes that lead to functional diversification. To address this critical problem we developed a method named SPEER (specificity prediction using amino acids' properties, entropy and evolution rate) to distinguish specificity determining sites from others. SPEER encodes the conservation patterns of amino acid types using their physico-chemical properties and the heterogeneity of evolutionary changes between and within the subfamilies. To test the method, we compiled a test set containing 13 protein families with known specificity determining sites. Extensive benchmarking by comparing the performance of SPEER with other specificity site prediction algorithms has shown that it performs better in predicting several categories of subfamily specific sites.
---
paper_title: [29] Identification of functional residues and secondary structure from protein multiple sequence alignment

paper_content:

Publisher Summary This chapter describes a strategy for the hierarchical analysis of residue conservation and the identification of functional residues and secondary structure from protein multiple sequence alignment. Hierarchical methods of alignment cope with large numbers of sequences and give reasonably accurate alignments. Having generated the alignment, the problem is to find out what it can tell us about the protein family. Interpretation of alignments can be, particularly difficult when there are large numbers of sequences to examine. The method allows the residue-specific similarities and differences in physicochemical properties among groups of sequences to be identified quickly. The method also highlights conserved positions across a complete alignment and, thus, can help to identify patterns characteristic of regular secondary structures. The chapter also discusses a procedure for applying these patterns in secondary structure prediction and evaluates their predictive power in six blind secondary-structure predictions.
---
paper_title: Evolutionary trace report_maker: a new type of service for comparative analysis of proteins

paper_content:

Summary: Evolutionary trace report_maker offers a new type of service for researchers investigating the function of novel proteins. It pools, from different sources, information about protein sequence, structure and elementary annotation, and to that background superimposes inference about the evolutionary behavior of individual residues, using real-valued evolutionary trace method. As its only input it takes a Protein Data Bank identifier or UniProt accession number, and returns a human-readable document in PDF format, supplemented by the original data needed to reproduce the results quoted in the report. ::: ::: Availability: Evolutionary trace reports are freely available for academic users at http://mammoth.bcm.tmc.edu/report-maker ::: ::: Contact: {imihalek,ires,lichtarge}@bcm.tmc.edu
---
paper_title: Evolutionary Trace Annotation Server: automated enzyme function prediction in protein structures using 3D templates

paper_content:

Summary:The Evolutionary Trace Annotation (ETA) Server predicts enzymatic activity. ETA starts with a structure of unknown function, such as those from structural genomics, and with no prior knowledge of its mechanism uses the phylogenetic Evolutionary Trace (ET) method to extract key functional residues and propose a function-associated 3D motif, called a 3D template. ETA then searches previously annotated structures for geometric template matches that suggest molecular and thus functional mimicry. In order to maximize the predictive value of these matches, ETA next applies distinctive specificity filters—evolutionary similarity, function plurality and match reciprocity. In large scale controls on enzymes, prediction coverage is 43% but the positive predictive value rises to 92%, thus minimizing false annotations. Users may modify any search parameter, including the template. ETA thus expands the ET suite for protein structure annotation, and can contribute to the annotation efforts of metaservers. ::: ::: Availability:The ETA Server is a web application available at http://mammoth.bcm.tmc.edu/eta/. ::: ::: Contact: [email protected]
---
paper_title: Identification of subfamily-specific sites based on active sites modeling and clustering

paper_content:

Motivation: Current computational approaches to function prediction are mostly based on protein sequence classification and transfer of annotation from known proteins to their closest homologous sequences relying on the orthology concept of function conservation. This approach suffers a major weakness: annotation reliability depends on global sequence similarity to known proteins and is poorly efficient for enzyme superfamilies that catalyze different reactions. Structural biology offers a different strategy to overcome the problem of annotation by adding information about protein 3D structures. This information can be used to identify amino acids located in active sites, focusing on detection of functional polymorphisms residues in an enzyme superfamily. Structural genomics programs are providing more and more novel protein structures at a high-throughput rate. However, there is still a huge gap between the number of sequences and available structures. Computational methods, such as homology modeling provides reliable approaches to bridge this gap and could be a new precise tool to annotate protein functions. ::: ::: Results: Here, we present Active Sites Modeling and Clustering (ASMC) method, a novel unsupervised method to classify sequences using structural information of protein pockets. ASMC combines homology modeling of family members, structural alignment of modeled active sites and a subsequent hierarchical conceptual classification. Comparison of profiles obtained from computed clusters allows the identification of residues correlated to subfamily function divergence, called specificity determining positions. ASMC method has been validated on a benchmark of 42 Pfam families for which previous resolved holo-structures were available. ASMC was also applied to several families containing known protein structures and comprehensive functional annotations. We will discuss how ASMC improves annotation and understanding of protein families functions by giving some specific illustrative examples on nucleotidyl cyclases, protein kinases and serine proteases. ::: ::: Availability: http://www.genoscope.fr/ASMC/. ::: ::: Contact:raquelcm@dcc.ufmg.br; kbastard@genoscope.cns.fr; artigue@genoscope.cns.fr ::: ::: Supplementary information:Supplementary data are available at Bioinformatics online.
---
paper_title: In silico discovery of enzyme-substrate specificity-determining residue clusters.

paper_content:

The binding between an enzyme and its substrate is highly specific, despite the fact that many different enzymes show significant sequence and structure similarity. There must be, then, substrate specificity-determining residues that enable different enzymes to recognize their unique substrates. We reason that a coordinated, not independent, action of both conserved and non-conserved residues determine enzymatic activity and specificity. Here, we present a surface patch ranking (SPR) method for in silico discovery of substrate specificity-determining residue clusters by exploring both sequence conservation and correlated mutations. As case studies we apply SPR to several highly homologous enzymatic protein pairs, such as guanylyl versus adenylyl cyclases, lactate versus malate dehydrogenases, and trypsin versus chymotrypsin. Without using experimental data, we predict several single and multi-residue clusters that are consistent with previous mutagenesis experimental results. Most single-residue clusters are directly involved in enzyme–substrate interactions, whereas multi-residue clusters are vital for domain–domain and regulator–enzyme interactions, indicating their complementary role in specificity determination. These results demonstrate that SPR may help the selection of target residues for mutagenesis experiments and, thus, focus rational drug design, protein engineering, and functional annotation to the relevant regions of a protein.
---
paper_title: Prediction of functional specificity determinants from protein sequences using log-likelihood ratios

paper_content:

Motivation: A number of methods have been developed to predict functional specificity determinants in protein families based on sequence information. Most of these methods rely on pre-defined functional subgroups. Manual subgroup definition is difficult because of the limited number of experimentally characterized subfamilies with differing specificity, while automatic subgroup partitioning using computational tools is a non-trivial task and does not always yield ideal results. ::: ::: Results: We propose a new approach SPEL (specificity positions by evolutionary likelihood) to detect positions that are likely to be functional specificity determinants. SPEL, which does not require subgroup definition, takes a multiple sequence alignment of a protein family as the only input, and assigns a P-value to every position in the alignment. Positions with low P-values are likely to be important for functional specificity. An evolutionary tree is reconstructed during the calculation, and P-value estimation is based on a random model that involves evolutionary simulations. Evolutionary log-likelihood is chosen as a measure of amino acid distribution at a position. To illustrate the performance of the method, we carried out a detailed analysis of two protein families (LacI/PurR and G protein α subunit), and compared our method with two existing methods (evolutionary trace and mutual information based). All three methods were also compared on a set of protein families with known ligand-bound structures. ::: ::: Availability: SPEL is freely available for non-commercial use. Its pre-compiled versions for several platforms and alignments used in this work are available at ftp://iole.swmed.edu/pub/SPEL/ ::: ::: Contact:grishin@chop.swmed.edu. ::: ::: Supplementary information: Supplementary materials are available at ftp:/iole.swmed.edu/pub/SPEL/
---
paper_title: SPEER-SERVER: a web server for prediction of protein specificity determining sites

paper_content:

Sites that show specific conservation patterns within subsets of proteins in a protein family are likely to be involved in the development of functional specificity. These sites, generally termed specificity determining sites (SDS), might play a crucial role in binding to a specific substrate or proteins. Identification of SDS through experimental techniques is a slow, difficult and tedious job. Hence, it is very important to develop efficient computational methods that can more expediently identify SDS. Herein, we present Specificity prediction using amino acids’ Properties, Entropy and Evolution Rate (SPEER)-SERVER, a web server that predicts SDS by analyzing quantitative measures of the conservation patterns of protein sites based on their physico-chemical properties and the heterogeneity of evolutionary changes between and within the protein subfamilies. This web server provides an improved representation of results, adds useful input and output options and integrates a wide range of analysis and data visualization tools when compared with the original standalone version of the SPEER algorithm. Extensive benchmarking finds that SPEER-SERVER exhibits sensitivity and precision performance that, on average, meets or exceeds that of other currently available methods. SPEER-SERVER is available at http://www.hpppi.iicb.res.in/ss/.
---
paper_title: Protein interactions and ligand binding: From protein subfamilies to functional specificity

paper_content:

The divergence accumulated during the evolution of protein families translates into their internal organization as subfamilies, and it is directly reflected in the characteristic patterns of differentially conserved residues. These specifically conserved positions in protein subfamilies are known as "specificity determining positions" (SDPs). Previous studies have limited their analysis to the study of the relationship between these positions and ligand-binding specificity, demonstrating significant yet limited predictive capacity. We have systematically extended this observation to include the role of differential protein interactions in the segregation of protein subfamilies and explored in detail the structural distribution of SDPs at protein interfaces. Our results show the extensive influence of protein interactions in the evolution of protein families and the widespread association of SDPs with protein interfaces. The combined analysis of SDPs in interfaces and ligand-binding sites provides a more complete picture of the organization of protein families, constituting the necessary framework for a large scale analysis of the evolution of protein function.
---
paper_title: Large-Scale Prediction of Function Shift in Protein Families with a Focus on Enzymatic Function

paper_content:

Protein function shift can be predicted from sequence comparisons, either using positive selection signals or evolutionary rate estimation. None of the methods have been validated on large datasets, however. Here we investigate existing and novel methods for protein function shift prediction, and benchmark the accuracy against a large dataset of proteins with known enzymatic functions. Function change was predicted between subfamilies by identifying two kinds of sites in a multiple sequence alignment: Conservation-Shifting Sites (CSS), which are conserved in two subfamilies using two different amino acid types, and Rate-Shifting Sites (RSS), which have different evolutionary rates in two subfamilies. CSS were predicted by a new entropy-based method, and RSS using the Rate-Shift program. In principle, the more CSS and RSS between two subfamilies, the more likely a function shift between them. A test dataset was built by extracting subfamilies from Pfam with different EC numbers that belong to the same domain family. Subfamilies were generated automatically using a phylogenetic tree-based program, BETE. The dataset comprised 997 subfamily pairs with four or more members per subfamily. We observed a significant increase in CSS and RSS for subfamily comparisons with different EC numbers compared to cases with same EC numbers. The discrimination was better using RSS than CSS, and was more pronounced for larger families. Combining RSS and CSS by discriminant analysis improved classification accuracy to 71%. The method was applied to the Pfam database and the results are available at http://FunShift.cgb.ki.se. A closer examination of some superfamily comparisons showed that single EC numbers sometimes embody distinct functional classes. Hence, the measured accuracy of function shift is underestimated.
---
paper_title: Prediction of enzyme function based on 3D templates of evolutionarily important amino acids

paper_content:

Background ::: Structural genomics projects such as the Protein Structure Initiative (PSI) yield many new structures, but often these have no known molecular functions. One approach to recover this information is to use 3D templates – structure-function motifs that consist of a few functionally critical amino acids and may suggest functional similarity when geometrically matched to other structures. Since experimentally determined functional sites are not common enough to define 3D templates on a large scale, this work tests a computational strategy to select relevant residues for 3D templates.
---
paper_title: Using evolutionary information to find specificity-determining and co-evolving residues.

paper_content:

Intricate networks of protein interactions rely on the ability of a protein to recognize its targets: other proteins, ligands, and sites on DNA and RNA. To recognize other molecules, it was suggested that a protein uses a small set of specificity-determining residues (SDRs). How can one find these residues in proteins and distinguish them from other functionally important amino acids? A number of bioinformatics methods to predict SDRs have been developed in recent years. These methods use genomic information and multiple sequence alignments to identify positions exhibiting a specific pattern of conservation and variability. The challenge is to delineate the evolutionary pattern of SDRs from that of the active site residues and the residues responsible for formation of the protein's structure. The phylogenetic history of a protein family makes such analysis particularly hard. Here we present two methods for finding the SDRs and the co-evolving residues (CERs) in proteins. We use a Monte Carlo approach for statistical inference, allowing us to reveal specific evolutionary patterns of SDRs and CERs. We apply these methods to study specific recognition in the bacterial two-component system and in the class Ia aminoacyl-tRNA synthetases. Our results agree well with structural information and the experimental analyses of these systems. Our results point at the complex and distinct patterns characteristic of the evolution of specificity in these systems.
---
paper_title: An automated stochastic approach to the identification of the protein specificity determinants and functional subfamilies

paper_content:

BackgroundRecent progress in sequencing and 3 D structure determination techniques stimulated development of approaches aimed at more precise annotation of proteins, that is, prediction of exact specificity to a ligand or, more broadly, to a binding partner of any kind.ResultsWe present a method, SDPclust, for identification of protein functional subfamilies coupled with prediction of specificity-determining positions (SDPs). SDPclust predicts specificity in a phylogeny-independent stochastic manner, which allows for the correct identification of the specificity for proteins that are separated on a phylogenetic tree, but still bind the same ligand. SDPclust is implemented as a Web-server http://bioinf.fbb.msu.ru/SDPfoxWeb/ and a stand-alone Java application available from the website.ConclusionsSDPclust performs a simultaneous identification of specificity determinants and specificity groups in a statistically robust and phylogeny-independent manner.
---
paper_title: Bayesian search of functionally divergent protein subgroups and their function specific residues

paper_content:

Motivation: The rapid increase in the amount of protein sequence data has created a need for an automated identification of evolutionarily related subgroups from large datasets. The existing methods typically require a priori specification of the number of putative groups, which defines the resolution of the classification solution. ::: ::: Results: We introduce a Bayesian model-based approach to simultaneous identification of evolutionary groups and conserved parts of the protein sequences. The model-based approach provides an intuitive and efficient way of determining the number of groups from the sequence data, in contrast to the ad hoc methods often exploited for similar purposes. Our model recognizes the areas in the sequences that are relevant for the clustering and regards other areas as noise. We have implemented the method using a fast stochastic optimization algorithm which yields a clustering associated with the estimated maximum posterior probability. The method has been shown to have high specificity and sensitivity in simulated and real clustering tasks. With real datasets the method also highlights the residues close to the active site. ::: ::: Availability: Software 'kPax' is available at http://www.rni.helsinki.fi/jic/softa.html ::: ::: Contact: pekka.marttinen@helsinki.fi ::: ::: Supplementary information: http://www.rni.helsinki.fi/~jic/softa.html
---
paper_title: Predicting specificity-determining residues in two large eukaryotic transcription factor families

paper_content:

Certain amino acid residues in a protein, when mutated, change the protein’s function. We present an improved method of finding these specificitydetermining positions that uses all the protein sequence data available for a family of homologous proteins. We study in detail two families of eukaryotic transcription factors, basic leucine zippers and nuclear receptors, because of the large amount of sequences and experimental data available. These protein families also have a clear definition of functional specificity: DNA-binding specificity. We compare our results to three other methods, including the evolutionary trace algorithm and a method that depends on orthology relationships. All of the predictions are compared to the available mutational and crystallographic data. We find that our method provides superior predictions of the known specificitydetermining residues and also predicts residue positions within these families that deserve further study for their roles in functional specificity.
---
paper_title: Automatic methods for predicting functionally important residues

paper_content:

Sequence analysis is often the first guide for the prediction of residues in a protein family that may have functional significance. A few methods have been proposed which use the division of protein families into subfamilies in the search for those positions that could have some functional significance for the whole family, but at the same time which exhibit the specificity of each subfamily (“Tree-determinant residues”). However, there are still many unsolved questions like the best division of a protein family into subfamilies, or the accurate detection of sequence variation patterns characteristic of different subfamilies. Here we present a systematic study in a significant number of protein families, testing the statistical meaning of the Tree-determinant residues predicted by three different methods that represent the range of available approaches. The first method takes as a starting point a phylogenetic representation of a protein family and, following the principle of Relative Entropy from Information Theory, automatically searches for the optimal division of the family into subfamilies. The second method looks for positions whose mutational behavior is reminiscent of the mutational behavior of the full-length proteins, by directly comparing the corresponding distance matrices. The third method is an automation of the analysis of distribution of sequences and amino acid positions in the corresponding multidimensional spaces using a vector-based principal component analysis. These three methods have been tested on two non-redundant lists of protein families: one composed by proteins that bind a variety of ligand groups, and the other composed by proteins with annotated functionally relevant sites. In most cases, the residues predicted by the three methods show a clear tendency to be close to bound ligands of biological relevance and to those amino acids described as participants in key aspects of protein function. These three automatic methods provide a wide range of possibilities for biologists to analyze their families of interest, in a similar way to the one presented here for the family of proteins related with ras-p21.
---
paper_title: Predicting functional divergence in protein evolution by site-specific rate shifts.

paper_content:

Most modern tools that analyze protein evolution allow individual sites to mutate at constant rates over the history of the protein family. However, Walter Fitch observed in the 1970s that, if a protein changes its function, the mutability of individual sites might also change. This observation is captured in the "non-homogeneous gamma model", which extracts functional information from gene families by examining the different rates at which individual sites evolve. This model has recently been coupled with structural and molecular biology to identify sites that are likely to be involved in changing function within the gene family. Applying this to multiple gene families highlights the widespread divergence of functional behavior among proteins to generate paralogs and orthologs.
---
paper_title: Supervised multivariate analysis of sequence groups to identify specificity determining residues

paper_content:

BackgroundProteins that evolve from a common ancestor can change functionality over time, and it is important to be able identify residues that cause this change. In this paper we show how a supervised multivariate statistical method, Between Group Analysis (BGA), can be used to identify these residues from families of proteins with different substrate specifities using multiple sequence alignments.ResultsWe demonstrate the usefulness of this method on three different test cases. Two of these test cases, the Lactate/Malate dehydrogenase family and Nucleotidyl Cyclases, consist of two functional groups. The other family, Serine Proteases consists of three groups. BGA was used to analyse and visualise these three families using two different encoding schemes for the amino acids.ConclusionThis overall combination of methods in this paper is powerful and flexible while being computationally very fast and simple. BGA is especially useful because it can be used to analyse any number of functional classes. In the examples we used in this paper, we have only used 2 or 3 classes for demonstration purposes but any number can be used and visualised.
---
paper_title: OrthoMCL: identification of ortholog groups for eukaryotic genomes.

paper_content:

The identification of orthologous groups is useful for genome annotation, studies on gene/protein evolution, comparative genomics, and the identification of taxonomically restricted sequences. Methods successfully exploited for prokaryotic genome analysis have proved difficult to apply to eukaryotes, however, as larger genomes may contain multiple paralogous genes, and sequence information is often incomplete. OrthoMCL provides a scalable method for constructing orthologous groups across multiple eukaryotic taxa, using a Markov Cluster algorithm to group (putative) orthologs and paralogs. This method performs similarly to the INPARANOID algorithm when applied to two genomes, but can be extended to cluster orthologs from multiple species. OrthoMCL clusters are coherent with groups identified by EGO, but improved recognition of "recent" paralogs permits overlapping EGO groups representing the same gene to be merged. Comparison with previously assigned EC annotations suggests a high degree of reliability, implying utility for automated eukaryotic genome annotation. OrthoMCL has been applied to the proteome data set from seven publicly available genomes (human, fly, worm, yeast, Arabidopsis, the malaria parasite Plasmodium falciparum, and Escherichia coli). A Web interface allows queries based on individual genes or user-defined phylogenetic patterns (http://www.cbil.upenn.edu/gene-family). Analysis of clusters incorporating P. falciparum genes identifies numerous enzymes that were incompletely annotated in first-pass annotation of the parasite genome.
---
paper_title: ConSurf: an algorithmic tool for the identification of functional regions in proteins by surface mapping of phylogenetic information

paper_content:

Abstract Experimental approaches for the identification of functionally important regions on the surface of a protein involve mutagenesis, in which exposed residues are replaced one after another while the change in binding to other proteins or changes in activity are recorded. However, practical considerations limit the use of these methods to small-scale studies, precluding a full mapping of all the functionally important residues on the surface of a protein. We present here an alternative approach involving the use of evolutionary data in the form of multiple-sequence alignment for a protein family to identify hot spots and surface patches that are likely to be in contact with other proteins, domains, peptides, DNA, RNA or ligands. The underlying assumption in this approach is that key residues that are important for binding should be conserved throughout evolution, just like residues that are crucial for maintaining the protein fold, i.e. buried residues. A main limitation in the implementation of this approach is that the sequence space of a protein family may be unevenly sampled, e.g. mammals may be overly represented. Thus, a seemingly conserved position in the alignment may reflect a taxonomically uneven sampling, rather than being indicative of structural or functional importance. To avoid this problem, we present here a novel methodology based on evolutionary relations among proteins as revealed by inferred phylogenetic trees, and demonstrate its capabilities for mapping binding sites in SH2 and PTB signaling domains. A computer program that implements these ideas is available freely at: http://ashtoret.tau.ac.il/∼rony
---
paper_title: Functional specificity lies within the properties and evolutionary changes of amino acids.

paper_content:

The rapid increase in the amount of protein sequence data has created a need for automated identification of sites that determine functional specificity among related subfamilies of proteins. A significant fraction of subfamily specific sites are only marginally conserved, which makes it extremely challenging to detect those amino acid changes that lead to functional diversification. To address this critical problem we developed a method named SPEER (specificity prediction using amino acids' properties, entropy and evolution rate) to distinguish specificity determining sites from others. SPEER encodes the conservation patterns of amino acid types using their physico-chemical properties and the heterogeneity of evolutionary changes between and within the subfamilies. To test the method, we compiled a test set containing 13 protein families with known specificity determining sites. Extensive benchmarking by comparing the performance of SPEER with other specificity site prediction algorithms has shown that it performs better in predicting several categories of subfamily specific sites.
---
paper_title: RIO: Analyzing proteomes by automated phylogenomics using resampled inference of orthologs

paper_content:

BACKGROUND ::: When analyzing protein sequences using sequence similarity searches, orthologous sequences (that diverged by speciation) are more reliable predictors of a new protein's function than paralogous sequences (that diverged by gene duplication). The utility of phylogenetic information in high-throughput genome annotation ("phylogenomics") is widely recognized, but existing approaches are either manual or not explicitly based on phylogenetic trees. ::: ::: ::: RESULTS ::: Here we present RIO (Resampled Inference of Orthologs), a procedure for automated phylogenomics using explicit phylogenetic inference. RIO analyses are performed over bootstrap resampled phylogenetic trees to estimate the reliability of orthology assignments. We also introduce supplementary concepts that are helpful for functional inference. RIO has been implemented as Perl pipeline connecting several C and Java programs. It is available at http://www.genetics.wustl.edu/eddy/forester/. A web server is at http://www.rio.wustl.edu/. RIO was tested on the Arabidopsis thaliana and Caenorhabditis elegans proteomes. ::: ::: ::: CONCLUSION ::: The RIO procedure is particularly useful for the automated detection of first representatives of novel protein subfamilies. We also describe how some orthologies can be misleading for functional inference.
---
paper_title: Automated Protein Subfamily Identification and Classification

paper_content:

Function prediction by homology is widely used to provide preliminary functional annotations for genes for which experimental evidence of function is unavailable or limited. This approach has been shown to be prone to systematic error, including percolation of annotation errors through sequence databases. Phylogenomic analysis avoids these errors in function prediction but has been difficult to automate for high-throughput application. To address this limitation, we present a computationally efficient pipeline for phylogenomic classification of proteins. This pipeline uses the SCI-PHY (Subfamily Classification in Phylogenomics) algorithm for automatic subfamily identification, followed by subfamily hidden Markov model (HMM) construction. A simple and computationally efficient scoring scheme using family and subfamily HMMs enables classification of novel sequences to protein families and subfamilies. Sequences representing entirely novel subfamilies are differentiated from those that can be classified to subfamilies in the input training set using logistic regression. Subfamily HMM parameters are estimated using an information-sharing protocol, enabling subfamilies containing even a single sequence to benefit from conservation patterns defining the family as a whole or in related subfamilies. SCI-PHY subfamilies correspond closely to functional subtypes defined by experts and to conserved clades found by phylogenetic analysis. Extensive comparisons of subfamily and family HMM performances show that subfamily HMMs dramatically improve the separation between homologous and non-homologous proteins in sequence database searches. Subfamily HMMs also provide extremely high specificity of classification and can be used to predict entirely novel subtypes. The SCI-PHY Web server at http://phylogenomics.berkeley.edu/SCI-PHY/ allows users to upload a multiple sequence alignment for subfamily identification and subfamily HMM construction. Biologists wishing to provide their own subfamily definitions can do so. Source code is available on the Web page. The Berkeley Phylogenomics Group PhyloFacts resource contains pre-calculated subfamily predictions and subfamily HMMs for more than 40,000 protein families and domains at http://phylogenomics.berkeley.edu/phylofacts/.
---
paper_title: Clustering of proximal sequence space for the identification of protein families

paper_content:

Motivation: The study of sequence space, and the deciphering of the structure of protein families and subfamilies, has up to now been required for work in comparative genomics and for the prediction of protein function. With the emergence of structural proteomics projects, it is becoming increasingly important to be able to select protein targets for structural studies that will appropriately cover the space of protein sequences, functions and genomic distribution. These problems are the motivation for the development of methods for clustering protein sequences and building families of potentially orthologous sequences, such as those proposed here. Results: First we developed a clustering strategy (Ncut algorithm) capable of forming groups of related sequences by assessing their pairwise relationships. The results presented for the ras super-family of proteins are similar to those produced by other clustering methods, but without the need for clustering the full sequence space. The Ncut clusters are then used as the input to a process of reconstruction of groups with equilibrated genomic composition formed by closely-related sequences. The results of applying this technique to the data set used in the construction of the COG database are very similar to those derived by the human experts responsible for this database. Availability: The analysis of different systems, including the COG equivalent 21 genomes are available at http: //www.pdg.cnb.uam.es/GenoClustering.html Contact: valencia@cnb.uam.es
---
paper_title: Characterization and prediction of residues determining protein functional specificity

paper_content:

Motivation: Within a homologous protein family, proteins may be grouped into subtypes that share specific functions that are not common to the entire family. Often, the amino acids present in a small number of sequence positions determine each protein's particular function-al specificity. Knowledge of these specificity determining positions (SDPs) aids in protein function prediction, drug design and experimental analysis. A number of sequence-based computational methods have been introduced for identifying SDPs; however, their further development and evaluation have been hindered by the limited number of known experimentally determined SDPs. ::: ::: Results: We combine several bioinformatics resources to automate a process, typically undertaken manually, to build a dataset of SDPs. The resulting large dataset, which consists of SDPs in enzymes, enables us to characterize SDPs in terms of their physicochemical and evolution-ary properties. It also facilitates the large-scale evaluation of sequence-based SDP prediction methods. We present a simple sequence-based SDP prediction method, GroupSim, and show that, surprisingly, it is competitive with a representative set of current methods. We also describe ConsWin, a heuristic that considers sequence conservation of neighboring amino acids, and demonstrate that it improves the performance of all methods tested on our large dataset of enzyme SDPs. ::: ::: Availability: Datasets and GroupSim code are available online at http://compbio.cs.princeton.edu/specificity/ ::: ::: Contact: msingh@cs.princeton.edu ::: ::: Supplementary information:Supplementary data are available at Bioinformatics online.
---
paper_title: Automated structure-based prediction of functional sites in proteins: applications to assessing the validity of inheriting protein function from homology in genome annotation and to protein docking.

paper_content:

A major problem in genome annotation is whether it is valid to transfer the function from a characterised protein to a homologue of unknown activity. Here, we show that one can employ a strategy that uses a structure-based prediction of protein functional sites to assess the reliability of functional inheritance. We have automated and benchmarked a method based on the evolutionary trace approach. Using a multiple sequence alignment, we identified invariant polar residues, which were then mapped onto the protein structure. Spatial clusters of these invariant residues formed the predicted functional site. For 68 of 86 proteins examined, the method yielded information about the observed functional site. This algorithm for functional site prediction was then used to assess the validity of transferring the function between homologues. This procedure was tested on 18 pairs of homologous proteins with unrelated function and 70 pairs of proteins with related function, and was shown to be 94 % accurate. This automated method could be linked to schemes for genome annotation. Finally, we examined the use of functional site prediction in protein-protein and protein-DNA docking. The use of predicted functional sites was shown to filter putative docked complexes with a discrimination similar to that obtained by manually including biological information about active sites or DNA-binding residues.
---
paper_title: Secator: A Program for Inferring Protein Subfamilies from Phylogenetic Trees

paper_content:

With the huge increase of protein data, an important problem is to estimate, within a large protein family, the number of sensible subsets for subsequent in-depth structural, functional, and evolutionary analyses. To tackle this problem, we developed a new program, Secator, which implements the principle of an ascending hierarchical method using a distance matrix based on a multiple alignment of protein sequences. Dissimilarity values assigned to the nodes of a deduced phylogenetic tree are partitioned by a new stopping rule introduced to automatically determine the significant dissimilarity values. The quality of the clusters obtained by Secator is verified by a separate Jackknife study. The method is demonstrated on 24 large protein families covering a wide spectrum of structural and sequence conservation and its usefulness and accuracy with real biological data is illustrated on two well-studied protein families (the Sm proteins and the nuclear receptors).
---
paper_title: Sequence analysis Multi-RELIEF : a method to recognize specificity determining residues from multiple sequence alignments using a Machine-Learning approach for feature weighting

paper_content:

Motivation: Identification of residues that account for protein function specificity is crucial, not only for understanding the nature of functional specificity, but also for protein engineering experiments aimed at switching the specificity of an enzyme, regulator or transporter. Available algorithms generally use multiple sequence alignments to identify residue positions conserved within subfamilies but divergent in between. However, many biological examples show a much subtler picture than simple intra-group conservation versus inter-group divergence. ::: ::: Results: We present multi-RELIEF, a novel approach for identifying specificity residues that is based on RELIEF, a state-of-the-art Machine-Learning technique for feature weighting. It estimates the expected ‘local’ functional specificity of residues from an alignment divided in multiple classes. Optionally, 3D structure information is exploited by increasing the weight of residues that have high-weight neighbors. Using ROC curves over a large body of experimental reference data, we show that (a) multi-RELIEF identifies specificity residues for the seven test sets used, (b) incorporating structural information improves prediction for specificity of interaction with small molecules and (c) comparison of multi-RELIEF with four other state-of-the-art algorithms indicates its robustness and best overall performance. ::: ::: Availability: A web-server implementation of multi-RELIEF is available at www.ibi.vu.nl/programs/multirelief. Matlab source code of the algorithm and data sets are available on request for academic use. ::: ::: Contact: [email protected] ::: ::: Supplementary information: Supplemenmtary data are available at Bioinformatics online
---
paper_title: Multi-Harmony: detecting functional specificity from sequence alignment

paper_content:

Many protein families contain sub-families with functional specialization, such as binding different ligands or being involved in different protein-protein interactions. A small number of amino acids generally determine functional specificity. The identification of these residues can aid the understanding of protein function and help finding targets for experimental analysis. Here, we present multi-Harmony, an interactive web sever for detecting sub-type-specific sites in proteins starting from a multiple sequence alignment. Combining our Sequence Harmony (SH) and multi-Relief (mR) methods in one web server allows simultaneous analysis and comparison of specificity residues; furthermore, both methods have been significantly improved and extended. SH has been extended to cope with more than two sub-groups. mR has been changed from a sampling implementation to a deterministic one, making it more consistent and user friendly. For both methods Z-scores are reported. The multi-Harmony web server produces a dynamic output page, which includes interactive connections to the Jalview and Jmol applets, thereby allowing interactive analysis of the results. Multi-Harmony is available at http://www.ibi.vu.nl/ programs/shmrwww.
---
paper_title: BADASP: predicting functional specificity in protein families using ancestral sequences

paper_content:

Summary: Burst After Duplication with Ancestral Sequence Predictions (BADASP) is a software package for identifying sites that may confer subfamily-specific biological functions in protein families following functional divergence of duplicated proteins. A given protein phylogeny is grouped into subfamilies based on orthology/paralogy relationships and/or user definitions. Ancestral sequences are then predicted from the sequence alignment and the functional specificity is calculated using variants of the Burst After Duplication method, which tests for radical amino acid substitutions following gene duplications that are subsequently conserved. Statistics are output along with subfamily groupings and ancestral sequences for an easy analysis with other packages. ::: ::: Availability: BADASP is freely available from http://www.bioinformatics.rcsi.ie/~redwards/badasp/ ::: ::: Contact: redwards@rcsi.ie ::: ::: Supplementary information: A manual with further details can be downloaded from http://www.bioinformatics.rcsi.ie/~redwards/badasp/
---
paper_title: Classification of protein families and detection of the determinant residues with an improved self-organizing map

paper_content:

Using a SOM (self-organizing map) we can classify sequences within a protein family into subgroups that generally correspond to biological subcategories. These maps tend to show sequence similarity as proximity in the map. Combining maps generated at different levels of resolution, the structure of relations in protein families can be captured that could not otherwise be represented in a single map. The underlying representation of maps enables us to retrieve characteristic sequence patterns for individual subgroups of sequences. Such patterns tend to correspond to functionally important regions. We present a modified SOM algorithm that includes a convergence test that dynamically controls the learning parameters to adapt them to the learning set instead of being fixed and externally optimized by trial and error. Given the variability of protein family size and distribution, the addition of this features is necessary. The method is successfully tested with a number of families. The rab family of small GTPases is used to illustrate the performance of the method.
---
paper_title: INTREPID: a web server for prediction of functionally important residues by evolutionary analysis

paper_content:

We present the INTREPID web server for predicting functionally important residues in proteins. INTREPID has been shown to boost the recall and precision of catalytic residue prediction over other sequence-based methods and can be used to identify other types of functional residues. The web server takes an input protein sequence, gathers homologs, constructs a multiple sequence alignment and phylogenetic tree and finally runs the INTREPID method to assign a score to each position. Residues predicted to be functionally important are displayed on homologous 3D structures (where available), highlighting spatial patterns of conservation at various significance thresholds. The INTREPID web server is available at http://phylogenomics.berkeley.edu/intrepid.
---
paper_title: Phylogeny-independent detection of functional residues

paper_content:

Motivation: Current projects for the massive characterization of proteomes are generating protein sequences and structures with unknown function. The difficulty of experimentally determining functionally important sites calls for the development of computational methods. The first techniques, based on the search for fully conserved positions in multiple sequence alignments (MSAs), were followed by methods for locating family-dependent conserved positions. These rely on the functional classification implicit in the alignment for locating these positions related with functional specificity. The next obvious step, still scarcely explored, is to detect these positions using a functional classification different from the one implicit in the sequence relationships between the proteins. Here, we present two new methods for locating functional positions which can incorporate an arbitrary external functional classification which may or may not coincide with the one implicit in the MSA. The Xdet method is able to use a functional classification with an associated hierarchy or similarity between functions to locate positions related to that classification. The MCdet method uses multivariate statistical analysis to locate positions responsible for each one of the functions within a multifunctional family. ::: ::: Results: We applied the methods to different cases, illustrating scenarios where there is a disagreement between the functional and the phylogenetic relationships, and demonstrated their usefulness for the phylogeny-independent prediction of functional positions. ::: ::: Availability: All computer programs and datasets used in this work are available from the authors for academic use. ::: ::: Contact: [email protected] ::: ::: Supplementary information: Supplementary data are available at http://pdg.cnb.uam.es/pazos/Xdet_MCdet_Add/
---
paper_title: The contrasting properties of conservation and correlated phylogeny in protein functional residue prediction

paper_content:

BackgroundAmino acids responsible for structure, core function or specificity may be inferred from multiple protein sequence alignments where a limited set of residue types are tolerated. The rise in available protein sequences continues to increase the power of techniques based on this principle.ResultsA new algorithm, SMERFS, for predicting protein functional sites from multiple sequences alignments was compared to 14 conservation measures and to the MINER algorithm. Validation was performed on an automatically generated dataset of 1457 families derived from the protein interactions database SNAPPI-DB, and a smaller manually curated set of 148 families. The best performing measure overall was Williamson property entropy, with ROC0.1 scores of 0.0087 and 0.0114 for domain and small molecule contact prediction, respectively. The Lancet method performed worse than random on protein-protein interaction site prediction (ROC0.1 score of 0.0008). The SMERFS algorithm gave similar accuracy to the phylogenetic tree-based MINER algorithm but was superior to Williamson in prediction of non-catalytic transient complex interfaces. SMERFS predicts sites that are significantly more solvent accessible compared to Williamson.ConclusionWilliamson property entropy is the the best performing of 14 conservation measures examined. The difference in performance of SMERFS relative to Williamson in manually defined complexes was dependent on complex type. The best choice of analysis method is therefore dependent on the system of interest. Additional computation employed by Miner in calculation of phylogenetic trees did not produce improved results over SMERFS. SMERFS performance was improved by use of windows over alignment columns, illustrating the necessity of considering the local environment of positions when assessing their functional significance.
---
paper_title: Determinants of protein function revealed by combinatorial entropy optimization

paper_content:

We use a new algorithm (combinatorial entropy optimization [CEO]) to identify specificity residues and functional subfamilies in sets of proteins related by evolution. Specificity residues are conserved within a subfamily but differ between subfamilies, and they typically encode functional diversity. We obtain good agreement between predicted specificity residues and experimentally known functional residues in protein interfaces. Such predicted functional determinants are useful for interpreting the functional consequences of mutations in natural evolution and disease.
---
paper_title: Automated ortholog inference from phylogenetic trees and calculation of orthology reliability

paper_content:

Motivation: Orthologous proteins in different species are likely to have similar biochemical function and biological role. When annotating a newly sequenced genome by sequence homology, the most precise and reliable functional information can thus be derived from orthologs in other species. A standard method of finding orthologs is to compare the sequence tree with the species tree. However, since the topology of phylogenetic tree is not always reliable one might get incorrect assignments. Results: Here we present a novel method that resolves this problem by analyzing a set of bootstrap trees instead of the optimal tree. The frequency of orthology assignments in the bootstrap trees can be interpreted as a support value for the possible orthology of the sequences. Our method is efficient enough to analyze data in the scale of whole genomes. It is implemented in Java and calculates orthology support levels for all pairwise combinations of homologous sequences of two species. The method was tested on simulated datasets and on real data of homologous proteins. Availability: Downloadable free of charge from ftp://ftp. cgb.ki.se/pub/prog/orthostrapper/ or on request from the authors.
---
paper_title: Automatic clustering of orthologs and in-paralogs from pairwise species comparisons

paper_content:

Orthologs are genes in different species that originate from a single gene in the last common ancestor of these species. Such genes have often retained identical biological roles in the present-day organisms. It is hence important to identify orthologs for transferring functional information between genes in different organisms with a high degree of reliability. For example, orthologs of human proteins are often functionally characterized in model organisms. Unfortunately, orthology analysis between human and e.g. invertebrates is often complex because of large numbers of paralogs within protein families. Paralogs that predate the species split, which we call out-paralogs, can easily be confused with true orthologs. Paralogs that arose after the species split, which we call in-paralogs, however, are bona fide orthologs by definition. Orthologs and in-paralogs are typically detected with phylogenetic methods, but these are slow and difficult to automate. Automatic clustering methods based on two-way best genome-wide matches on the other hand, have so far not separated in-paralogs from out-paralogs effectively. We present a fully automatic method for finding orthologs and in-paralogs from two species. Ortholog clusters are seeded with a two-way best pairwise match, after which an algorithm for adding in-paralogs is applied. The method bypasses multiple alignments and phylogenetic trees, which can be slow and error-prone steps in classical ortholog detection. Still, it robustly detects complex orthologous relationships and assigns confidence values for both orthologs and in-paralogs. The program, called INPARANOID, was tested on all completely sequenced eukaryotic genomes. To assess the quality of INPARANOID results, ortholog clusters were generated from a dataset of worm and mammalian transmembrane proteins, and were compared to clusters derived by manual tree-based ortholog detection methods. This study led to the identification with a high degree of confidence of over a dozen novel worm-mammalian ortholog assignments that were previously undetected because of shortcomings of phylogenetic methods.A WWW server that allows searching for orthologs between human and several fully sequenced genomes is installed at http://www.cgb.ki.se/inparanoid/. This is the first comprehensive resource with orthologs of all fully sequenced eukaryotic genomes. Programs and tables of orthology assignments are available from the same location.
---
paper_title: Automated hierarchical classification of protein domain subfamilies based on functionally-divergent residue signatures

paper_content:

BACKGROUND ::: The NCBI Conserved Domain Database (CDD) consists of a collection of multiple sequence alignments of protein domains that are at various stages of being manually curated into evolutionary hierarchies based on conserved and divergent sequence and structural features. These domain models are annotated to provide insights into the relationships between sequence, structure and function via web-based BLAST searches. ::: ::: ::: RESULTS ::: Here we automate the generation of conserved domain (CD) hierarchies using a combination of heuristic and Markov chain Monte Carlo (MCMC) sampling procedures and starting from a (typically very large) multiple sequence alignment. This procedure relies on statistical criteria to define each hierarchy based on the conserved and divergent sequence patterns associated with protein functional-specialization. At the same time this facilitates the sequence and structural annotation of residues that are functionally important. These statistical criteria also provide a means to objectively assess the quality of CD hierarchies, a non-trivial task considering that the protein subgroups are often very distantly related--a situation in which standard phylogenetic methods can be unreliable. Our aim here is to automatically generate (typically sub-optimal) hierarchies that, based on statistical criteria and visual comparisons, are comparable to manually curated hierarchies; this serves as the first step toward the ultimate goal of obtaining optimal hierarchical classifications. A plot of runtimes for the most time-intensive (non-parallelizable) part of the algorithm indicates a nearly linear time complexity so that, even for the extremely large Rossmann fold protein class, results were obtained in about a day. ::: ::: ::: CONCLUSIONS ::: This approach automates the rapid creation of protein domain hierarchies and thus will eliminate one of the most time consuming aspects of conserved domain database curation. At the same time, it also facilitates protein domain annotation by identifying those pattern residues that most distinguish each protein domain subgroup from other related subgroups.
---
paper_title: An Introduction to Variable and Feature Selection

paper_content:

Variable and feature selection have become the focus of much research in areas of application for which datasets with tens or hundreds of thousands of variables are available. These areas include text processing of internet documents, gene expression array analysis, and combinatorial chemistry. The objective of variable selection is three-fold: improving the prediction performance of the predictors, providing faster and more cost-effective predictors, and providing a better understanding of the underlying process that generated the data. The contributions of this special issue cover a wide range of aspects of such problems: providing a better definition of the objective function, feature construction, feature ranking, multivariate feature selection, efficient search methods, and feature validity assessment methods.
---
paper_title: Combining specificity determining and conserved residues improves functional site prediction

paper_content:

BackgroundPredicting the location of functionally important sites from protein sequence and/or structure is a long-standing problem in computational biology. Most current approaches make use of sequence conservation, assuming that amino acid residues conserved within a protein family are most likely to be functionally important. Most often these approaches do not consider many residues that act to define specific sub-functions within a family, or they make no distinction between residues important for function and those more relevant for maintaining structure (e.g. in the hydrophobic core). Many protein families bind and/or act on a variety of ligands, meaning that conserved residues often only bind a common ligand sub-structure or perform general catalytic activities.ResultsHere we present a novel method for functional site prediction based on identification of conserved positions, as well as those responsible for determining ligand specificity. We define Specificity-Determining Positions (SDPs), as those occupied by conserved residues within sub-groups of proteins in a family having a common specificity, but differ between groups, and are thus likely to account for specific recognition events. We benchmark the approach on enzyme families of known 3D structure with bound substrates, and find that in nearly all families residues predicted by SDPsite are in contact with the bound substrate, and that the addition of SDPs significantly improves functional site prediction accuracy. We apply SDPsite to various families of proteins containing known three-dimensional structures, but lacking clear functional annotations, and discusse several illustrative examples.ConclusionThe results suggest a better means to predict functional details for the thousands of protein structures determined prior to a clear understanding of molecular function.
---
paper_title: Sequence comparison by sequence harmony identifies subtype-specific functional sites

paper_content:

Multiple sequence alignments are often used to reveal functionally important residues within a protein family. They can be particularly useful for the identification of key residues that determine functional differences between protein subfamilies. We present a new entropy-based method, Sequence Harmony (SH) that accurately detects subfamily-specific positions from a multiple sequence alignment. The SH algorithm implements a novel formula, able to score compositional differences between subfamilies, without imposing conservation, in a simple manner on an intuitive scale. We compare our method with the most important published methods, i.e. AMAS, TreeDet and SDP-pred, using three well-studied protein families: the receptor-binding domain (MH2) of the Smad family of transcription factors, the Ras-superfamily of small GTPases and the MIP-family of integral membrane transporters. We demonstrate that SH accurately selects known functional sites with higher coverage than the other methods for these test-cases. This shows that compositional differences between protein subfamilies provide sufficient basis for identification of functional sites. In addition, SH selects a number of sites of unknown function that could be interesting candidates for further experimental investigation.
---
paper_title: SDPpred: a tool for prediction of amino acid residues that determine differences in functional specificity of homologous proteins.

paper_content:

SDPpred (Specificity Determining Position prediction) is a tool for prediction of residues in protein sequences that determine the proteins' functional specificity. It is designed for analysis of protein families whose members have biochemically similar but not identical interaction partners (e.g. different substrates for a family of transporters). SDPpred predicts residues that could be responsible for the proteins' choice of their correct interaction partners. The input of SDPpred is a multiple alignment of a protein family divided into a number of specificity groups, within which the interaction partner is believed to be the same. SDPpred does not require information about the secondary or three-dimensional structure of proteins. It produces a set of the alignment positions (specificity determining positions) that determine differences in functional specificity. SDPpred is available at http://math.genebee.msu.ru/~psn/.
---
paper_title: In silico discovery of enzyme-substrate specificity-determining residue clusters.

paper_content:

The binding between an enzyme and its substrate is highly specific, despite the fact that many different enzymes show significant sequence and structure similarity. There must be, then, substrate specificity-determining residues that enable different enzymes to recognize their unique substrates. We reason that a coordinated, not independent, action of both conserved and non-conserved residues determine enzymatic activity and specificity. Here, we present a surface patch ranking (SPR) method for in silico discovery of substrate specificity-determining residue clusters by exploring both sequence conservation and correlated mutations. As case studies we apply SPR to several highly homologous enzymatic protein pairs, such as guanylyl versus adenylyl cyclases, lactate versus malate dehydrogenases, and trypsin versus chymotrypsin. Without using experimental data, we predict several single and multi-residue clusters that are consistent with previous mutagenesis experimental results. Most single-residue clusters are directly involved in enzyme–substrate interactions, whereas multi-residue clusters are vital for domain–domain and regulator–enzyme interactions, indicating their complementary role in specificity determination. These results demonstrate that SPR may help the selection of target residues for mutagenesis experiments and, thus, focus rational drug design, protein engineering, and functional annotation to the relevant regions of a protein.
---
paper_title: SPEER-SERVER: a web server for prediction of protein specificity determining sites

paper_content:

Sites that show specific conservation patterns within subsets of proteins in a protein family are likely to be involved in the development of functional specificity. These sites, generally termed specificity determining sites (SDS), might play a crucial role in binding to a specific substrate or proteins. Identification of SDS through experimental techniques is a slow, difficult and tedious job. Hence, it is very important to develop efficient computational methods that can more expediently identify SDS. Herein, we present Specificity prediction using amino acids’ Properties, Entropy and Evolution Rate (SPEER)-SERVER, a web server that predicts SDS by analyzing quantitative measures of the conservation patterns of protein sites based on their physico-chemical properties and the heterogeneity of evolutionary changes between and within the protein subfamilies. This web server provides an improved representation of results, adds useful input and output options and integrates a wide range of analysis and data visualization tools when compared with the original standalone version of the SPEER algorithm. Extensive benchmarking finds that SPEER-SERVER exhibits sensitivity and precision performance that, on average, meets or exceeds that of other currently available methods. SPEER-SERVER is available at http://www.hpppi.iicb.res.in/ss/.
---
paper_title: Using evolutionary information to find specificity-determining and co-evolving residues.

paper_content:

Intricate networks of protein interactions rely on the ability of a protein to recognize its targets: other proteins, ligands, and sites on DNA and RNA. To recognize other molecules, it was suggested that a protein uses a small set of specificity-determining residues (SDRs). How can one find these residues in proteins and distinguish them from other functionally important amino acids? A number of bioinformatics methods to predict SDRs have been developed in recent years. These methods use genomic information and multiple sequence alignments to identify positions exhibiting a specific pattern of conservation and variability. The challenge is to delineate the evolutionary pattern of SDRs from that of the active site residues and the residues responsible for formation of the protein's structure. The phylogenetic history of a protein family makes such analysis particularly hard. Here we present two methods for finding the SDRs and the co-evolving residues (CERs) in proteins. We use a Monte Carlo approach for statistical inference, allowing us to reveal specific evolutionary patterns of SDRs and CERs. We apply these methods to study specific recognition in the bacterial two-component system and in the class Ia aminoacyl-tRNA synthetases. Our results agree well with structural information and the experimental analyses of these systems. Our results point at the complex and distinct patterns characteristic of the evolution of specificity in these systems.
---
paper_title: Functional specificity lies within the properties and evolutionary changes of amino acids.

paper_content:

The rapid increase in the amount of protein sequence data has created a need for automated identification of sites that determine functional specificity among related subfamilies of proteins. A significant fraction of subfamily specific sites are only marginally conserved, which makes it extremely challenging to detect those amino acid changes that lead to functional diversification. To address this critical problem we developed a method named SPEER (specificity prediction using amino acids' properties, entropy and evolution rate) to distinguish specificity determining sites from others. SPEER encodes the conservation patterns of amino acid types using their physico-chemical properties and the heterogeneity of evolutionary changes between and within the subfamilies. To test the method, we compiled a test set containing 13 protein families with known specificity determining sites. Extensive benchmarking by comparing the performance of SPEER with other specificity site prediction algorithms has shown that it performs better in predicting several categories of subfamily specific sites.
---
paper_title: Sequence analysis Multi-RELIEF : a method to recognize specificity determining residues from multiple sequence alignments using a Machine-Learning approach for feature weighting

paper_content:

Motivation: Identification of residues that account for protein function specificity is crucial, not only for understanding the nature of functional specificity, but also for protein engineering experiments aimed at switching the specificity of an enzyme, regulator or transporter. Available algorithms generally use multiple sequence alignments to identify residue positions conserved within subfamilies but divergent in between. However, many biological examples show a much subtler picture than simple intra-group conservation versus inter-group divergence. ::: ::: Results: We present multi-RELIEF, a novel approach for identifying specificity residues that is based on RELIEF, a state-of-the-art Machine-Learning technique for feature weighting. It estimates the expected ‘local’ functional specificity of residues from an alignment divided in multiple classes. Optionally, 3D structure information is exploited by increasing the weight of residues that have high-weight neighbors. Using ROC curves over a large body of experimental reference data, we show that (a) multi-RELIEF identifies specificity residues for the seven test sets used, (b) incorporating structural information improves prediction for specificity of interaction with small molecules and (c) comparison of multi-RELIEF with four other state-of-the-art algorithms indicates its robustness and best overall performance. ::: ::: Availability: A web-server implementation of multi-RELIEF is available at www.ibi.vu.nl/programs/multirelief. Matlab source code of the algorithm and data sets are available on request for academic use. ::: ::: Contact: [email protected] ::: ::: Supplementary information: Supplemenmtary data are available at Bioinformatics online
---
paper_title: Multi-Harmony: detecting functional specificity from sequence alignment

paper_content:

Many protein families contain sub-families with functional specialization, such as binding different ligands or being involved in different protein-protein interactions. A small number of amino acids generally determine functional specificity. The identification of these residues can aid the understanding of protein function and help finding targets for experimental analysis. Here, we present multi-Harmony, an interactive web sever for detecting sub-type-specific sites in proteins starting from a multiple sequence alignment. Combining our Sequence Harmony (SH) and multi-Relief (mR) methods in one web server allows simultaneous analysis and comparison of specificity residues; furthermore, both methods have been significantly improved and extended. SH has been extended to cope with more than two sub-groups. mR has been changed from a sampling implementation to a deterministic one, making it more consistent and user friendly. For both methods Z-scores are reported. The multi-Harmony web server produces a dynamic output page, which includes interactive connections to the Jalview and Jmol applets, thereby allowing interactive analysis of the results. Multi-Harmony is available at http://www.ibi.vu.nl/ programs/shmrwww.
---
paper_title: Automated ortholog inference from phylogenetic trees and calculation of orthology reliability

paper_content:

Motivation: Orthologous proteins in different species are likely to have similar biochemical function and biological role. When annotating a newly sequenced genome by sequence homology, the most precise and reliable functional information can thus be derived from orthologs in other species. A standard method of finding orthologs is to compare the sequence tree with the species tree. However, since the topology of phylogenetic tree is not always reliable one might get incorrect assignments. Results: Here we present a novel method that resolves this problem by analyzing a set of bootstrap trees instead of the optimal tree. The frequency of orthology assignments in the bootstrap trees can be interpreted as a support value for the possible orthology of the sequences. Our method is efficient enough to analyze data in the scale of whole genomes. It is implemented in Java and calculates orthology support levels for all pairwise combinations of homologous sequences of two species. The method was tested on simulated datasets and on real data of homologous proteins. Availability: Downloadable free of charge from ftp://ftp. cgb.ki.se/pub/prog/orthostrapper/ or on request from the authors.
---
paper_title: Combining specificity determining and conserved residues improves functional site prediction

paper_content:

BackgroundPredicting the location of functionally important sites from protein sequence and/or structure is a long-standing problem in computational biology. Most current approaches make use of sequence conservation, assuming that amino acid residues conserved within a protein family are most likely to be functionally important. Most often these approaches do not consider many residues that act to define specific sub-functions within a family, or they make no distinction between residues important for function and those more relevant for maintaining structure (e.g. in the hydrophobic core). Many protein families bind and/or act on a variety of ligands, meaning that conserved residues often only bind a common ligand sub-structure or perform general catalytic activities.ResultsHere we present a novel method for functional site prediction based on identification of conserved positions, as well as those responsible for determining ligand specificity. We define Specificity-Determining Positions (SDPs), as those occupied by conserved residues within sub-groups of proteins in a family having a common specificity, but differ between groups, and are thus likely to account for specific recognition events. We benchmark the approach on enzyme families of known 3D structure with bound substrates, and find that in nearly all families residues predicted by SDPsite are in contact with the bound substrate, and that the addition of SDPs significantly improves functional site prediction accuracy. We apply SDPsite to various families of proteins containing known three-dimensional structures, but lacking clear functional annotations, and discusse several illustrative examples.ConclusionThe results suggest a better means to predict functional details for the thousands of protein structures determined prior to a clear understanding of molecular function.
---
paper_title: Prediction of functional specificity determinants from protein sequences using log-likelihood ratios

paper_content:

Motivation: A number of methods have been developed to predict functional specificity determinants in protein families based on sequence information. Most of these methods rely on pre-defined functional subgroups. Manual subgroup definition is difficult because of the limited number of experimentally characterized subfamilies with differing specificity, while automatic subgroup partitioning using computational tools is a non-trivial task and does not always yield ideal results. ::: ::: Results: We propose a new approach SPEL (specificity positions by evolutionary likelihood) to detect positions that are likely to be functional specificity determinants. SPEL, which does not require subgroup definition, takes a multiple sequence alignment of a protein family as the only input, and assigns a P-value to every position in the alignment. Positions with low P-values are likely to be important for functional specificity. An evolutionary tree is reconstructed during the calculation, and P-value estimation is based on a random model that involves evolutionary simulations. Evolutionary log-likelihood is chosen as a measure of amino acid distribution at a position. To illustrate the performance of the method, we carried out a detailed analysis of two protein families (LacI/PurR and G protein α subunit), and compared our method with two existing methods (evolutionary trace and mutual information based). All three methods were also compared on a set of protein families with known ligand-bound structures. ::: ::: Availability: SPEL is freely available for non-commercial use. Its pre-compiled versions for several platforms and alignments used in this work are available at ftp://iole.swmed.edu/pub/SPEL/ ::: ::: Contact:grishin@chop.swmed.edu. ::: ::: Supplementary information: Supplementary materials are available at ftp:/iole.swmed.edu/pub/SPEL/
---
paper_title: SPEER-SERVER: a web server for prediction of protein specificity determining sites

paper_content:

Sites that show specific conservation patterns within subsets of proteins in a protein family are likely to be involved in the development of functional specificity. These sites, generally termed specificity determining sites (SDS), might play a crucial role in binding to a specific substrate or proteins. Identification of SDS through experimental techniques is a slow, difficult and tedious job. Hence, it is very important to develop efficient computational methods that can more expediently identify SDS. Herein, we present Specificity prediction using amino acids’ Properties, Entropy and Evolution Rate (SPEER)-SERVER, a web server that predicts SDS by analyzing quantitative measures of the conservation patterns of protein sites based on their physico-chemical properties and the heterogeneity of evolutionary changes between and within the protein subfamilies. This web server provides an improved representation of results, adds useful input and output options and integrates a wide range of analysis and data visualization tools when compared with the original standalone version of the SPEER algorithm. Extensive benchmarking finds that SPEER-SERVER exhibits sensitivity and precision performance that, on average, meets or exceeds that of other currently available methods. SPEER-SERVER is available at http://www.hpppi.iicb.res.in/ss/.
---
paper_title: Ensemble approach to predict specificity determinants: benchmarking and validation

paper_content:

BackgroundIt is extremely important and challenging to identify the sites that are responsible for functional specification or diversification in protein families. In this study, a rigorous comparative benchmarking protocol was employed to provide a reliable evaluation of methods which predict the specificity determining sites. Subsequently, three best performing methods were applied to identify new potential specificity determining sites through ensemble approach and common agreement of their prediction results.ResultsIt was shown that the analysis of structural characteristics of predicted specificity determining sites might provide the means to validate their prediction accuracy. For example, we found that for smaller distances it holds true that the more reliable the prediction method is, the closer predicted specificity determining sites are to each other and to the ligand.ConclusionWe observed certain similarities of structural features between predicted and actual subsites which might point to their functional relevance. We speculate that majority of the identified potential specificity determining sites might be indirectly involved in specific interactions and could be ideal target for mutagenesis experiments.
---
paper_title: Bayesian search of functionally divergent protein subgroups and their function specific residues

paper_content:

Motivation: The rapid increase in the amount of protein sequence data has created a need for an automated identification of evolutionarily related subgroups from large datasets. The existing methods typically require a priori specification of the number of putative groups, which defines the resolution of the classification solution. ::: ::: Results: We introduce a Bayesian model-based approach to simultaneous identification of evolutionary groups and conserved parts of the protein sequences. The model-based approach provides an intuitive and efficient way of determining the number of groups from the sequence data, in contrast to the ad hoc methods often exploited for similar purposes. Our model recognizes the areas in the sequences that are relevant for the clustering and regards other areas as noise. We have implemented the method using a fast stochastic optimization algorithm which yields a clustering associated with the estimated maximum posterior probability. The method has been shown to have high specificity and sensitivity in simulated and real clustering tasks. With real datasets the method also highlights the residues close to the active site. ::: ::: Availability: Software 'kPax' is available at http://www.rni.helsinki.fi/jic/softa.html ::: ::: Contact: pekka.marttinen@helsinki.fi ::: ::: Supplementary information: http://www.rni.helsinki.fi/~jic/softa.html
---
paper_title: Predicting functional divergence in protein evolution by site-specific rate shifts.

paper_content:

Most modern tools that analyze protein evolution allow individual sites to mutate at constant rates over the history of the protein family. However, Walter Fitch observed in the 1970s that, if a protein changes its function, the mutability of individual sites might also change. This observation is captured in the "non-homogeneous gamma model", which extracts functional information from gene families by examining the different rates at which individual sites evolve. This model has recently been coupled with structural and molecular biology to identify sites that are likely to be involved in changing function within the gene family. Applying this to multiple gene families highlights the widespread divergence of functional behavior among proteins to generate paralogs and orthologs.
---
paper_title: OrthoMCL: identification of ortholog groups for eukaryotic genomes.

paper_content:

The identification of orthologous groups is useful for genome annotation, studies on gene/protein evolution, comparative genomics, and the identification of taxonomically restricted sequences. Methods successfully exploited for prokaryotic genome analysis have proved difficult to apply to eukaryotes, however, as larger genomes may contain multiple paralogous genes, and sequence information is often incomplete. OrthoMCL provides a scalable method for constructing orthologous groups across multiple eukaryotic taxa, using a Markov Cluster algorithm to group (putative) orthologs and paralogs. This method performs similarly to the INPARANOID algorithm when applied to two genomes, but can be extended to cluster orthologs from multiple species. OrthoMCL clusters are coherent with groups identified by EGO, but improved recognition of "recent" paralogs permits overlapping EGO groups representing the same gene to be merged. Comparison with previously assigned EC annotations suggests a high degree of reliability, implying utility for automated eukaryotic genome annotation. OrthoMCL has been applied to the proteome data set from seven publicly available genomes (human, fly, worm, yeast, Arabidopsis, the malaria parasite Plasmodium falciparum, and Escherichia coli). A Web interface allows queries based on individual genes or user-defined phylogenetic patterns (http://www.cbil.upenn.edu/gene-family). Analysis of clusters incorporating P. falciparum genes identifies numerous enzymes that were incompletely annotated in first-pass annotation of the parasite genome.
---
paper_title: Functional specificity lies within the properties and evolutionary changes of amino acids.

paper_content:

The rapid increase in the amount of protein sequence data has created a need for automated identification of sites that determine functional specificity among related subfamilies of proteins. A significant fraction of subfamily specific sites are only marginally conserved, which makes it extremely challenging to detect those amino acid changes that lead to functional diversification. To address this critical problem we developed a method named SPEER (specificity prediction using amino acids' properties, entropy and evolution rate) to distinguish specificity determining sites from others. SPEER encodes the conservation patterns of amino acid types using their physico-chemical properties and the heterogeneity of evolutionary changes between and within the subfamilies. To test the method, we compiled a test set containing 13 protein families with known specificity determining sites. Extensive benchmarking by comparing the performance of SPEER with other specificity site prediction algorithms has shown that it performs better in predicting several categories of subfamily specific sites.
---
paper_title: RIO: Analyzing proteomes by automated phylogenomics using resampled inference of orthologs

paper_content:

BACKGROUND ::: When analyzing protein sequences using sequence similarity searches, orthologous sequences (that diverged by speciation) are more reliable predictors of a new protein's function than paralogous sequences (that diverged by gene duplication). The utility of phylogenetic information in high-throughput genome annotation ("phylogenomics") is widely recognized, but existing approaches are either manual or not explicitly based on phylogenetic trees. ::: ::: ::: RESULTS ::: Here we present RIO (Resampled Inference of Orthologs), a procedure for automated phylogenomics using explicit phylogenetic inference. RIO analyses are performed over bootstrap resampled phylogenetic trees to estimate the reliability of orthology assignments. We also introduce supplementary concepts that are helpful for functional inference. RIO has been implemented as Perl pipeline connecting several C and Java programs. It is available at http://www.genetics.wustl.edu/eddy/forester/. A web server is at http://www.rio.wustl.edu/. RIO was tested on the Arabidopsis thaliana and Caenorhabditis elegans proteomes. ::: ::: ::: CONCLUSION ::: The RIO procedure is particularly useful for the automated detection of first representatives of novel protein subfamilies. We also describe how some orthologies can be misleading for functional inference.
---
paper_title: Automated Protein Subfamily Identification and Classification

paper_content:

Function prediction by homology is widely used to provide preliminary functional annotations for genes for which experimental evidence of function is unavailable or limited. This approach has been shown to be prone to systematic error, including percolation of annotation errors through sequence databases. Phylogenomic analysis avoids these errors in function prediction but has been difficult to automate for high-throughput application. To address this limitation, we present a computationally efficient pipeline for phylogenomic classification of proteins. This pipeline uses the SCI-PHY (Subfamily Classification in Phylogenomics) algorithm for automatic subfamily identification, followed by subfamily hidden Markov model (HMM) construction. A simple and computationally efficient scoring scheme using family and subfamily HMMs enables classification of novel sequences to protein families and subfamilies. Sequences representing entirely novel subfamilies are differentiated from those that can be classified to subfamilies in the input training set using logistic regression. Subfamily HMM parameters are estimated using an information-sharing protocol, enabling subfamilies containing even a single sequence to benefit from conservation patterns defining the family as a whole or in related subfamilies. SCI-PHY subfamilies correspond closely to functional subtypes defined by experts and to conserved clades found by phylogenetic analysis. Extensive comparisons of subfamily and family HMM performances show that subfamily HMMs dramatically improve the separation between homologous and non-homologous proteins in sequence database searches. Subfamily HMMs also provide extremely high specificity of classification and can be used to predict entirely novel subtypes. The SCI-PHY Web server at http://phylogenomics.berkeley.edu/SCI-PHY/ allows users to upload a multiple sequence alignment for subfamily identification and subfamily HMM construction. Biologists wishing to provide their own subfamily definitions can do so. Source code is available on the Web page. The Berkeley Phylogenomics Group PhyloFacts resource contains pre-calculated subfamily predictions and subfamily HMMs for more than 40,000 protein families and domains at http://phylogenomics.berkeley.edu/phylofacts/.
---
paper_title: Clustering of proximal sequence space for the identification of protein families

paper_content:

Motivation: The study of sequence space, and the deciphering of the structure of protein families and subfamilies, has up to now been required for work in comparative genomics and for the prediction of protein function. With the emergence of structural proteomics projects, it is becoming increasingly important to be able to select protein targets for structural studies that will appropriately cover the space of protein sequences, functions and genomic distribution. These problems are the motivation for the development of methods for clustering protein sequences and building families of potentially orthologous sequences, such as those proposed here. Results: First we developed a clustering strategy (Ncut algorithm) capable of forming groups of related sequences by assessing their pairwise relationships. The results presented for the ras super-family of proteins are similar to those produced by other clustering methods, but without the need for clustering the full sequence space. The Ncut clusters are then used as the input to a process of reconstruction of groups with equilibrated genomic composition formed by closely-related sequences. The results of applying this technique to the data set used in the construction of the COG database are very similar to those derived by the human experts responsible for this database. Availability: The analysis of different systems, including the COG equivalent 21 genomes are available at http: //www.pdg.cnb.uam.es/GenoClustering.html Contact: valencia@cnb.uam.es
---
paper_title: Characterization and prediction of residues determining protein functional specificity

paper_content:

Motivation: Within a homologous protein family, proteins may be grouped into subtypes that share specific functions that are not common to the entire family. Often, the amino acids present in a small number of sequence positions determine each protein's particular function-al specificity. Knowledge of these specificity determining positions (SDPs) aids in protein function prediction, drug design and experimental analysis. A number of sequence-based computational methods have been introduced for identifying SDPs; however, their further development and evaluation have been hindered by the limited number of known experimentally determined SDPs. ::: ::: Results: We combine several bioinformatics resources to automate a process, typically undertaken manually, to build a dataset of SDPs. The resulting large dataset, which consists of SDPs in enzymes, enables us to characterize SDPs in terms of their physicochemical and evolution-ary properties. It also facilitates the large-scale evaluation of sequence-based SDP prediction methods. We present a simple sequence-based SDP prediction method, GroupSim, and show that, surprisingly, it is competitive with a representative set of current methods. We also describe ConsWin, a heuristic that considers sequence conservation of neighboring amino acids, and demonstrate that it improves the performance of all methods tested on our large dataset of enzyme SDPs. ::: ::: Availability: Datasets and GroupSim code are available online at http://compbio.cs.princeton.edu/specificity/ ::: ::: Contact: msingh@cs.princeton.edu ::: ::: Supplementary information:Supplementary data are available at Bioinformatics online.
---
paper_title: Secator: A Program for Inferring Protein Subfamilies from Phylogenetic Trees

paper_content:

With the huge increase of protein data, an important problem is to estimate, within a large protein family, the number of sensible subsets for subsequent in-depth structural, functional, and evolutionary analyses. To tackle this problem, we developed a new program, Secator, which implements the principle of an ascending hierarchical method using a distance matrix based on a multiple alignment of protein sequences. Dissimilarity values assigned to the nodes of a deduced phylogenetic tree are partitioned by a new stopping rule introduced to automatically determine the significant dissimilarity values. The quality of the clusters obtained by Secator is verified by a separate Jackknife study. The method is demonstrated on 24 large protein families covering a wide spectrum of structural and sequence conservation and its usefulness and accuracy with real biological data is illustrated on two well-studied protein families (the Sm proteins and the nuclear receptors).
---
paper_title: Sequence analysis Multi-RELIEF : a method to recognize specificity determining residues from multiple sequence alignments using a Machine-Learning approach for feature weighting

paper_content:

Motivation: Identification of residues that account for protein function specificity is crucial, not only for understanding the nature of functional specificity, but also for protein engineering experiments aimed at switching the specificity of an enzyme, regulator or transporter. Available algorithms generally use multiple sequence alignments to identify residue positions conserved within subfamilies but divergent in between. However, many biological examples show a much subtler picture than simple intra-group conservation versus inter-group divergence. ::: ::: Results: We present multi-RELIEF, a novel approach for identifying specificity residues that is based on RELIEF, a state-of-the-art Machine-Learning technique for feature weighting. It estimates the expected ‘local’ functional specificity of residues from an alignment divided in multiple classes. Optionally, 3D structure information is exploited by increasing the weight of residues that have high-weight neighbors. Using ROC curves over a large body of experimental reference data, we show that (a) multi-RELIEF identifies specificity residues for the seven test sets used, (b) incorporating structural information improves prediction for specificity of interaction with small molecules and (c) comparison of multi-RELIEF with four other state-of-the-art algorithms indicates its robustness and best overall performance. ::: ::: Availability: A web-server implementation of multi-RELIEF is available at www.ibi.vu.nl/programs/multirelief. Matlab source code of the algorithm and data sets are available on request for academic use. ::: ::: Contact: [email protected] ::: ::: Supplementary information: Supplemenmtary data are available at Bioinformatics online
---
paper_title: BADASP: predicting functional specificity in protein families using ancestral sequences

paper_content:

Summary: Burst After Duplication with Ancestral Sequence Predictions (BADASP) is a software package for identifying sites that may confer subfamily-specific biological functions in protein families following functional divergence of duplicated proteins. A given protein phylogeny is grouped into subfamilies based on orthology/paralogy relationships and/or user definitions. Ancestral sequences are then predicted from the sequence alignment and the functional specificity is calculated using variants of the Burst After Duplication method, which tests for radical amino acid substitutions following gene duplications that are subsequently conserved. Statistics are output along with subfamily groupings and ancestral sequences for an easy analysis with other packages. ::: ::: Availability: BADASP is freely available from http://www.bioinformatics.rcsi.ie/~redwards/badasp/ ::: ::: Contact: redwards@rcsi.ie ::: ::: Supplementary information: A manual with further details can be downloaded from http://www.bioinformatics.rcsi.ie/~redwards/badasp/
---
paper_title: Classification of protein families and detection of the determinant residues with an improved self-organizing map

paper_content:

Using a SOM (self-organizing map) we can classify sequences within a protein family into subgroups that generally correspond to biological subcategories. These maps tend to show sequence similarity as proximity in the map. Combining maps generated at different levels of resolution, the structure of relations in protein families can be captured that could not otherwise be represented in a single map. The underlying representation of maps enables us to retrieve characteristic sequence patterns for individual subgroups of sequences. Such patterns tend to correspond to functionally important regions. We present a modified SOM algorithm that includes a convergence test that dynamically controls the learning parameters to adapt them to the learning set instead of being fixed and externally optimized by trial and error. Given the variability of protein family size and distribution, the addition of this features is necessary. The method is successfully tested with a number of families. The rab family of small GTPases is used to illustrate the performance of the method.
---
paper_title: Phylogeny-independent detection of functional residues

paper_content:

Motivation: Current projects for the massive characterization of proteomes are generating protein sequences and structures with unknown function. The difficulty of experimentally determining functionally important sites calls for the development of computational methods. The first techniques, based on the search for fully conserved positions in multiple sequence alignments (MSAs), were followed by methods for locating family-dependent conserved positions. These rely on the functional classification implicit in the alignment for locating these positions related with functional specificity. The next obvious step, still scarcely explored, is to detect these positions using a functional classification different from the one implicit in the sequence relationships between the proteins. Here, we present two new methods for locating functional positions which can incorporate an arbitrary external functional classification which may or may not coincide with the one implicit in the MSA. The Xdet method is able to use a functional classification with an associated hierarchy or similarity between functions to locate positions related to that classification. The MCdet method uses multivariate statistical analysis to locate positions responsible for each one of the functions within a multifunctional family. ::: ::: Results: We applied the methods to different cases, illustrating scenarios where there is a disagreement between the functional and the phylogenetic relationships, and demonstrated their usefulness for the phylogeny-independent prediction of functional positions. ::: ::: Availability: All computer programs and datasets used in this work are available from the authors for academic use. ::: ::: Contact: [email protected] ::: ::: Supplementary information: Supplementary data are available at http://pdg.cnb.uam.es/pazos/Xdet_MCdet_Add/
---
paper_title: Automatic clustering of orthologs and in-paralogs from pairwise species comparisons

paper_content:

Orthologs are genes in different species that originate from a single gene in the last common ancestor of these species. Such genes have often retained identical biological roles in the present-day organisms. It is hence important to identify orthologs for transferring functional information between genes in different organisms with a high degree of reliability. For example, orthologs of human proteins are often functionally characterized in model organisms. Unfortunately, orthology analysis between human and e.g. invertebrates is often complex because of large numbers of paralogs within protein families. Paralogs that predate the species split, which we call out-paralogs, can easily be confused with true orthologs. Paralogs that arose after the species split, which we call in-paralogs, however, are bona fide orthologs by definition. Orthologs and in-paralogs are typically detected with phylogenetic methods, but these are slow and difficult to automate. Automatic clustering methods based on two-way best genome-wide matches on the other hand, have so far not separated in-paralogs from out-paralogs effectively. We present a fully automatic method for finding orthologs and in-paralogs from two species. Ortholog clusters are seeded with a two-way best pairwise match, after which an algorithm for adding in-paralogs is applied. The method bypasses multiple alignments and phylogenetic trees, which can be slow and error-prone steps in classical ortholog detection. Still, it robustly detects complex orthologous relationships and assigns confidence values for both orthologs and in-paralogs. The program, called INPARANOID, was tested on all completely sequenced eukaryotic genomes. To assess the quality of INPARANOID results, ortholog clusters were generated from a dataset of worm and mammalian transmembrane proteins, and were compared to clusters derived by manual tree-based ortholog detection methods. This study led to the identification with a high degree of confidence of over a dozen novel worm-mammalian ortholog assignments that were previously undetected because of shortcomings of phylogenetic methods.A WWW server that allows searching for orthologs between human and several fully sequenced genomes is installed at http://www.cgb.ki.se/inparanoid/. This is the first comprehensive resource with orthologs of all fully sequenced eukaryotic genomes. Programs and tables of orthology assignments are available from the same location.
---
paper_title: Sequence comparison by sequence harmony identifies subtype-specific functional sites

paper_content:

Multiple sequence alignments are often used to reveal functionally important residues within a protein family. They can be particularly useful for the identification of key residues that determine functional differences between protein subfamilies. We present a new entropy-based method, Sequence Harmony (SH) that accurately detects subfamily-specific positions from a multiple sequence alignment. The SH algorithm implements a novel formula, able to score compositional differences between subfamilies, without imposing conservation, in a simple manner on an intuitive scale. We compare our method with the most important published methods, i.e. AMAS, TreeDet and SDP-pred, using three well-studied protein families: the receptor-binding domain (MH2) of the Smad family of transcription factors, the Ras-superfamily of small GTPases and the MIP-family of integral membrane transporters. We demonstrate that SH accurately selects known functional sites with higher coverage than the other methods for these test-cases. This shows that compositional differences between protein subfamilies provide sufficient basis for identification of functional sites. In addition, SH selects a number of sites of unknown function that could be interesting candidates for further experimental investigation.
---
paper_title: SDPpred: a tool for prediction of amino acid residues that determine differences in functional specificity of homologous proteins.

paper_content:

SDPpred (Specificity Determining Position prediction) is a tool for prediction of residues in protein sequences that determine the proteins' functional specificity. It is designed for analysis of protein families whose members have biochemically similar but not identical interaction partners (e.g. different substrates for a family of transporters). SDPpred predicts residues that could be responsible for the proteins' choice of their correct interaction partners. The input of SDPpred is a multiple alignment of a protein family divided into a number of specificity groups, within which the interaction partner is believed to be the same. SDPpred does not require information about the secondary or three-dimensional structure of proteins. It produces a set of the alignment positions (specificity determining positions) that determine differences in functional specificity. SDPpred is available at http://math.genebee.msu.ru/~psn/.
---
paper_title: SPEER-SERVER: a web server for prediction of protein specificity determining sites

paper_content:

Sites that show specific conservation patterns within subsets of proteins in a protein family are likely to be involved in the development of functional specificity. These sites, generally termed specificity determining sites (SDS), might play a crucial role in binding to a specific substrate or proteins. Identification of SDS through experimental techniques is a slow, difficult and tedious job. Hence, it is very important to develop efficient computational methods that can more expediently identify SDS. Herein, we present Specificity prediction using amino acids’ Properties, Entropy and Evolution Rate (SPEER)-SERVER, a web server that predicts SDS by analyzing quantitative measures of the conservation patterns of protein sites based on their physico-chemical properties and the heterogeneity of evolutionary changes between and within the protein subfamilies. This web server provides an improved representation of results, adds useful input and output options and integrates a wide range of analysis and data visualization tools when compared with the original standalone version of the SPEER algorithm. Extensive benchmarking finds that SPEER-SERVER exhibits sensitivity and precision performance that, on average, meets or exceeds that of other currently available methods. SPEER-SERVER is available at http://www.hpppi.iicb.res.in/ss/.
---
paper_title: Ensemble approach to predict specificity determinants: benchmarking and validation

paper_content:

BackgroundIt is extremely important and challenging to identify the sites that are responsible for functional specification or diversification in protein families. In this study, a rigorous comparative benchmarking protocol was employed to provide a reliable evaluation of methods which predict the specificity determining sites. Subsequently, three best performing methods were applied to identify new potential specificity determining sites through ensemble approach and common agreement of their prediction results.ResultsIt was shown that the analysis of structural characteristics of predicted specificity determining sites might provide the means to validate their prediction accuracy. For example, we found that for smaller distances it holds true that the more reliable the prediction method is, the closer predicted specificity determining sites are to each other and to the ligand.ConclusionWe observed certain similarities of structural features between predicted and actual subsites which might point to their functional relevance. We speculate that majority of the identified potential specificity determining sites might be indirectly involved in specific interactions and could be ideal target for mutagenesis experiments.
---
paper_title: Functional specificity lies within the properties and evolutionary changes of amino acids.

paper_content:

The rapid increase in the amount of protein sequence data has created a need for automated identification of sites that determine functional specificity among related subfamilies of proteins. A significant fraction of subfamily specific sites are only marginally conserved, which makes it extremely challenging to detect those amino acid changes that lead to functional diversification. To address this critical problem we developed a method named SPEER (specificity prediction using amino acids' properties, entropy and evolution rate) to distinguish specificity determining sites from others. SPEER encodes the conservation patterns of amino acid types using their physico-chemical properties and the heterogeneity of evolutionary changes between and within the subfamilies. To test the method, we compiled a test set containing 13 protein families with known specificity determining sites. Extensive benchmarking by comparing the performance of SPEER with other specificity site prediction algorithms has shown that it performs better in predicting several categories of subfamily specific sites.
---
paper_title: Characterization and prediction of residues determining protein functional specificity

paper_content:

Motivation: Within a homologous protein family, proteins may be grouped into subtypes that share specific functions that are not common to the entire family. Often, the amino acids present in a small number of sequence positions determine each protein's particular function-al specificity. Knowledge of these specificity determining positions (SDPs) aids in protein function prediction, drug design and experimental analysis. A number of sequence-based computational methods have been introduced for identifying SDPs; however, their further development and evaluation have been hindered by the limited number of known experimentally determined SDPs. ::: ::: Results: We combine several bioinformatics resources to automate a process, typically undertaken manually, to build a dataset of SDPs. The resulting large dataset, which consists of SDPs in enzymes, enables us to characterize SDPs in terms of their physicochemical and evolution-ary properties. It also facilitates the large-scale evaluation of sequence-based SDP prediction methods. We present a simple sequence-based SDP prediction method, GroupSim, and show that, surprisingly, it is competitive with a representative set of current methods. We also describe ConsWin, a heuristic that considers sequence conservation of neighboring amino acids, and demonstrate that it improves the performance of all methods tested on our large dataset of enzyme SDPs. ::: ::: Availability: Datasets and GroupSim code are available online at http://compbio.cs.princeton.edu/specificity/ ::: ::: Contact: msingh@cs.princeton.edu ::: ::: Supplementary information:Supplementary data are available at Bioinformatics online.
---
paper_title: Multi-Harmony: detecting functional specificity from sequence alignment

paper_content:

Many protein families contain sub-families with functional specialization, such as binding different ligands or being involved in different protein-protein interactions. A small number of amino acids generally determine functional specificity. The identification of these residues can aid the understanding of protein function and help finding targets for experimental analysis. Here, we present multi-Harmony, an interactive web sever for detecting sub-type-specific sites in proteins starting from a multiple sequence alignment. Combining our Sequence Harmony (SH) and multi-Relief (mR) methods in one web server allows simultaneous analysis and comparison of specificity residues; furthermore, both methods have been significantly improved and extended. SH has been extended to cope with more than two sub-groups. mR has been changed from a sampling implementation to a deterministic one, making it more consistent and user friendly. For both methods Z-scores are reported. The multi-Harmony web server produces a dynamic output page, which includes interactive connections to the Jalview and Jmol applets, thereby allowing interactive analysis of the results. Multi-Harmony is available at http://www.ibi.vu.nl/ programs/shmrwww.
---
paper_title: Phylogeny-independent detection of functional residues

paper_content:

Motivation: Current projects for the massive characterization of proteomes are generating protein sequences and structures with unknown function. The difficulty of experimentally determining functionally important sites calls for the development of computational methods. The first techniques, based on the search for fully conserved positions in multiple sequence alignments (MSAs), were followed by methods for locating family-dependent conserved positions. These rely on the functional classification implicit in the alignment for locating these positions related with functional specificity. The next obvious step, still scarcely explored, is to detect these positions using a functional classification different from the one implicit in the sequence relationships between the proteins. Here, we present two new methods for locating functional positions which can incorporate an arbitrary external functional classification which may or may not coincide with the one implicit in the MSA. The Xdet method is able to use a functional classification with an associated hierarchy or similarity between functions to locate positions related to that classification. The MCdet method uses multivariate statistical analysis to locate positions responsible for each one of the functions within a multifunctional family. ::: ::: Results: We applied the methods to different cases, illustrating scenarios where there is a disagreement between the functional and the phylogenetic relationships, and demonstrated their usefulness for the phylogeny-independent prediction of functional positions. ::: ::: Availability: All computer programs and datasets used in this work are available from the authors for academic use. ::: ::: Contact: [email protected] ::: ::: Supplementary information: Supplementary data are available at http://pdg.cnb.uam.es/pazos/Xdet_MCdet_Add/
---
paper_title: Prediction of functional specificity determinants from protein sequences using log-likelihood ratios

paper_content:

Motivation: A number of methods have been developed to predict functional specificity determinants in protein families based on sequence information. Most of these methods rely on pre-defined functional subgroups. Manual subgroup definition is difficult because of the limited number of experimentally characterized subfamilies with differing specificity, while automatic subgroup partitioning using computational tools is a non-trivial task and does not always yield ideal results. ::: ::: Results: We propose a new approach SPEL (specificity positions by evolutionary likelihood) to detect positions that are likely to be functional specificity determinants. SPEL, which does not require subgroup definition, takes a multiple sequence alignment of a protein family as the only input, and assigns a P-value to every position in the alignment. Positions with low P-values are likely to be important for functional specificity. An evolutionary tree is reconstructed during the calculation, and P-value estimation is based on a random model that involves evolutionary simulations. Evolutionary log-likelihood is chosen as a measure of amino acid distribution at a position. To illustrate the performance of the method, we carried out a detailed analysis of two protein families (LacI/PurR and G protein α subunit), and compared our method with two existing methods (evolutionary trace and mutual information based). All three methods were also compared on a set of protein families with known ligand-bound structures. ::: ::: Availability: SPEL is freely available for non-commercial use. Its pre-compiled versions for several platforms and alignments used in this work are available at ftp://iole.swmed.edu/pub/SPEL/ ::: ::: Contact:grishin@chop.swmed.edu. ::: ::: Supplementary information: Supplementary materials are available at ftp:/iole.swmed.edu/pub/SPEL/
---
paper_title: SPEER-SERVER: a web server for prediction of protein specificity determining sites

paper_content:

Sites that show specific conservation patterns within subsets of proteins in a protein family are likely to be involved in the development of functional specificity. These sites, generally termed specificity determining sites (SDS), might play a crucial role in binding to a specific substrate or proteins. Identification of SDS through experimental techniques is a slow, difficult and tedious job. Hence, it is very important to develop efficient computational methods that can more expediently identify SDS. Herein, we present Specificity prediction using amino acids’ Properties, Entropy and Evolution Rate (SPEER)-SERVER, a web server that predicts SDS by analyzing quantitative measures of the conservation patterns of protein sites based on their physico-chemical properties and the heterogeneity of evolutionary changes between and within the protein subfamilies. This web server provides an improved representation of results, adds useful input and output options and integrates a wide range of analysis and data visualization tools when compared with the original standalone version of the SPEER algorithm. Extensive benchmarking finds that SPEER-SERVER exhibits sensitivity and precision performance that, on average, meets or exceeds that of other currently available methods. SPEER-SERVER is available at http://www.hpppi.iicb.res.in/ss/.
---
paper_title: Predicting functional divergence in protein evolution by site-specific rate shifts.

paper_content:

Most modern tools that analyze protein evolution allow individual sites to mutate at constant rates over the history of the protein family. However, Walter Fitch observed in the 1970s that, if a protein changes its function, the mutability of individual sites might also change. This observation is captured in the "non-homogeneous gamma model", which extracts functional information from gene families by examining the different rates at which individual sites evolve. This model has recently been coupled with structural and molecular biology to identify sites that are likely to be involved in changing function within the gene family. Applying this to multiple gene families highlights the widespread divergence of functional behavior among proteins to generate paralogs and orthologs.
---
paper_title: Functional specificity lies within the properties and evolutionary changes of amino acids.

paper_content:

The rapid increase in the amount of protein sequence data has created a need for automated identification of sites that determine functional specificity among related subfamilies of proteins. A significant fraction of subfamily specific sites are only marginally conserved, which makes it extremely challenging to detect those amino acid changes that lead to functional diversification. To address this critical problem we developed a method named SPEER (specificity prediction using amino acids' properties, entropy and evolution rate) to distinguish specificity determining sites from others. SPEER encodes the conservation patterns of amino acid types using their physico-chemical properties and the heterogeneity of evolutionary changes between and within the subfamilies. To test the method, we compiled a test set containing 13 protein families with known specificity determining sites. Extensive benchmarking by comparing the performance of SPEER with other specificity site prediction algorithms has shown that it performs better in predicting several categories of subfamily specific sites.
---
paper_title: Characterization and prediction of residues determining protein functional specificity

paper_content:

Motivation: Within a homologous protein family, proteins may be grouped into subtypes that share specific functions that are not common to the entire family. Often, the amino acids present in a small number of sequence positions determine each protein's particular function-al specificity. Knowledge of these specificity determining positions (SDPs) aids in protein function prediction, drug design and experimental analysis. A number of sequence-based computational methods have been introduced for identifying SDPs; however, their further development and evaluation have been hindered by the limited number of known experimentally determined SDPs. ::: ::: Results: We combine several bioinformatics resources to automate a process, typically undertaken manually, to build a dataset of SDPs. The resulting large dataset, which consists of SDPs in enzymes, enables us to characterize SDPs in terms of their physicochemical and evolution-ary properties. It also facilitates the large-scale evaluation of sequence-based SDP prediction methods. We present a simple sequence-based SDP prediction method, GroupSim, and show that, surprisingly, it is competitive with a representative set of current methods. We also describe ConsWin, a heuristic that considers sequence conservation of neighboring amino acids, and demonstrate that it improves the performance of all methods tested on our large dataset of enzyme SDPs. ::: ::: Availability: Datasets and GroupSim code are available online at http://compbio.cs.princeton.edu/specificity/ ::: ::: Contact: msingh@cs.princeton.edu ::: ::: Supplementary information:Supplementary data are available at Bioinformatics online.
---
paper_title: Sequence analysis Multi-RELIEF : a method to recognize specificity determining residues from multiple sequence alignments using a Machine-Learning approach for feature weighting

paper_content:

Motivation: Identification of residues that account for protein function specificity is crucial, not only for understanding the nature of functional specificity, but also for protein engineering experiments aimed at switching the specificity of an enzyme, regulator or transporter. Available algorithms generally use multiple sequence alignments to identify residue positions conserved within subfamilies but divergent in between. However, many biological examples show a much subtler picture than simple intra-group conservation versus inter-group divergence. ::: ::: Results: We present multi-RELIEF, a novel approach for identifying specificity residues that is based on RELIEF, a state-of-the-art Machine-Learning technique for feature weighting. It estimates the expected ‘local’ functional specificity of residues from an alignment divided in multiple classes. Optionally, 3D structure information is exploited by increasing the weight of residues that have high-weight neighbors. Using ROC curves over a large body of experimental reference data, we show that (a) multi-RELIEF identifies specificity residues for the seven test sets used, (b) incorporating structural information improves prediction for specificity of interaction with small molecules and (c) comparison of multi-RELIEF with four other state-of-the-art algorithms indicates its robustness and best overall performance. ::: ::: Availability: A web-server implementation of multi-RELIEF is available at www.ibi.vu.nl/programs/multirelief. Matlab source code of the algorithm and data sets are available on request for academic use. ::: ::: Contact: [email protected] ::: ::: Supplementary information: Supplemenmtary data are available at Bioinformatics online
---
paper_title: Multi-Harmony: detecting functional specificity from sequence alignment

paper_content:

Many protein families contain sub-families with functional specialization, such as binding different ligands or being involved in different protein-protein interactions. A small number of amino acids generally determine functional specificity. The identification of these residues can aid the understanding of protein function and help finding targets for experimental analysis. Here, we present multi-Harmony, an interactive web sever for detecting sub-type-specific sites in proteins starting from a multiple sequence alignment. Combining our Sequence Harmony (SH) and multi-Relief (mR) methods in one web server allows simultaneous analysis and comparison of specificity residues; furthermore, both methods have been significantly improved and extended. SH has been extended to cope with more than two sub-groups. mR has been changed from a sampling implementation to a deterministic one, making it more consistent and user friendly. For both methods Z-scores are reported. The multi-Harmony web server produces a dynamic output page, which includes interactive connections to the Jalview and Jmol applets, thereby allowing interactive analysis of the results. Multi-Harmony is available at http://www.ibi.vu.nl/ programs/shmrwww.
---
paper_title: Phylogeny-independent detection of functional residues

paper_content:

Motivation: Current projects for the massive characterization of proteomes are generating protein sequences and structures with unknown function. The difficulty of experimentally determining functionally important sites calls for the development of computational methods. The first techniques, based on the search for fully conserved positions in multiple sequence alignments (MSAs), were followed by methods for locating family-dependent conserved positions. These rely on the functional classification implicit in the alignment for locating these positions related with functional specificity. The next obvious step, still scarcely explored, is to detect these positions using a functional classification different from the one implicit in the sequence relationships between the proteins. Here, we present two new methods for locating functional positions which can incorporate an arbitrary external functional classification which may or may not coincide with the one implicit in the MSA. The Xdet method is able to use a functional classification with an associated hierarchy or similarity between functions to locate positions related to that classification. The MCdet method uses multivariate statistical analysis to locate positions responsible for each one of the functions within a multifunctional family. ::: ::: Results: We applied the methods to different cases, illustrating scenarios where there is a disagreement between the functional and the phylogenetic relationships, and demonstrated their usefulness for the phylogeny-independent prediction of functional positions. ::: ::: Availability: All computer programs and datasets used in this work are available from the authors for academic use. ::: ::: Contact: [email protected] ::: ::: Supplementary information: Supplementary data are available at http://pdg.cnb.uam.es/pazos/Xdet_MCdet_Add/
---
paper_title: AL2CO: calculation of positional conservation in a protein sequence alignment

paper_content:

MOTIVATION ::: Amino acid sequence alignments are widely used in the analysis of protein structure, function and evolutionary relationships. Proteins within a superfamily usually share the same fold and possess related functions. These structural and functional constraints are reflected in the alignment conservation patterns. Positions of functional and/or structural importance tend to be more conserved. Conserved positions are usually clustered in distinct motifs surrounded by sequence segments of low conservation. Poorly conserved regions might also arise from the imperfections in multiple alignment algorithms and thus indicate possible alignment errors. Quantification of conservation by attributing a conservation index to each aligned position makes motif detection more convenient. Mapping these conservation indices onto a protein spatial structure helps to visualize spatial conservation features of the molecule and to predict functionally and/or structurally important sites. Analysis of conservation indices could be a useful tool in detection of potentially misaligned regions and will aid in improvement of multiple alignments. ::: ::: ::: RESULTS ::: We developed a program to calculate a conservation index at each position in a multiple sequence alignment using several methods. Namely, amino acid frequencies at each position are estimated and the conservation index is calculated from these frequencies. We utilize both unweighted frequencies and frequencies weighted using two different strategies. Three conceptually different approaches (entropy-based, variance-based and matrix score-based) are implemented in the algorithm to define the conservation index. Calculating conservation indices for 35522 positions in 284 alignments from SMART database we demonstrate that different methods result in highly correlated (correlation coefficient more than 0.85) conservation indices. Conservation indices show statistically significant correlation between sequentially adjacent positions i and i + j, where j < 13, and averaging of the indices over the window of three positions is optimal for motif detection. Positions with gaps display substantially lower conservation properties. We compare conservation properties of the SMART alignments or FSSP structural alignments to those of the ClustalW alignments. The results suggest that conservation indices should be a valuable tool of alignment quality assessment and might be used as an objective function for refinement of multiple alignments. ::: ::: ::: AVAILABILITY ::: The C code of the AL2CO program and its pre-compiled versions for several platforms as well as the details of the analysis are freely available at ftp://iole.swmed.edu/pub/al2co/.
---
paper_title: SPEER-SERVER: a web server for prediction of protein specificity determining sites

paper_content:

Sites that show specific conservation patterns within subsets of proteins in a protein family are likely to be involved in the development of functional specificity. These sites, generally termed specificity determining sites (SDS), might play a crucial role in binding to a specific substrate or proteins. Identification of SDS through experimental techniques is a slow, difficult and tedious job. Hence, it is very important to develop efficient computational methods that can more expediently identify SDS. Herein, we present Specificity prediction using amino acids’ Properties, Entropy and Evolution Rate (SPEER)-SERVER, a web server that predicts SDS by analyzing quantitative measures of the conservation patterns of protein sites based on their physico-chemical properties and the heterogeneity of evolutionary changes between and within the protein subfamilies. This web server provides an improved representation of results, adds useful input and output options and integrates a wide range of analysis and data visualization tools when compared with the original standalone version of the SPEER algorithm. Extensive benchmarking finds that SPEER-SERVER exhibits sensitivity and precision performance that, on average, meets or exceeds that of other currently available methods. SPEER-SERVER is available at http://www.hpppi.iicb.res.in/ss/.
---
paper_title: Characterization and prediction of residues determining protein functional specificity

paper_content:

Motivation: Within a homologous protein family, proteins may be grouped into subtypes that share specific functions that are not common to the entire family. Often, the amino acids present in a small number of sequence positions determine each protein's particular function-al specificity. Knowledge of these specificity determining positions (SDPs) aids in protein function prediction, drug design and experimental analysis. A number of sequence-based computational methods have been introduced for identifying SDPs; however, their further development and evaluation have been hindered by the limited number of known experimentally determined SDPs. ::: ::: Results: We combine several bioinformatics resources to automate a process, typically undertaken manually, to build a dataset of SDPs. The resulting large dataset, which consists of SDPs in enzymes, enables us to characterize SDPs in terms of their physicochemical and evolution-ary properties. It also facilitates the large-scale evaluation of sequence-based SDP prediction methods. We present a simple sequence-based SDP prediction method, GroupSim, and show that, surprisingly, it is competitive with a representative set of current methods. We also describe ConsWin, a heuristic that considers sequence conservation of neighboring amino acids, and demonstrate that it improves the performance of all methods tested on our large dataset of enzyme SDPs. ::: ::: Availability: Datasets and GroupSim code are available online at http://compbio.cs.princeton.edu/specificity/ ::: ::: Contact: msingh@cs.princeton.edu ::: ::: Supplementary information:Supplementary data are available at Bioinformatics online.
---
paper_title: The Gene Ontology (GO) database and informatics resource.

paper_content:

The Gene Ontology (GO) project (http://www. geneontology.org/) provides structured, controlled vocabularies and classifications that cover several domains of molecular and cellular biology and are freely available for community use in the annotation of genes, gene products and sequences. Many model organism databases and genome annotation groups use the GO and contribute their annotation sets to the GO resource. The GO database integrates the vocabularies and contributed annotations and provides full access to this information in several formats. Members of the GO Consortium continually work collectively, involving outside experts as needed, to expand and update the GO vocabularies. The GO Web resource also provides access to extensive documentation about the GO project and links to applications that use GO data for functional analyses.
---
paper_title: Characterization and prediction of residues determining protein functional specificity

paper_content:

Motivation: Within a homologous protein family, proteins may be grouped into subtypes that share specific functions that are not common to the entire family. Often, the amino acids present in a small number of sequence positions determine each protein's particular function-al specificity. Knowledge of these specificity determining positions (SDPs) aids in protein function prediction, drug design and experimental analysis. A number of sequence-based computational methods have been introduced for identifying SDPs; however, their further development and evaluation have been hindered by the limited number of known experimentally determined SDPs. ::: ::: Results: We combine several bioinformatics resources to automate a process, typically undertaken manually, to build a dataset of SDPs. The resulting large dataset, which consists of SDPs in enzymes, enables us to characterize SDPs in terms of their physicochemical and evolution-ary properties. It also facilitates the large-scale evaluation of sequence-based SDP prediction methods. We present a simple sequence-based SDP prediction method, GroupSim, and show that, surprisingly, it is competitive with a representative set of current methods. We also describe ConsWin, a heuristic that considers sequence conservation of neighboring amino acids, and demonstrate that it improves the performance of all methods tested on our large dataset of enzyme SDPs. ::: ::: Availability: Datasets and GroupSim code are available online at http://compbio.cs.princeton.edu/specificity/ ::: ::: Contact: msingh@cs.princeton.edu ::: ::: Supplementary information:Supplementary data are available at Bioinformatics online.
---
paper_title: Phylogeny-independent detection of functional residues

paper_content:

Motivation: Current projects for the massive characterization of proteomes are generating protein sequences and structures with unknown function. The difficulty of experimentally determining functionally important sites calls for the development of computational methods. The first techniques, based on the search for fully conserved positions in multiple sequence alignments (MSAs), were followed by methods for locating family-dependent conserved positions. These rely on the functional classification implicit in the alignment for locating these positions related with functional specificity. The next obvious step, still scarcely explored, is to detect these positions using a functional classification different from the one implicit in the sequence relationships between the proteins. Here, we present two new methods for locating functional positions which can incorporate an arbitrary external functional classification which may or may not coincide with the one implicit in the MSA. The Xdet method is able to use a functional classification with an associated hierarchy or similarity between functions to locate positions related to that classification. The MCdet method uses multivariate statistical analysis to locate positions responsible for each one of the functions within a multifunctional family. ::: ::: Results: We applied the methods to different cases, illustrating scenarios where there is a disagreement between the functional and the phylogenetic relationships, and demonstrated their usefulness for the phylogeny-independent prediction of functional positions. ::: ::: Availability: All computer programs and datasets used in this work are available from the authors for academic use. ::: ::: Contact: [email protected] ::: ::: Supplementary information: Supplementary data are available at http://pdg.cnb.uam.es/pazos/Xdet_MCdet_Add/
---
paper_title: Prediction of functional specificity determinants from protein sequences using log-likelihood ratios

paper_content:

Motivation: A number of methods have been developed to predict functional specificity determinants in protein families based on sequence information. Most of these methods rely on pre-defined functional subgroups. Manual subgroup definition is difficult because of the limited number of experimentally characterized subfamilies with differing specificity, while automatic subgroup partitioning using computational tools is a non-trivial task and does not always yield ideal results. ::: ::: Results: We propose a new approach SPEL (specificity positions by evolutionary likelihood) to detect positions that are likely to be functional specificity determinants. SPEL, which does not require subgroup definition, takes a multiple sequence alignment of a protein family as the only input, and assigns a P-value to every position in the alignment. Positions with low P-values are likely to be important for functional specificity. An evolutionary tree is reconstructed during the calculation, and P-value estimation is based on a random model that involves evolutionary simulations. Evolutionary log-likelihood is chosen as a measure of amino acid distribution at a position. To illustrate the performance of the method, we carried out a detailed analysis of two protein families (LacI/PurR and G protein α subunit), and compared our method with two existing methods (evolutionary trace and mutual information based). All three methods were also compared on a set of protein families with known ligand-bound structures. ::: ::: Availability: SPEL is freely available for non-commercial use. Its pre-compiled versions for several platforms and alignments used in this work are available at ftp://iole.swmed.edu/pub/SPEL/ ::: ::: Contact:grishin@chop.swmed.edu. ::: ::: Supplementary information: Supplementary materials are available at ftp:/iole.swmed.edu/pub/SPEL/
---
paper_title: SPEER-SERVER: a web server for prediction of protein specificity determining sites

paper_content:

Sites that show specific conservation patterns within subsets of proteins in a protein family are likely to be involved in the development of functional specificity. These sites, generally termed specificity determining sites (SDS), might play a crucial role in binding to a specific substrate or proteins. Identification of SDS through experimental techniques is a slow, difficult and tedious job. Hence, it is very important to develop efficient computational methods that can more expediently identify SDS. Herein, we present Specificity prediction using amino acids’ Properties, Entropy and Evolution Rate (SPEER)-SERVER, a web server that predicts SDS by analyzing quantitative measures of the conservation patterns of protein sites based on their physico-chemical properties and the heterogeneity of evolutionary changes between and within the protein subfamilies. This web server provides an improved representation of results, adds useful input and output options and integrates a wide range of analysis and data visualization tools when compared with the original standalone version of the SPEER algorithm. Extensive benchmarking finds that SPEER-SERVER exhibits sensitivity and precision performance that, on average, meets or exceeds that of other currently available methods. SPEER-SERVER is available at http://www.hpppi.iicb.res.in/ss/.
---
paper_title: Characterization and prediction of residues determining protein functional specificity

paper_content:

Motivation: Within a homologous protein family, proteins may be grouped into subtypes that share specific functions that are not common to the entire family. Often, the amino acids present in a small number of sequence positions determine each protein's particular function-al specificity. Knowledge of these specificity determining positions (SDPs) aids in protein function prediction, drug design and experimental analysis. A number of sequence-based computational methods have been introduced for identifying SDPs; however, their further development and evaluation have been hindered by the limited number of known experimentally determined SDPs. ::: ::: Results: We combine several bioinformatics resources to automate a process, typically undertaken manually, to build a dataset of SDPs. The resulting large dataset, which consists of SDPs in enzymes, enables us to characterize SDPs in terms of their physicochemical and evolution-ary properties. It also facilitates the large-scale evaluation of sequence-based SDP prediction methods. We present a simple sequence-based SDP prediction method, GroupSim, and show that, surprisingly, it is competitive with a representative set of current methods. We also describe ConsWin, a heuristic that considers sequence conservation of neighboring amino acids, and demonstrate that it improves the performance of all methods tested on our large dataset of enzyme SDPs. ::: ::: Availability: Datasets and GroupSim code are available online at http://compbio.cs.princeton.edu/specificity/ ::: ::: Contact: msingh@cs.princeton.edu ::: ::: Supplementary information:Supplementary data are available at Bioinformatics online.
---
paper_title: Phylogeny-independent detection of functional residues

paper_content:

Motivation: Current projects for the massive characterization of proteomes are generating protein sequences and structures with unknown function. The difficulty of experimentally determining functionally important sites calls for the development of computational methods. The first techniques, based on the search for fully conserved positions in multiple sequence alignments (MSAs), were followed by methods for locating family-dependent conserved positions. These rely on the functional classification implicit in the alignment for locating these positions related with functional specificity. The next obvious step, still scarcely explored, is to detect these positions using a functional classification different from the one implicit in the sequence relationships between the proteins. Here, we present two new methods for locating functional positions which can incorporate an arbitrary external functional classification which may or may not coincide with the one implicit in the MSA. The Xdet method is able to use a functional classification with an associated hierarchy or similarity between functions to locate positions related to that classification. The MCdet method uses multivariate statistical analysis to locate positions responsible for each one of the functions within a multifunctional family. ::: ::: Results: We applied the methods to different cases, illustrating scenarios where there is a disagreement between the functional and the phylogenetic relationships, and demonstrated their usefulness for the phylogeny-independent prediction of functional positions. ::: ::: Availability: All computer programs and datasets used in this work are available from the authors for academic use. ::: ::: Contact: [email protected] ::: ::: Supplementary information: Supplementary data are available at http://pdg.cnb.uam.es/pazos/Xdet_MCdet_Add/
---
paper_title: SPEER-SERVER: a web server for prediction of protein specificity determining sites

paper_content:

Sites that show specific conservation patterns within subsets of proteins in a protein family are likely to be involved in the development of functional specificity. These sites, generally termed specificity determining sites (SDS), might play a crucial role in binding to a specific substrate or proteins. Identification of SDS through experimental techniques is a slow, difficult and tedious job. Hence, it is very important to develop efficient computational methods that can more expediently identify SDS. Herein, we present Specificity prediction using amino acids’ Properties, Entropy and Evolution Rate (SPEER)-SERVER, a web server that predicts SDS by analyzing quantitative measures of the conservation patterns of protein sites based on their physico-chemical properties and the heterogeneity of evolutionary changes between and within the protein subfamilies. This web server provides an improved representation of results, adds useful input and output options and integrates a wide range of analysis and data visualization tools when compared with the original standalone version of the SPEER algorithm. Extensive benchmarking finds that SPEER-SERVER exhibits sensitivity and precision performance that, on average, meets or exceeds that of other currently available methods. SPEER-SERVER is available at http://www.hpppi.iicb.res.in/ss/.
---
paper_title: Ensemble approach to predict specificity determinants: benchmarking and validation

paper_content:

BackgroundIt is extremely important and challenging to identify the sites that are responsible for functional specification or diversification in protein families. In this study, a rigorous comparative benchmarking protocol was employed to provide a reliable evaluation of methods which predict the specificity determining sites. Subsequently, three best performing methods were applied to identify new potential specificity determining sites through ensemble approach and common agreement of their prediction results.ResultsIt was shown that the analysis of structural characteristics of predicted specificity determining sites might provide the means to validate their prediction accuracy. For example, we found that for smaller distances it holds true that the more reliable the prediction method is, the closer predicted specificity determining sites are to each other and to the ligand.ConclusionWe observed certain similarities of structural features between predicted and actual subsites which might point to their functional relevance. We speculate that majority of the identified potential specificity determining sites might be indirectly involved in specific interactions and could be ideal target for mutagenesis experiments.
---
paper_title: Functional specificity lies within the properties and evolutionary changes of amino acids.

paper_content:

The rapid increase in the amount of protein sequence data has created a need for automated identification of sites that determine functional specificity among related subfamilies of proteins. A significant fraction of subfamily specific sites are only marginally conserved, which makes it extremely challenging to detect those amino acid changes that lead to functional diversification. To address this critical problem we developed a method named SPEER (specificity prediction using amino acids' properties, entropy and evolution rate) to distinguish specificity determining sites from others. SPEER encodes the conservation patterns of amino acid types using their physico-chemical properties and the heterogeneity of evolutionary changes between and within the subfamilies. To test the method, we compiled a test set containing 13 protein families with known specificity determining sites. Extensive benchmarking by comparing the performance of SPEER with other specificity site prediction algorithms has shown that it performs better in predicting several categories of subfamily specific sites.
---
paper_title: Characterization and prediction of residues determining protein functional specificity

paper_content:

Motivation: Within a homologous protein family, proteins may be grouped into subtypes that share specific functions that are not common to the entire family. Often, the amino acids present in a small number of sequence positions determine each protein's particular function-al specificity. Knowledge of these specificity determining positions (SDPs) aids in protein function prediction, drug design and experimental analysis. A number of sequence-based computational methods have been introduced for identifying SDPs; however, their further development and evaluation have been hindered by the limited number of known experimentally determined SDPs. ::: ::: Results: We combine several bioinformatics resources to automate a process, typically undertaken manually, to build a dataset of SDPs. The resulting large dataset, which consists of SDPs in enzymes, enables us to characterize SDPs in terms of their physicochemical and evolution-ary properties. It also facilitates the large-scale evaluation of sequence-based SDP prediction methods. We present a simple sequence-based SDP prediction method, GroupSim, and show that, surprisingly, it is competitive with a representative set of current methods. We also describe ConsWin, a heuristic that considers sequence conservation of neighboring amino acids, and demonstrate that it improves the performance of all methods tested on our large dataset of enzyme SDPs. ::: ::: Availability: Datasets and GroupSim code are available online at http://compbio.cs.princeton.edu/specificity/ ::: ::: Contact: msingh@cs.princeton.edu ::: ::: Supplementary information:Supplementary data are available at Bioinformatics online.
---
paper_title: Sequence analysis Multi-RELIEF : a method to recognize specificity determining residues from multiple sequence alignments using a Machine-Learning approach for feature weighting

paper_content:

Motivation: Identification of residues that account for protein function specificity is crucial, not only for understanding the nature of functional specificity, but also for protein engineering experiments aimed at switching the specificity of an enzyme, regulator or transporter. Available algorithms generally use multiple sequence alignments to identify residue positions conserved within subfamilies but divergent in between. However, many biological examples show a much subtler picture than simple intra-group conservation versus inter-group divergence. ::: ::: Results: We present multi-RELIEF, a novel approach for identifying specificity residues that is based on RELIEF, a state-of-the-art Machine-Learning technique for feature weighting. It estimates the expected ‘local’ functional specificity of residues from an alignment divided in multiple classes. Optionally, 3D structure information is exploited by increasing the weight of residues that have high-weight neighbors. Using ROC curves over a large body of experimental reference data, we show that (a) multi-RELIEF identifies specificity residues for the seven test sets used, (b) incorporating structural information improves prediction for specificity of interaction with small molecules and (c) comparison of multi-RELIEF with four other state-of-the-art algorithms indicates its robustness and best overall performance. ::: ::: Availability: A web-server implementation of multi-RELIEF is available at www.ibi.vu.nl/programs/multirelief. Matlab source code of the algorithm and data sets are available on request for academic use. ::: ::: Contact: [email protected] ::: ::: Supplementary information: Supplemenmtary data are available at Bioinformatics online
---

Title: A survey on prediction of specificity-determining sites in proteins

Section 1: INTRODUCTION
Description 1: Introduce the field and context of specificity-determining sites (SDS) in proteins, the evolutionary theories behind them, and the various challenges in identifying these sites computationally.

Section 2: STATUS OF THE FIELD Existing algorithms for SDS prediction
Description 2: Discuss the status and development of various computational approaches used for SDS prediction, including early approaches, entropy-based methods, evolutionary rate-based methods, automated subgrouping, 3D structure-based methods, machine learning techniques, and ensemble approaches.

Section 3: Performance of SDS-predicting algorithms
Description 3: Assess the performance of different SDS prediction algorithms using various metrics like sensitivity, accuracy, F score, MCC, and area under the curve (AUC).

Section 4: Effective benchmarking of SDS prediction algorithms
Description 4: Present an exhaustive and objective benchmarking analysis of the existing SDS prediction methods using multiple data sets of various sizes and biological functions.

Section 5: Overall performance of SDS prediction programs
Description 5: Analyze the overall performance of the reviewed SDS prediction programs, focusing on their strengths and weaknesses in predicting different types of SDS and their efficiency in various conservation spectra.

Section 6: Comparison of performance based on biological functions
Description 6: Compare the performance of SDS prediction programs across different biological functions and data sets, including enzyme classes and non-enzyme classes.

Section 7: Influence of subfamily number
Description 7: Examine how the number of subfamilies in a protein sequence alignment affects the variability and complexity of identifying SDS, and the performance of prediction algorithms in such scenarios.

Section 8: CONCLUSION
Description 8: Summarize the findings from the review, detailing the progress made in the field, the best performing approaches, and the future directions for both computational and experimental efforts in SDS prediction.

Section 9: SUPPLEMENTARY DATA
Description 9: Mention the availability of supplementary data online for extended analysis and additional details pertaining to the reviewed prediction methods and benchmarking studies.

Section 10: Key Points
Description 10: Highlight key points from the review, summarizing critical aspects and the importance of predicting specificity-determining sites in proteins.