Automatic methods for predicting functionally important residues

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.     

Subfamily logos: visualization of sequence deviations at alignment positions with high information content

Background  

Recognition of relevant sequence deviations can be valuable for elucidating functional differences between protein subfamilies. Interesting residues at highly conserved positions can then be mutated and experimentally analyzed. However, identification of such sites is tedious because automated approaches are scarce.     

Integration of Evolutionary Features for the Identification of Functionally Important Residues in Major Facilitator Superfamily Transporters

The identification of functionally important residues is an important challenge for understanding the molecular mechanisms of proteins. Membrane protein transporters operate two-state allosteric conformational changes using functionally important cooperative residues that mediate long-range communication from the substrate binding site to the translocation pathway. In this study, we identified functionally important cooperative residues of membrane protein transporters by integrating sequence conservation and co-evolutionary information. A newly derived evolutionary feature, the co-evolutionary coupling number, was introduced to measure the connectivity of co-evolving residue pairs and was integrated with the sequence conservation score. We tested this method on three Major Facilitator Superfamily (MFS) transporters, LacY, GlpT, and EmrD. MFS transporters are an important family of membrane protein transporters, which utilize diverse substrates, catalyze different modes of transport using unique combinations of functional residues, and have enough characterized functional residues to validate the performance of our method. We found that the conserved cores of evolutionarily coupled residues are involved in specific substrate recognition and translocation of MFS transporters. Furthermore, a subset of the residues forms an interaction network connecting functional sites in the protein structure. We also confirmed that our method is effective on other membrane protein transporters. Our results provide insight into the location of functional residues important for the molecular mechanisms of membrane protein transporters.     

Partially-supervised protein subclass discovery with simultaneous annotation of functional residues

Background  

The study of functional subfamilies of protein domain families and the identification of the residues which determine substrate specificity is an important question in the analysis of protein domains. One way to address this question is the use of clustering methods for protein sequence data and approaches to predict functional residues based on such clusterings. The locations of putative functional residues in known protein structures provide insights into how different substrate specificities are reflected on the protein structure level.     

ConSurf: an algorithmic tool for the identification of functional regions in proteins by surface mapping of phylogenetic information

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/ approximately rony Copyright 2001 Academic Press.     

CASTOR: clustering algorithm for sequence taxonomical organization and relationships.

Given a set of related proteins, two important problems in biology are the inference of protein subsets such that members of one subset share a common function and the identification of protein regions that possess functional significance. The former is typically approached by hierarchical bottom-up clustering based on pairwise sequence similarity and various linkage rules. The latter is typically approached in a supervised manner, based on global multiple sequence alignment. However, the two problems are inextricably linked, since functional subsets are usually characterized by distinctive functional regions. This paper introduces CASTOR, an automatic and unsupervised system that addresses both problems simultaneously and efficiently. It identifies protein regions that are likely to have functional significance by discovering and refining statistically significant motifs. It infers likely functional protein subsets and their relationships based on the presence of the discovered motifs in a top-down and recursive manner, allowing the identification of both hierarchical and nonhierarchical subset relationships. This is, to our knowledge, the first system that approaches both problems simultaneously in a top-down, systematic manner. CASTOR's performance is evaluated against the G-protein coupled receptor superfamily. The identified protein regions lead to a taxonomical organization of this superfamily that is in remarkable agreement with a biologically motivated one and which outperforms those produced by bottom-up clustering methods. We also find that conventional hierarchical representations may fail to accurately describe the complexity of evolutionary development responsible for the final organization of a complex protein family. In particular, many functional relationships governing distant subfamilies of such a protein family may not be represented hierarchically.     

Isofunctional Protein Subfamily Detection Using Data Integration and Spectral Clustering

As increasingly more genomes are sequenced, the vast majority of proteins may only be annotated computationally, given experimental investigation is extremely costly. This highlights the need for computational methods to determine protein functions quickly and reliably. We believe dividing a protein family into subtypes which share specific functions uncommon to the whole family reduces the function annotation problem’s complexity. Hence, this work’s purpose is to detect isofunctional subfamilies inside a family of unknown function, while identifying differentiating residues. Similarity between protein pairs according to various properties is interpreted as functional similarity evidence. Data are integrated using genetic programming and provided to a spectral clustering algorithm, which creates clusters of similar proteins. The proposed framework was applied to well-known protein families and to a family of unknown function, then compared to ASMC. Results showed our fully automated technique obtained better clusters than ASMC for two families, besides equivalent results for other two, including one whose clusters were manually defined. Clusters produced by our framework showed great correspondence with the known subfamilies, besides being more contrasting than those produced by ASMC. Additionally, for the families whose specificity determining positions are known, such residues were among those our technique considered most important to differentiate a given group. When run with the crotonase and enolase SFLD superfamilies, the results showed great agreement with this gold-standard. Best results consistently involved multiple data types, thus confirming our hypothesis that similarities according to different knowledge domains may be used as functional similarity evidence. Our main contributions are the proposed strategy for selecting and integrating data types, along with the ability to work with noisy and incomplete data; domain knowledge usage for detecting subfamilies in a family with different specificities, thus reducing the complexity of the experimental function characterization problem; and the identification of residues responsible for specificity.     

Functional diversity of Csk, Chk, and Src SH2 domains due to a single residue variation

The C-terminal Src kinase (Csk) family of protein tyrosine kinases contains two members: Csk and Csk homologous kinase (Chk). Both phosphorylate and inactivate Src family kinases. Recent reports suggest that the Src homology (SH) 2 domains of Csk and Chk may bind to different phosphoproteins, which provides a basis for different cellular functions for Csk and Chk. To verify and characterize such a functional divergence, we compared the binding properties of the Csk, Chk, and Src SH2 domains and investigated the structural basis for the functional divergence. First, the study demonstrated striking functional differences between the Csk and Chk SH2 domains and revealed functional similarities between the Chk and Src SH2 domains. Second, structural analysis and mutagenic studies revealed that the functional differences among the three SH2 domains were largely controlled by one residue, Glu127 in Csk, Ile167 in Chk, and Lys200 in Src. Mutating these residues in the Csk or Chk SH2 domain to the Src counterpart resulted in dramatic gain of function similar to Src SH2 domain, whereas mutating Lys200 in Src SH2 domain to Glu (the Csk counterpart) resulted in loss of Src SH2 function. Third, a single point mutation of E127K rendered Csk responsive to activation by a Src SH2 domain ligand. Finally, the optimal phosphopeptide sequence for the Chk SH2 domain was determined. These results provide a compelling explanation for the functional differences between two homologous protein tyrosine kinases and reveal a new structure-function relationship for the SH2 domains.     

Functional specificity lies within the properties and evolutionary changes of amino acids

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.     

Using evolutionary rates to investigate protein functional divergence and conservation. A case study of the carbonic anhydrases

Functional constraints on proteins limit their evolutionary rates at specific sites. These constraints allow for the interpretation of conserved residues and sites with a rate change as those most likely underlying the functional similarities and differences among protein subfamilies, respectively. This study describes new likelihood-ratio tests (LRTs) that complement existing ones for the identification of both conserved and rate change sites. These identifications are validated by the recovery of residues that are known from existing biochemical and structural information to be critical for the functional similarities and differences among carbonic anhydrases (CAs). In combination with this other information, these LRTs also support a unique antioxidant defense role for the puzzling CA III. As illustrated by the CAs, these LRTs, in combination with other biological evidence, offer a powerful and cost-effective approach for testing hypotheses, making predictions, and designing experiments in protein functional studies.     

A Combinatorial Approach to Detect Coevolved Amino Acid Networks in Protein Families of Variable Divergence

Communication between distant sites often defines the biological role of a protein: amino acid long-range interactions are as important in binding specificity, allosteric regulation and conformational change as residues directly contacting the substrate. The maintaining of functional and structural coupling of long-range interacting residues requires coevolution of these residues. Networks of interaction between coevolved residues can be reconstructed, and from the networks, one can possibly derive insights into functional mechanisms for the protein family. We propose a combinatorial method for mapping conserved networks of amino acid interactions in a protein which is based on the analysis of a set of aligned sequences, the associated distance tree and the combinatorics of its subtrees. The degree of coevolution of all pairs of coevolved residues is identified numerically, and networks are reconstructed with a dedicated clustering algorithm. The method drops the constraints on high sequence divergence limiting the range of applicability of the statistical approaches previously proposed. We apply the method to four protein families where we show an accurate detection of functional networks and the possibility to treat sets of protein sequences of variable divergence.     

A method to predict residues conferring functional differences between related proteins: application to MAP kinase pathways

Physicochemical properties are potentially useful in predicting functional differences between aligned protein subfamilies. We present a method that considers physicochemical properties from ancestral sequences predicted to have given rise to the subfamilies of interest by gene duplication. Comparison between two map kinases subfamilies, p38 and ERK, revealed a region that had an excess of change in properties after gene duplication followed by conservation within the two subfamilies. This region corresponded to that experimentally defined as important for substrate and pathway specificity. The derived scores for the region of interest were found to differ significantly in their distribution compared to the rest of the protein when the Kolmogorov-Smirnov test was applied (p = 0.005). Thus, the incorporation of ancestral physicochemical properties is useful in predicting functional differences between protein subfamilies. In addition, the method was applied to the MKK and MAPK components of the p38 and JNK pathways. These proteins showed a similar pattern in their evolution and regions predicted to confer functional differences are discussed.     

Assignment of protein function and discovery of novel nucleolar proteins based on automatic analysis of MEDLINE

Attribution of the most probable functions to proteins identified by proteomics is a significant challenge that requires extensive literature analysis. We have developed a system for automated prediction of implicit and explicit biologically meaningful functions for a proteomics study of the nucleolus. This approach uses a set of vocabulary terms to map and integrate the information from the entire MEDLINE database. Based on a combination of cross-species sequence homology searches and the corresponding literature, our approach facilitated the direct association between sequence data and information from biological texts describing function. Comparison of our automated functional assignment to manual annotation demonstrated our method to be highly effective. To establish the sensitivity, we defined the functional subtleties within a family containing a highly conserved sequence. Clustering of the DEAD-box protein family of RNA helicases confirmed that these proteins shared similar morphology although functional subfamilies were accurately identified by our approach. We visualized the nucleolar proteome in terms of protein functions using multi-dimensional scaling, showing functional associations between nucleolar proteins that were not previously realized. Finally, by clustering the functional properties of the established nucleolar proteins, we predicted novel nucleolar proteins. Subsequently, nonproteomics studies confirmed the predictions of previously unidentified nucleolar proteins.     

Distinguishing structural and functional restraints in evolution in order to identify interaction sites

Structural genomics projects are producing many three-dimensional structures of proteins that have been identified only from their gene sequences. It is therefore important to develop computational methods that will predict sites involved in productive intermolecular interactions that might give clues about functions. Techniques based on evolutionary conservation of amino acids have the advantage over physiochemical methods in that they are more general. However, the majority of techniques neither use all available structural and sequence information, nor are able to distinguish between evolutionary restraints that arise from the need to maintain structure and those that arise from function. Three methods to identify evolutionary restraints on protein sequence and structure are described here. The first identifies those residues that have a higher degree of conservation than expected: this is achieved by comparing for each amino acid position the sequence conservation observed in the homologous family of proteins with the degree of conservation predicted on the basis of amino acid type and local environment. The second uses information theory to identify those positions where environment-specific substitution tables make poor predictions of the overall amino acid substitution pattern. The third method identifies those residues that have highly conserved positions when three-dimensional structures of proteins in a homologous family are superposed. The scores derived from these methods are mapped onto the protein three-dimensional structures and contoured, allowing identification clusters of residues with strong evolutionary restraints that are sites of interaction in proteins involved in a variety of functions. Our method differs from other published techniques by making use of structural information to identify restraints that arise from the structure of the protein and differentiating these restraints from others that derive from intermolecular interactions that mediate functions in the whole organism.     

Predicting functionally important residues from sequence conservation

MOTIVATION: All residues in a protein are not equally important. Some are essential for the proper structure and function of the protein, whereas others can be readily replaced. Conservation analysis is one of the most widely used methods for predicting these functionally important residues in protein sequences. RESULTS: We introduce an information-theoretic approach for estimating sequence conservation based on Jensen-Shannon divergence. We also develop a general heuristic that considers the estimated conservation of sequentially neighboring sites. In large-scale testing, we demonstrate that our combined approach outperforms previous conservation-based measures in identifying functionally important residues; in particular, it is significantly better than the commonly used Shannon entropy measure. We find that considering conservation at sequential neighbors improves the performance of all methods tested. Our analysis also reveals that many existing methods that attempt to incorporate the relationships between amino acids do not lead to better identification of functionally important sites. Finally, we find that while conservation is highly predictive in identifying catalytic sites and residues near bound ligands, it is much less effective in identifying residues in protein-protein interfaces. AVAILABILITY: Data sets and code for all conservation measures evaluated are available at http://compbio.cs.princeton.edu/conservation/     

Prediction of functional sites in proteins using conserved functional group analysis

A detailed knowledge of a protein's functional site is an absolute prerequisite for understanding its mode of action at the molecular level. However, the rapid pace at which sequence and structural information is being accumulated for proteins greatly exceeds our ability to determine their biochemical roles experimentally. As a result, computational methods are required which allow for the efficient processing of the evolutionary information contained in this wealth of data, in particular that related to the nature and location of functionally important sites and residues. The method presented here, referred to as conserved functional group (CFG) analysis, relies on a simplified representation of the chemical groups found in amino acid side-chains to identify functional sites from a single protein structure and a number of its sequence homologues. We show that CFG analysis can fully or partially predict the location of functional sites in approximately 96% of the 470 cases tested and that, unlike other methods available, it is able to tolerate wide variations in sequence identity. In addition, we discuss its potential in a structural genomics context, where automation, scalability and efficiency are critical, and an increasing number of protein structures are determined with no prior knowledge of function. This is exemplified by our analysis of the hypothetical protein Ydde_Ecoli, whose structure was recently solved by members of the North East Structural Genomics consortium. Although the proposed active site for this protein needs to be validated experimentally, this example illustrates the scope of CFG analysis as a general tool for the identification of residues likely to play an important role in a protein's biochemical function. Thus, our method offers a convenient solution to rapidly and automatically process the vast amounts of data that are beginning to emerge from structural genomics projects.     

The ASPP interaction network: electrostatic differentiation between pro‐ and anti‐apoptotic proteins

The ASPP proteins are apoptosis regulators: ASPP1 and ASPP2 promote, while iASPP inhibits, apoptosis. The mechanism by which these different outcomes are achieved is still unknown. The C‐terminal ankyrin repeats and SH3 domain (ANK‐SH3) mediate the interactions of the ASPP proteins with major apoptosis regulators such as p53, Bcl‐2, and NFκB. The structure of the complex between ASPP2_ANK‐SH3 and the core domain of p53 (p53CD) was previously determined. We have recently characterized the individual interactions of ASPP2_ANK‐SH3 with Bcl‐2 and NFκB, as well as a regulatory intramolecular interaction with the proline rich domain of ASPP2. Here we compared the ASPP interactions at two levels: ASPP2_ANK‐SH3 with different proteins, and different ASPP family members with each protein partner. We found that the binding sites of ASPP2 to p53CD, Bcl‐2, and NFκB are different, yet lie on the same face of ASPP2_ANK‐SH3. The intramolecular binding site to the proline rich domain overlaps the three intermolecular binding sites. To reveal the basis of functional diversity in the ASPP family, we compared their protein‐binding domains. A subset of surface‐exposed residues differentiates ASPP1 and ASPP2 from iASPP: ASPP1/2 are more negatively charged in specific residues that contact positively charged residues of p53CD, Bcl‐2, and NFκB. We also found a gain of positive charge at the non‐protein binding face of ASPP1/2, suggesting a role in electrostatic direction towards the negatively charged protein binding face. The electrostatic differences in binding interfaces between the ASPP proteins may be one of the causes for their different function. Copyright © 2010 John Wiley & Sons, Ltd.     

The integrase family of tyrosine recombinases: evolution of a conserved active site domain.

The integrases are a diverse family of tyrosine recombinases which rearrange DNA duplexes by means of conservative site-specific recombination reactions. Members of this family, of which the well-studied lambda Int protein is the prototype, were previously found to share four strongly conserved residues, including an active site tyrosine directly involved in transesterification. However, few additional sequence similarities were found in the original group of 27 proteins. We have now identified a total of 81 members of the integrase family deposited in the databases. Alignment and comparisons of these sequences combined with an evolutionary analysis aided in identifying broader sequence similarities and clarifying the possible functions of these conserved residues. This analysis showed that members of the family aggregate into subfamilies which are consistent with their biological roles; these subfamilies have significant levels of sequence similarity beyond the four residues previously identified. It was also possible to map the location of conserved residues onto the available crystal structures; most of the conserved residues cluster in the predicted active site cleft. In addition, these results offer clues into an apparent discrepancy between the mechanisms of different subfamilies of integrases.     

Improvement in protein functional site prediction by distinguishing structural and functional constraints on protein family evolution using computational design

The prediction of functional sites in newly solved protein structures is a challenge for computational structural biology. Most methods for approaching this problem use evolutionary conservation as the primary indicator of the location of functional sites. However, sequence conservation reflects not only evolutionary selection at functional sites to maintain protein function, but also selection throughout the protein to maintain the stability of the folded state. To disentangle sequence conservation due to protein functional constraints from sequence conservation due to protein structural constraints, we use all atom computational protein design methodology to predict sequence profiles expected under solely structural constraints, and to compute the free energy difference between the naturally occurring amino acid and the lowest free energy amino acid at each position. We show that functional sites are more likely than non-functional sites to have computed sequence profiles which differ significantly from the naturally occurring sequence profiles and to have residues with sub-optimal free energies, and that incorporation of these two measures improves sequence based prediction of protein functional sites. The combined sequence and structure based functional site prediction method has been implemented in a publicly available web server.