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.     

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

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.     

Correlated substitution analysis and the prediction of amino acid structural contacts

It has long been suspected that analysis of correlated amino acid substitutions should uncover pairs or clusters of sites that are spatially proximal in mature protein structures. Accordingly, methods based on different mathematical principles such as information theory, correlation coefficients and maximum likelihood have been developed to identify co-evolving amino acids from multiple sequence alignments. Sets of pairs of sites whose behaviour is identified by these methods as correlated are often significantly enriched in pairs of spatially proximal residues. However, relatively high levels of false-positive predictions typically render such methods, in isolation, of little use in the ab initio prediction of protein structure. Misleading signal (or problems with the estimation of significance levels) can be caused by phylogenetic correlations between homologous sequences and from correlation due to factors other than spatial proximity (for example, correlation of sites which are not spatially close but which are involved in common functional properties of the protein). In recent years, several workers have suggested that information from correlated substitutions should be combined with other sources of information (secondary structure, solvent accessibility, evolutionary rates) in an attempt to reduce the proportion of false-positive predictions. We review methods for the detection of correlated amino acid substitutions, compare their relative performance in contact prediction and predict future directions in the field.     

Disentangling Direct from Indirect Co-Evolution of Residues in Protein Alignments

Predicting protein structure from primary sequence is one of the ultimate challenges in computational biology. Given the large amount of available sequence data, the analysis of co-evolution, i.e., statistical dependency, between columns in multiple alignments of protein domain sequences remains one of the most promising avenues for predicting residues that are contacting in the structure. A key impediment to this approach is that strong statistical dependencies are also observed for many residue pairs that are distal in the structure. Using a comprehensive analysis of protein domains with available three-dimensional structures we show that co-evolving contacts very commonly form chains that percolate through the protein structure, inducing indirect statistical dependencies between many distal pairs of residues. We characterize the distributions of length and spatial distance traveled by these co-evolving contact chains and show that they explain a large fraction of observed statistical dependencies between structurally distal pairs. We adapt a recently developed Bayesian network model into a rigorous procedure for disentangling direct from indirect statistical dependencies, and we demonstrate that this method not only successfully accomplishes this task, but also allows contacts with weak statistical dependency to be detected. To illustrate how additional information can be incorporated into our method, we incorporate a phylogenetic correction, and we develop an informative prior that takes into account that the probability for a pair of residues to contact depends strongly on their primary-sequence distance and the amount of conservation that the corresponding columns in the multiple alignment exhibit. We show that our model including these extensions dramatically improves the accuracy of contact prediction from multiple sequence alignments.     

Co-evolving residues in membrane proteins

MOTIVATION: The analysis of co-evolving residues has been exhaustively evaluated for the prediction of intramolecular amino acid contacts in soluble proteins. Although a variety of different methods for the detection of these co-evolving residues have been developed, the fraction of correctly predicted contacts remained insufficient for their reliable application in the construction of structural models. Membrane proteins, which constitute between one-fourth and one-third of all proteins in an organism, were only considered in few individual case studies. RESULTS: We present the first general study of correlated mutations in alpha-helical membrane proteins. Using seven different prediction algorithms, we extracted co-evolving residues for 14 membrane proteins having a solved 3D structure. On average, distances between correlated pairs of residues lying on different transmembrane segments were found to be significantly smaller compared to a random prediction. Covariation of residues was frequently found in direct sequence neighborhood to helix-helix contacts. Based on the results obtained from individual prediction methods, we constructed a consensus prediction for every protein in the dataset that combines obtained correlations from different prediction algorithms and simultaneously removes likely false positives. Using this consensus prediction, 53% of all predicted residue pairs were found within one helix turn of an observed helix-helix contact. Based on the combination of co-evolving residues detected with the four best prediction algorithms, interacting helices could be predicted with a specificity of 83% and sensitivity of 42%. AVAILABILITY: http://webclu.bio.wzw.tum.de/helixcorr/     

Analysis of homodimeric protein interfaces by graph-spectral methods

The quaternary structures impart structural and functional credibility to proteins. In a multi-subunit protein, it is important to understand the factors that drive the association or dissociation of the subunits. It is a well known fact that both hydrophobic and charged interactions contribute to the stability of the protein interface. The interface residues are also known to be highly conserved. Though they are buried in the oligomer, these residues are either exposed or partially exposed in the monomer. It is felt that a systematic and objective method of identifying interface clusters and their analysis can significantly contribute to the identification of a residue or a collection of residues important for oligomerization. Recently, we have applied the techniques of graph-spectral methods to a variety of problems related to protein structure and folding. A major advantage of this methodology is that the problem is viewed from a global protein topology point of view rather than localized regions of the protein structure. In the present investigation, we have applied the methods of graph-spectral analysis to identify side chain clusters at the interface and the centers of these clusters in a set of homodimeric proteins. These clusters are analyzed in terms of properties such as amino acid composition, accessibility to solvent and conservation of residues. Interesting results such as participation of charged and aromatic residues like arginine, glutamic acid, histidine, phenylalanine and tyrosine, consistent with earlier investigations, have emerged from these analyses. Important additional information is that the residues involved are a part of a cluster(s) and that they are sequentially distant residues which have come closer to each other in the three-dimensional structure of the protein. These residues can easily be detected using our graph-spectral algorithm. This method has also been used to identify important residues ('hot spots') in dimerization and also to detect dimerization sites on the monomer. The residues predicted using the present algorithm have correlated well with the experiments indicating the efficacy of this method in predicting residues involved in dimer stability.     

Structure-based rebuilding of coevolutionary information reveals functional modules in rhodopsin structure

Correlated mutation analysis (CMA) has been used to investigate protein functional sites. However, CMA has suffered from low signal-to-noise ratio caused by meaningless phylogenetic signals or structural constraints. We present a new method, Structure-based Correlated Mutation Analysis (SCMA), which encodes coevolution scores into a protein structure network. A path-based network model is adapted to describe information transfer between residues, and the statistical significance is estimated by network shuffling. This model intrinsically assumes that residues in physical contact have a more reliable coevolution score than distant residues, and that coevolution in distant residues likely arises from a series of contacting and coevolving residues. In addition, coevolutionary coupling is statistically controlled to remove the structural effects. When applied to the rhodopsin structure, the SCMA method identified a much higher percentage of functional residues than the typical coevolution score (61% vs. 22%). In addition, statistically significant residues are used to construct the coevolved residue-residue subnetwork. The network has one highly connected node (retinal bound Lys296), indicating that Lys296 can induce and regulate most other coevolved residues in a variety of locations. The coevolved network consists of a few modular clusters which have distinct functional roles. This article is part of a Special Issue entitled: Computational Methods for Protein Interaction and Structural Prediction.     

Inferring functional information from domain co-evolution

MOTIVATION: Co-evolution is a powerful mechanism for understanding protein function. Prior work in this area has shown that co-evolving proteins are more likely to share the same function than those that do not because of functional constraints. Many of the efforts founded on this observation, however, are at the level of entire sequences, implicitly assuming that the complete protein sequence follows a single evolutionary trajectory. Since it is well known that a domain can exist in various contexts, this assumption is not valid for numerous multi-domain proteins. Motivated by these observations, we introduce a novel technique called Coevolutionary-Matrix that captures co-evolution between regions of two proteins. Instead of using existing domain information, the method exploits residue-level conservation to identify co-evolving regions that might correspond to domains. RESULTS: We show that the Coevolutionary-Matrix method can detect greater number of known functional associations for the Escherichia coli proteins when compared with earlier implementations of phylogenetic profiles. Furthermore, co-evolving regions of proteins detected by our method enable us to make hypotheses about their specific functions, many of which are supported by existing biochemical studies.     

Structural clusters of evolutionary trace residues are statistically significant and common in proteins

Given the massive increase in the number of new sequences and structures, a critical problem is how to integrate these raw data into meaningful biological information. One approach, the Evolutionary Trace, or ET, uses phylogenetic information to rank the residues in a protein sequence by evolutionary importance and then maps those ranked at the top onto a representative structure. If these residues form structural clusters, they can identify functional surfaces such as those involved in molecular recognition. Now that a number of examples have shown that ET can identify binding sites and focus mutational studies on their relevant functional determinants, we ask whether the method can be improved so as to be applicable on a large scale. To address this question, we introduce a new treatment of gaps resulting from insertions and deletions, which streamlines the selection of sequences used as input. We also introduce objective statistics to assess the significance of the total number of clusters and of the size of the largest one. As a result of the novel treatment of gaps, ET performance improves measurably. We find evolutionarily privileged clusters that are significant at the 5% level in 45 out of 46 (98%) proteins drawn from a variety of structural classes and biological functions. In 37 of the 38 proteins for which a protein-ligand complex is available, the dominant cluster contacts the ligand. We conclude that spatial clustering of evolutionarily important residues is a general phenomenon, consistent with the cooperative nature of residues that determine structure and function. In practice, these results suggest that ET can be applied on a large scale to identify functional sites in a significant fraction of the structures in the protein databank (PDB). This approach to combining raw sequences and structure to obtain detailed insights into the molecular basis of function should prove valuable in the context of the Structural Genomics Initiative.     

Localized network centrality and essentiality in the yeast–protein interaction network

It has been suggested that a close relationship exists between gene essentiality and network centrality in protein–protein interaction networks. However, recent studies have reported somewhat conflicting results on this relationship. In this study, we investigated whether essential proteins could be inferred from network centrality alone. In addition, we determined which centrality measures describe the essentiality well. For this analysis, we devised new local centrality measures based on several well‐known centrality measures to more precisely describe the connection between network topology and essentiality. We examined two recent yeast protein–protein interaction networks using 40 different centrality measures. We discovered a close relationship between the path‐based localized information centrality and gene essentiality, which suggested underlying topological features that represent essentiality. We propose that two important features of the localized information centrality (proper representation of environmental complexity and the consideration of local sub‐networks) are the key factors that reveal essentiality. In addition, a random forest classifier showed reasonable performance at classifying essential proteins. Finally, the results of clustering analysis using centrality measures indicate that some network clusters are closely related with both particular biological processes and essentiality, suggesting that functionally related proteins tend to share similar network properties.     

Interaction energy based protein structure networks

The three-dimensional structure of a protein is formed and maintained by the noncovalent interactions among the amino-acid residues of the polypeptide chain. These interactions can be represented collectively in the form of a network. So far, such networks have been investigated by considering the connections based on distances between the amino-acid residues. Here we present a method of constructing the structure network based on interaction energies among the amino-acid residues in the protein. We have investigated the properties of such protein energy-based networks (PENs) and have shown correlations to protein structural features such as the clusters of residues involved in stability, formation of secondary and super-secondary structural units. Further we demonstrate that the analysis of PENs in terms of parameters such as hubs and shortest paths can provide a variety of biologically important information, such as the residues crucial for stabilizing the folded units and the paths of communication between distal residues in the protein. Finally, the energy regimes for different levels of stabilization in the protein structure have clearly emerged from the PEN analysis.     

Using information theory to search for co-evolving residues in proteins

MOTIVATION: Some functionally important protein residues are easily detected since they correspond to conserved columns in a multiple sequence alignment (MSA). However important residues may also mutate, with compensatory mutations occurring elsewhere in the protein, which serve to preserve or restore functionality. It is difficult to distinguish these co-evolving sites from other non-conserved sites. RESULTS: We used Mutual Information (MI) to identify co-evolving positions. Using in silico evolved MSAs, we examined the effects of the number of sequences, the size of amino acid alphabet and the mutation rate on two sources of background MI: finite sample size effects and phylogenetic influence. We then assessed the performance of various normalizations of MI in enhancing detection of co-evolving positions and found that normalization by the pair entropy was optimal. Real protein alignments were analyzed and co-evolving isolated pairs were often found to be in contact with each other. AVAILABILITY: All data and program files can be found at http://www.biochem.uwo.ca/cgi-bin/CDD/index.cgi     

Mapping Flexibility and the Assembly Switch of Cell Division Protein FtsZ by Computational and Mutational Approaches

The molecular switch for nucleotide-regulated assembly and disassembly of the main prokaryotic cell division protein FtsZ is unknown despite the numerous crystal structures that are available. We have characterized the functional motions in FtsZ with a computational consensus of essential dynamics, structural comparisons, sequence conservation, and networks of co-evolving residues. Employing this information, we have constructed 17 mutants, which alter the FtsZ functional cycle at different stages, to modify FtsZ flexibility. The mutant phenotypes ranged from benign to total inactivation and included increased GTPase, reduced assembly, and stabilized assembly. Six mutations clustering at the long cleft between the C-terminal β-sheet and core helix H7 deviated FtsZ assembly into curved filaments with inhibited GTPase, which still polymerize cooperatively. These mutations may perturb the predicted closure of the C-terminal domain onto H7 required for switching between curved and straight association modes and for GTPase activation. By mapping the FtsZ assembly switch, this work also gives insight into FtsZ druggability because the curved mutations delineate the putative binding site of the promising antibacterial FtsZ inhibitor PC190723.     

Combining protein sequence,structure, and dynamics: A novel approach for functional evolution analysis of PAS domain superfamily

PAS domains are widespread in archaea, bacteria, and eukaryota, and play important roles in various functions. In this study, we aim to explore functional evolutionary relationship among proteins in the PAS domain superfamily in view of the sequence‐structure‐dynamics‐function relationship. We collected protein sequences and crystal structure data from RCSB Protein Data Bank of the PAS domain superfamily belonging to three biological functions (nucleotide binding, photoreceptor activity, and transferase activity). Protein sequences were aligned and then used to select sequence‐conserved residues and build phylogenetic tree. Three‐dimensional structure alignment was also applied to obtain structure‐conserved residues. The protein dynamics were analyzed using elastic network model (ENM) and validated by molecular dynamics (MD) simulation. The result showed that the proteins with same function could be grouped by sequence similarity, and proteins in different functional groups displayed statistically significant difference in their vibrational patterns. Interestingly, in all three functional groups, conserved amino acid residues identified by sequence and structure conservation analysis generally have a lower fluctuation than other residues. In addition, the fluctuation of conserved residues in each biological function group was strongly correlated with the corresponding biological function. This research suggested a direct connection in which the protein sequences were related to various functions through structural dynamics. This is a new attempt to delineate functional evolution of proteins using the integrated information of sequence, structure, and dynamics.     

Using indirect protein-protein interactions for protein complex prediction

Protein complexes are fundamental for understanding principles of cellular organizations. As the sizes of protein-protein interaction (PPI) networks are increasing, accurate and fast protein complex prediction from these PPI networks can serve as a guide for biological experiments to discover novel protein complexes. However, it is not easy to predict protein complexes from PPI networks, especially in situations where the PPI network is noisy and still incomplete. Here, we study the use of indirect interactions between level-2 neighbors (level-2 interactions) for protein complex prediction. We know from previous work that proteins which do not interact but share interaction partners (level-2 neighbors) often share biological functions. We have proposed a method in which all direct and indirect interactions are first weighted using topological weight (FS-Weight), which estimates the strength of functional association. Interactions with low weight are removed from the network, while level-2 interactions with high weight are introduced into the interaction network. Existing clustering algorithms can then be applied to this modified network. We have also proposed a novel algorithm that searches for cliques in the modified network, and merge cliques to form clusters using a "partial clique merging" method. Experiments show that (1) the use of indirect interactions and topological weight to augment protein-protein interactions can be used to improve the precision of clusters predicted by various existing clustering algorithms; and (2) our complex-finding algorithm performs very well on interaction networks modified in this way. Since no other information except the original PPI network is used, our approach would be very useful for protein complex prediction, especially for prediction of novel protein complexes.     

Computational Modeling of Allosteric Regulation in the Hsp90 Chaperones: A Statistical Ensemble Analysis of Protein Structure Networks and Allosteric Communications

A fundamental role of the Hsp90 chaperone in regulating functional activity of diverse protein clients is essential for the integrity of signaling networks. In this work we have combined biophysical simulations of the Hsp90 crystal structures with the protein structure network analysis to characterize the statistical ensemble of allosteric interaction networks and communication pathways in the Hsp90 chaperones. We have found that principal structurally stable communities could be preserved during dynamic changes in the conformational ensemble. The dominant contribution of the inter-domain rigidity to the interaction networks has emerged as a common factor responsible for the thermodynamic stability of the active chaperone form during the ATPase cycle. Structural stability analysis using force constant profiling of the inter-residue fluctuation distances has identified a network of conserved structurally rigid residues that could serve as global mediating sites of allosteric communication. Mapping of the conformational landscape with the network centrality parameters has demonstrated that stable communities and mediating residues may act concertedly with the shifts in the conformational equilibrium and could describe the majority of functionally significant chaperone residues. The network analysis has revealed a relationship between structural stability, global centrality and functional significance of hotspot residues involved in chaperone regulation. We have found that allosteric interactions in the Hsp90 chaperone may be mediated by modules of structurally stable residues that display high betweenness in the global interaction network. The results of this study have suggested that allosteric interactions in the Hsp90 chaperone may operate via a mechanism that combines rapid and efficient communication by a single optimal pathway of structurally rigid residues and more robust signal transmission using an ensemble of suboptimal multiple communication routes. This may be a universal requirement encoded in protein structures to balance the inherent tension between resilience and efficiency of the residue interaction networks.     

Regional covariation and its application for predicting protein contact patches

Correlated mutation analysis (CMA) is an effective approach for predicting functional and structural residue interactions from multiple sequence alignments (MSAs) of proteins. As nearby residues may also play a role in a given functional interaction, we were interested in seeing whether covarying sites were clustered, and whether this could be used to enhance the predictive power of CMA. A large‐scale search for coevolving regions within protein domains revealed that if two sites in a MSA covary, then neighboring sites in the alignment also typically covary, resulting in clusters of covarying residues. The program PatchD( http://www.uhnres.utoronto.ca/labs/tillier/ ) was developed to measure the covariation between disconnected sequence clusters to reveal patch covariation. Patches that exhibit strong covariation identify multiple residues that are generally nearby in the protein structure, suggesting that the detection of covarying patches can be used in conjunction with traditional CMA approaches to reveal functional interaction partners. Proteins 2010. © 2009 Wiley‐Liss, Inc.     

Folding of bovine pancreatic trypsin inhibitor (BPTI) variants in which almost half the residues are alanine

Recent studies indicate that a fraction of the information contained in an amino acid sequence may be sufficient for specifying a native protein structure. An earlier alanine-scanning experiment conducted on bovine pancreatic trypsin inhibitor (BPTI; 58 residues) suggested that if cumulative mutations have additive effects on protein stability, a native protein structure could be built from BPTI sequences that contained many alanine residues distributed throughout the protein. To test this hypothesis, we designed and produced six BPTI mutants containing from 21 to 29 alanine residues. We found that the melting temperature of mutants containing up to 27 alanine residues (48 % of the total number of residues) could be predicted quite well by the sum of the change in melting temperature for the single mutations. Additionally, these same mutants folded into a native-like structure, as judged by their cooperative thermal denaturation curves and heteronuclear multiple quantum correlation (HMQC) NMR spectra. A BPTI mutant containing 22 alanine residues was further shown by 2D and 3D-NMR to fold into a structure very similar to that of native BPTI, and to be a functional trypsin inhibitor. These results provide insight into the extent to which native protein structure and function can be achieved with a highly simplified amino acid sequence.     

真核翻译延伸因子1A蛋白家族功能位点的进化踪迹分析

真核翻译延伸因子1A（eEF1A）是真核生物蛋白质翻译过程中能将氨酰tRNA运送到核糖体A位点参与多肽延伸反应的多功能蛋白质. 本文主要利用多种生物信息学分析工具进行地中海涡虫翻译延伸因子1A（SmEF1A）蛋白序列的查找与eEF1A直系同源蛋白的搜索, 并基于90条直系同源蛋白进行eEF1A蛋白家族的进化踪迹分析和SmEF1A蛋白功能位点的比较研究. 结果表明,在eEF1A蛋白家族中共识别到338个踪迹残基位点和20个踪迹残基富集区域,SmEF1A蛋白的功能位点与踪迹残基位点密切相关,与GTP/Mg2+结合相关的S21、T72、D91、G94等重要位点均为全家族保守的踪迹残基,N 糖基化、磷酸化等蛋白修饰位点中踪迹残基位点往往是被修饰的部位或修饰功能发挥的关键辅助位点,而位于分子表面的配基结合口袋则与20个踪迹残基富集区域在分子表面形成的踪迹残基簇关系密切. eEF1A蛋白家族的进化踪迹分析为eEF1A蛋白重要功能区域关键残基的确定和未知功能位点的预测提供了重要信息.