Improving position-specific predictions of protein functional sites using phylogenetic motifs

Enzyme function and evolution through the lens of bioinformatics

Enzymes have been shaped by evolution over billions of years to catalyse the chemical reactions that support life on earth. Dispersed in the literature, or organised in online databases, knowledge about enzymes can be structured in distinct dimensions, either related to their quality as biological macromolecules, such as their sequence and structure, or related to their chemical functions, such as the catalytic site, kinetics, mechanism, and overall reaction. The evolution of enzymes can only be understood when each of these dimensions is considered. In addition, many of the properties of enzymes only make sense in the light of evolution. We start this review by outlining the main paradigms of enzyme evolution, including gene duplication and divergence, convergent evolution, and evolution by recombination of domains. In the second part, we overview the current collective knowledge about enzymes, as organised by different types of data and collected in several databases. We also highlight some increasingly powerful computational tools that can be used to close gaps in understanding, in particular for types of data that require laborious experimental protocols. We believe that recent advances in protein structure prediction will be a powerful catalyst for the prediction of binding, mechanism, and ultimately, chemical reactions. A comprehensive mapping of enzyme function and evolution may be attainable in the near future.

Structure-based Methods for Computational Protein Functional Site Prediction

Due to the advent of high throughput sequencing techniques and structural genomic projects, the number of gene and protein sequences has been ever increasing. Computational methods to annotate these genes and proteins are even more indispensable. Proteins are important macromolecules and study of the function of proteins is an important problem in structural bioinformatics. This paper discusses a number of methods to predict protein functional site especially focusing on protein ligand binding site prediction. Initially, a short overview is presented on recent advances in methods for selection of homologous sequences. Furthermore, a few recent structural based approaches and sequence-and-structure based approaches for protein functional sites are discussed in details.

Identification of catalytic residues using a novel feature that integrates the microenvironment and geometrical location properties of residues

Enzymes play a fundamental role in almost all biological processes and identification of catalytic residues is a crucial step for deciphering the biological functions and understanding the underlying catalytic mechanisms. In this work, we developed a novel structural feature called MEDscore to identify catalytic residues, which integrated the microenvironment (ME) and geometrical properties of amino acid residues. Firstly, we converted a residue's ME into a series of spatially neighboring residue pairs, whose likelihood of being located in a catalytic ME was deduced from a benchmark enzyme dataset. We then calculated an ME-based score, termed as MEscore, by summing up the likelihood of all residue pairs. Secondly, we defined a parameter called Dscore to measure the relative distance of a residue to the center of the protein, provided that catalytic residues are typically located in the center of the protein structure. Finally, we defined the MEDscore feature based on an effective nonlinear integration of MEscore and Dscore. When evaluated on a well-prepared benchmark dataset using five-fold cross-validation tests, MEDscore achieved a robust performance in identifying catalytic residues with an AUC1.0 of 0.889. At a ≤ 10% false positive rate control, MEDscore correctly identified approximately 70% of the catalytic residues. Remarkably, MEDscore achieved a competitive performance compared with the residue conservation score (e.g. CONscore), the most informative singular feature predominantly employed to identify catalytic residues. To the best of our knowledge, MEDscore is the first singular structural feature exhibiting such an advantage. More importantly, we found that MEDscore is complementary with CONscore and a significantly improved performance can be achieved by combining CONscore with MEDscore in a linear manner. As an implementation of this work, MEDscore has been made freely accessible at http://protein.cau.edu.cn/mepi/.

L1pred: a sequence-based prediction tool for catalytic residues in enzymes with the L1-logreg classifier

To understand enzyme functions, identifying the catalytic residues is a usual first step. Moreover, knowledge about catalytic residues is also useful for protein engineering and drug-design. However, to experimentally identify catalytic residues remains challenging for reasons of time and cost. Therefore, computational methods have been explored to predict catalytic residues. Here, we developed a new algorithm, L1pred, for catalytic residue prediction, by using the L1-logreg classifier to integrate eight sequence-based scoring functions. We tested L1pred and compared it against several existing sequence-based methods on carefully designed datasets Data604 and Data63. With ten-fold cross-validation, L1pred showed the area under precision-recall curve (AUPR) and the area under ROC curve (AUC) of 0.2198 and 0.9494 on the training dataset, Data604, respectively. In addition, on the independent test dataset, Data63, it showed the AUPR and AUC values of 0.2636 and 0.9375, respectively. Compared with other sequence-based methods, L1pred showed the best performance on both datasets. We also analyzed the importance of each attribute in the algorithm, and found that all the scores contributed more or less equally to the L1pred performance.

KvSNP: accurately predicting the effect of genetic variants in voltage-gated potassium channels

MOTIVATION

Non-synonymous single nucleotide polymorphisms (nsSNPs) in voltage-gated potassium (Kv) channels cause diseases with potentially fatal consequences in seemingly healthy individuals. Identifying disease-causing genetic variation will aid presymptomatic diagnosis and treatment of such disorders. NsSNP-effect predictors are hypothesized to perform best when developed for specific gene families. We, thus, created KvSNP: a method that assigns a disease-causing probability to Kv-channel nsSNPs.

RESULTS

KvSNP outperforms popular non gene-family-specific methods (SNPs&GO, SIFT and Polyphen) in predicting the disease potential of Kv-channel variants, according to all tested metrics (accuracy, Matthews correlation coefficient and area under receiver operator characteristic curve). Most significantly, it increases the separation of the median predicted disease probabilities between benign and disease-causing SNPs by 26% on the next-best competitor. KvSNP has ranked 172 uncharacterized Kv-channel nsSNPs by disease-causing probability.

AVAILABILITY AND IMPLEMENTATION

KvSNP, a WEKA implementation is available at www.bioinformatics.leeds.ac.uk/KvDB/KvSNP.html.

CONTACT

d.r.westhead@leeds.ac.uk

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Functionally important positions can comprise the majority of a protein's architecture

Concomitant with the genomic era, many bioinformatics programs have been developed to identify functionally important positions from sequence alignments of protein families. To evaluate these analyses, many have used the LacI/GalR family and determined whether positions predicted to be "important" are validated by published experiments. However, we previously noted that predictions do not identify all of the experimentally important positions present in the linker regions of these homologs. In an attempt to reconcile these differences, we corrected and expanded the LacI/GalR sequence set commonly used in sequence/function analyses. Next, a variety of analyses were carried out (1) for the entire LacI/GalR sequence set and (2) for a subset of homologs with functionally-important "YxPxxxAxxL" motifs in their linkers. This strategy was devised to determine whether predictions could be improved by knowledge-based sequence sorting and-for some analyses-did increase the number of linker positions identified. However, two functionally important linker positions were not reliably identified by any analysis. Finally, we compared the new predictions to all known experimental data for E. coli LacI and three homologous linkers. From these, we estimate that >50% of positions are important to the functions of the LacI/GalR homologs. In corollary, neutral positions might occur less frequently and might be easier to detect in sequence analyses. Although analyses have successfully guided mutations that partially exchange protein functions, a better experimental understanding of the sequence/function relationships in protein families would be helpful for uncovering the remaining rules used by nature to evolve new protein functions.

Topology improves phylogenetic motif functional site predictions

Prediction of protein functional sites from sequence-derived data remains an open bioinformatics problem. We have developed a phylogenetic motif (PM) functional site prediction approach that identifies functional sites from alignment fragments that parallel the evolutionary patterns of the family. In our approach, PMs are identified by comparing tree topologies of each alignment fragment to that of the complete phylogeny. Herein, we bypass the phylogenetic reconstruction step and identify PMs directly from distance matrix comparisons. In order to optimize the new algorithm, we consider three different distance matrices and 13 different matrix similarity scores. We assess the performance of the various approaches on a structurally nonredundant data set that includes three types of functional site definitions. Without exception, the predictive power of the original approach outperforms the distance matrix variants. While the distance matrix methods fail to improve upon the original approach, our results are important because they clearly demonstrate that the improved predictive power is based on the topological comparisons. Meaning that phylogenetic trees are a straightforward, yet powerful way to improve functional site prediction accuracy. While complementary studies have shown that topology improves predictions of protein-protein interactions, this report represents the first demonstration that trees improve functional site predictions as well.

Energy based approach for understanding the recognition mechanism in protein-protein complexes

Protein-protein interactions play an essential role in the regulation of various cellular processes. Understanding the recognition mechanism of protein-protein complexes is a challenging task in molecular and computational biology. In this work, we have developed an energy based approach for identifying the binding sites and important residues for binding in protein-protein complexes. The new approach is different from the traditional distance based contacts in which the repulsive interactions are treated as binding sites as well as the contacts within a specific cutoff have been treated in the same way. We found that the residues and residue-pairs with charged and aromatic side chains are important for binding. These residues influence to form cation-, electrostatic and aromatic interactions. Our observation has been verified with the experimental binding specificity of protein-protein complexes and found good agreement with experiments. Based on these results we have proposed a novel mechanism for the recognition of protein-protein complexes: the charged and aromatic residues in receptor and ligand initiate recognition by making suitable interactions between them; the neighboring hydrophobic residues assist the stability of complex along with other hydrogen bonding partners by the polar residues. Further, the propensity of residues in the binding sites of receptors and ligands, atomic contributions and the influence on secondary structure will be discussed.

Predicting protein ligand binding sites by combining evolutionary sequence conservation and 3D structure

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

Protein molecules are ubiquitous in the cell; they perform thousands of functions crucial for life. Proteins accomplish nearly all of these functions by interacting with other molecules. These interactions are mediated by specific amino acid positions in the proteins. Knowledge of these “functional sites” is crucial for understanding the molecular mechanisms by which proteins carry out their functions; however, functional sites have not been identified in the vast majority of proteins. Here, we present ConCavity, a computational method that predicts small molecule binding sites in proteins by combining analysis of evolutionary sequence conservation and protein 3D structure. ConCavity provides significant improvement over previous approaches, especially on large, multi-chain proteins. In contrast to earlier methods which only predict entire binding sites, ConCavity makes specific predictions of positions in space that are likely to overlap ligand atoms and of residues that are likely to contact bound ligands. These predictions can be used to aid computational function prediction, to guide experimental protein analysis, and to focus computationally intensive techniques used in drug discovery.

Comparing the functional roles of nonconserved sequence positions in homologous transcription repressors: implications for sequence/function analyses

The explosion of protein sequences deduced from genetic code has led to both a problem and a potential resource: Efficient data use requires interpreting the functional impact of sequence change without experimentally characterizing each protein variant. Several groups have hypothesized that interpretation could be aided by analyzing the sequences of naturally occurring homologues. To that end, myriad sequence/function analyses have been developed to predict which conserved, semi-conserved, and nonconserved positions are functionally important. These positions must be discriminated from the nonconserved positions that are functionally silent. However, the assumptions that underlie sequence analyses are based on experimental results that are sparse and usually designed to address different questions. Here, we use three homologues from a test family common to bioinformatics-the LacI/GalR transcription repressors-to test a common assumption: If a position is functionally important for one family member, it has similar importance in all homologues. We generated experimental sequence/function information for each nonconserved position in the 18 amino acids that link the DNA-binding and regulatory domains of three LacI/GalR homologues. We find that the functional importance of each position is preserved among the three linkers, albeit to different degrees. We also find that every linker position contributes to function, which has twofold implications. (1) Since the linker positions range from highly conserved to semi-conserved to nonconserved and contribute to affinity, selectivity, and allosteric response, we assert that sequence/function analyses must identify positions in the LacI/GalR linkers to be qualified as "successful". Many analyses overlook this region since most of the residues do not directly contact ligand. (2) No position in the LacI/GalR linker is functionally silent. This finding is inconsistent with another underlying principle of many analyses: Using sequence sets to discriminate important from non-contributing positions obligates silent positions, which denotes that most homologues tolerate a variety of amino acid substitutions at the position without functional change. Instead, additional combinatorial mutants in the LacI/GalR linkers show that particular substitutions can be silent in a context-dependent manner. Thus, specific permutations of sequence change (rather than change at silent positions) would facilitate neutral drift during evolution. Finally, the combinatorial mutants also reveal functional synergy between semi- and nonconserved positions. Such functional relationships would be missed by analyses that rely primarily upon co-evolution.

Several appropriate background distributions for entropy-based protein sequence conservation measures

Amino acid background distribution is an important factor for entropy-based methods which extract sequence conservation information from protein multiple sequence alignments (MSAs). However, MSAs are usually not large enough to allow a reliable observed background distribution. In this paper, we propose two new estimations of background distribution. One is an integration of the observed background distribution and the position-specific residue distribution, and the other is a normalized square root of observed background frequency. To validate these new background distributions, they are applied to the relative entropy model to find catalytic sites and ligand binding sites from protein MSAs. Experimental results show that they are superior to the observed background distribution in predicting functionally important residues.