Finite sample sizes and phylogeny do not ACCOUNT FOR THE MUTUAL INFORMATION OBSERVED FOR MOST SITE-PAIRS IN MULTIPLE SEQUENCE ALIGNMENT

Mutual information (MI) is a measure frequently used to find co-evolving sites in protein families. However, factors unrelated to protein structure and function, in particular sampling variance in amino acid counts and complex evolutionary relationships among sequences, contribute to ML Understanding the contribution of these components is essential for isolating the MI associated with structural or functional co-evolution. To date, the contributions of these factors to mutual information have not been fully elucidated. We find that stochastic variations in amino acid counts and shared phylogeny each contribute substantially to measured MI. Nonetheless, the mutual information observed in real-world protein families is consistently higher than the expected contribution of these two factors. In contrast, when using synthetic data with realistic substitution rates and phylogenies, but without structural or functional constraints, the observed levels of MI match those expected due to stochastic and phylogenetic background. Our results suggest that either low levels of co-evolution are ubiquitous across positions in protein families, or some unknown factor exists beyond the currently hypothesized components of intra-protein mutual information: sampling variance, phylogenetics and structural/functional co-evolution

Why Should We Care About Molecular Coevolution?

Non-independent evolution of amino acid sites has become a noticeable limitation of most methods aimed at identifying selective constraints at functionally important amino acid sites or protein regions. The need for a generalised framework to account for non-independence of amino acid sites has fuelled the design and development of new mathematical models and computational tools centred on resolving this problem. Molecular coevolution is one of the most active areas of research, with an increasing rate of new models and methods being developed everyday. Both parametric and non-parametric methods have been developed to account for correlated variability of amino acid sites. These methods have been utilised for detecting phylogenetic, functional and structural coevolution as well as to identify surfaces of amino acid sites involved in protein-protein interactions. Here we discuss and briefly describe these methods, and identify their advantages and limitations

Fast and Robust Characterization of Time-Heterogeneous Sequence Evolutionary Processes Using Substitution Mapping

Genes and genomes do not evolve similarly in all branches of the tree of life. Detecting and characterizing the heterogeneity in time, and between lineages, of the nucleotide (or amino acid) substitution process is an important goal of current molecular evolutionary research. This task is typically achieved through the use of non-homogeneous models of sequence evolution, which being highly parametrized and computationally-demanding are not appropriate for large-scale analyses. Here we investigate an alternative methodological option based on probabilistic substitution mapping. The idea is to first reconstruct the substitutional history of each site of an alignment under a homogeneous model of sequence evolution, then to characterize variations in the substitution process across lineages based on substitution counts. Using simulated and published datasets, we demonstrate that probabilistic substitution mapping is robust in that it typically provides accurate reconstruction of sequence ancestry even when the true process is heterogeneous, but a homogeneous model is adopted. Consequently, we show that the new approach is essentially as efficient as and extremely faster than (up to 25 000 times) existing methods, thus paving the way for a systematic survey of substitution process heterogeneity across genes and lineages

New methods to measure residues coevolution in proteins

<p>Abstract</p> <p>Background</p> <p>The covariation of two sites in a protein is often used as the degree of their coevolution. To quantify the covariation many methods have been developed and most of them are based on residues position-specific frequencies by using the mutual information (MI) model.</p> <p>Results</p> <p>In the paper, we proposed several new measures to incorporate new biological constraints in quantifying the covariation. The first measure is the mutual information with the amino acid background distribution (MIB), which incorporates the amino acid background distribution into the marginal distribution of the MI model. The modification is made to remove the effect of amino acid evolutionary pressure in measuring covariation. The second measure is the mutual information of residues physicochemical properties (MIP), which is used to measure the covariation of physicochemical properties of two sites. The third measure called MIBP is proposed by applying residues physicochemical properties into the MIB model. Moreover, scores of our new measures are applied to a robust indicator <it>conn(k) </it>in finding the covariation signal of each site.</p> <p>Conclusions</p> <p>We find that incorporating amino acid background distribution is effective in removing the effect of evolutionary pressure of amino acids. Thus the MIB measure describes more biological background information for the coevolution of residues. Besides, our analysis also reveals that the covariation of physicochemical properties is a new aspect of coevolution information.</p

Identification of specificity determining residues in peptide recognition domains using an information theoretic approach applied to large-scale binding maps

<p>Abstract</p> <p>Background</p> <p>Peptide Recognition Domains (PRDs) are commonly found in signaling proteins. They mediate protein-protein interactions by recognizing and binding short motifs in their ligands. Although a great deal is known about PRDs and their interactions, prediction of PRD specificities remains largely an unsolved problem.</p> <p>Results</p> <p>We present a novel approach to identifying these Specificity Determining Residues (SDRs). Our algorithm generalizes earlier information theoretic approaches to coevolution analysis, to become applicable to this problem. It leverages the growing wealth of binding data between PRDs and large numbers of random peptides, and searches for PRD residues that exhibit strong evolutionary covariation with some positions of the statistical profiles of bound peptides. The calculations involve only information from sequences, and thus can be applied to PRDs without crystal structures. We applied the approach to PDZ, SH3 and kinase domains, and evaluated the results using both residue proximity in co-crystal structures and verified binding specificity maps from mutagenesis studies.</p> <p>Discussion</p> <p>Our predictions were found to be strongly correlated with the physical proximity of residues, demonstrating the ability of our approach to detect physical interactions of the binding partners. Some high-scoring pairs were further confirmed to affect binding specificity using previous experimental results. Combining the covariation results also allowed us to predict binding profiles with higher reliability than two other methods that do not explicitly take residue covariation into account.</p> <p>Conclusions</p> <p>The general applicability of our approach to the three different domain families demonstrated in this paper suggests its potential in predicting binding targets and assisting the exploration of binding mechanisms.</p

Prevalence of Epistasis in the Evolution of Influenza A Surface Proteins

The surface proteins of human influenza A viruses experience positive selection to escape both human immunity and, more recently, antiviral drug treatments. In bacteria and viruses, immune-escape and drug-resistant phenotypes often appear through a combination of several mutations that have epistatic effects on pathogen fitness. However, the extent and structure of epistasis in influenza viral proteins have not been systematically investigated. Here, we develop a novel statistical method to detect positive epistasis between pairs of sites in a protein, based on the observed temporal patterns of sequence evolution. The method rests on the simple idea that a substitution at one site should rapidly follow a substitution at another site if the sites are positively epistatic. We apply this method to the surface proteins hemagglutinin and neuraminidase of influenza A virus subtypes H3N2 and H1N1. Compared to a non-epistatic null distribution, we detect substantial amounts of epistasis and determine the identities of putatively epistatic pairs of sites. In particular, using sequence data alone, our method identifies epistatic interactions between specific sites in neuraminidase that have recently been demonstrated, in vitro, to confer resistance to the drug oseltamivir; these epistatic interactions are responsible for widespread drug resistance among H1N1 viruses circulating today. This experimental validation demonstrates the predictive power of our method to identify epistatic sites of importance for viral adaptation and public health. We conclude that epistasis plays a large role in shaping the molecular evolution of influenza viruses. In particular, sites with , which would normally not be identified as positively selected, can facilitate viral adaptation through epistatic interactions with their partner sites. The knowledge of specific interactions among sites in influenza proteins may help us to predict the course of antigenic evolution and, consequently, to select more appropriate vaccines and drugs

Computing Highly Correlated Positions Using Mutual Information and Graph Theory for G Protein-Coupled Receptors

G protein-coupled receptors (GPCRs) are a superfamily of seven transmembrane-spanning proteins involved in a wide array of physiological functions and are the most common targets of pharmaceuticals. This study aims to identify a cohort or clique of positions that share high mutual information. Using a multiple sequence alignment of the transmembrane (TM) domains, we calculated the mutual information between all inter-TM pairs of aligned positions and ranked the pairs by mutual information. A mutual information graph was constructed with vertices that corresponded to TM positions and edges between vertices were drawn if the mutual information exceeded a threshold of statistical significance. Positions with high degree (i.e. had significant mutual information with a large number of other positions) were found to line a well defined inter-TM ligand binding cavity for class A as well as class C GPCRs. Although the natural ligands of class C receptors bind to their extracellular N-terminal domains, the possibility of modulating their activity through ligands that bind to their helical bundle has been reported. Such positions were not found for class B GPCRs, in agreement with the observation that there are not known ligands that bind within their TM helical bundle. All identified key positions formed a clique within the MI graph of interest. For a subset of class A receptors we also considered the alignment of a portion of the second extracellular loop, and found that the two positions adjacent to the conserved Cys that bridges the loop with the TM3 qualified as key positions. Our algorithm may be useful for localizing topologically conserved regions in other protein families

Integrated Analysis of Residue Coevolution and Protein Structure in ABC Transporters

Intraprotein side chain contacts can couple the evolutionary process of amino acid substitution at one position to that at another. This coupling, known as residue coevolution, may vary in strength. Conserved contacts thus not only define 3-dimensional protein structure, but also indicate which residue-residue interactions are crucial to a protein’s function. Therefore, prediction of strongly coevolving residue-pairs helps clarify molecular mechanisms underlying function. Previously, various coevolution detectors have been employed separately to predict these pairs purely from multiple sequence alignments, while disregarding available structural information. This study introduces an integrative framework that improves the accuracy of such predictions, relative to previous approaches, by combining multiple coevolution detectors and incorporating structural contact information. This framework is applied to the ABC-B and ABC-C transporter families, which include the drug exporter P-glycoprotein involved in multidrug resistance of cancer cells, as well as the CFTR chloride channel linked to cystic fibrosis disease. The predicted coevolving pairs are further analyzed based on conformational changes inferred from outward- and inward-facing transporter structures. The analysis suggests that some pairs coevolved to directly regulate conformational changes of the alternating-access transport mechanism, while others to stabilize rigid-body-like components of the protein structure. Moreover, some identified pairs correspond to residues previously implicated in cystic fibrosis