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

By Lukas Burger, E. van Nimwegen. 2010

214 citations recorded by the source corpus.

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.

Subfields: Molecular Biology

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Citations in the corpus

Direct-coupling analysis of residue coevolution captures native contacts across many protein families (2011, positive): “CijðA;BÞ 1⁄4 f ijðA;BÞ − f iðAÞf jðBÞ: [10]”
Protein structure prediction from sequence variation (2012, methodology): “…global statistical methods, we (D.S.M. and colleagues 15 ) also have folded proteins using residue contacts predicted by a Bayesian network model 19 , reporting three-dimensional structure error between 4 and 6 C a r.m.s. deviation, at somewhat lower accuracy than with contacts predicted by the…”
3D structures of membrane proteins from genomic sequencing (2012, neutral): “Themostaccurateofthesestrategiesuseglobalstatistical methods, such as maximum entropy (Marks et al., 2011; Morcos et al., 2011), Bayesian networks (Burger and van Nimwegen, 2010), or covariance estimation (Jones et al., 2012; Meinshausen andBuhlmann,2006).”
Dissecting the Specificity of Protein-Protein Interaction in Bacterial Two-Component Signaling: Orphans and Crosstalks (2011, background): “ig. S1), thereby vastly improving the accuracy of contact residue predictions. The idea of disentangling direct from indirect correlations was observed to be successful also in single protein domains [18; 19]. The same principle forms the basis of our approach to studying interaction specificity. Co”
Improved Contact Predictions Using the Recognition of Protein Like Contact Patterns (2014, methodology): “The introduction of predictors using global models, inferring couplings between residues from the observed correlations, significantly increases the accuracy of co-variation based methods [12-16].”
Protein contact prediction by integrating joint evolutionary coupling analysis and supervised learning (2013, methodology): “Different from existing single-family EC analysis that uses residue coevolution information in only the target protein family, our joint EC analysis uses residue coevolution in both the target family and its related families, which may have divergent sequences but similar folds.”
Deep architectures for protein contact map prediction (2012, background): “Received on May 25, 2012; revised on July 5, 2012; accepted on July 22, 2012”
Large-Scale Conformational Transitions and Dimerization Are Encoded in the Amino-Acid Sequences of Hsp70 Chaperones (2015, unknown): “As shown by Burger and van Nimwegen [58] in the context of mutual information coevolutionary networks, the shortest paths efficiently capture the mediation of coevolutions along chains of residues.”
3D Protein Structure Predicted from Sequence (2011, unknown): “In (A), The two global models, the Bayesian network model (BNM, green [13]) and direct information model (DI, red, this work and [46] have a consistently high rate of correctly predicted contacts (true ”