Abstract
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.
Highlights
The identification of functionally and structurally important elements in DNA, RNA and proteins from their sequences has been a major focus of computational biology for several decades
With the large amount of data currently available, deep multiple alignments can be constructed of protein sequences that likely fold into a common structure, and several methods have been proposed for predicting contacting residues from statistical dependencies exhibited by pairs of alignment columns
Through a comprehensive analysis of 2009 protein domains, we show that a large fraction of these distal dependencies are indirect and result from chains of contacting pairs that percolate through the protein
Summary
The identification of functionally and structurally important elements in DNA, RNA and proteins from their sequences has been a major focus of computational biology for several decades. Socalled profile hidden Markov models [1] of protein families and domains have been highly successful in identifying sequences that have similar function and fold into a common structure, making them among the most important tools in functional genomics, see e.g. Currently the multiple alignments of many protein families and domains include hundreds and sometimes even thousands of sequences, making it possible to systematically investigate dependencies between the residues at different positions
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