Predicting diverse M-best protein contact maps

TL;DR

This paper introduces a novel method to predict multiple diverse protein contact maps from a single sequence, improving accuracy over existing methods especially for proteins with few homologs.

Contribution

It presents a structure learning approach that generates a set of diverse contact maps, outperforming traditional EC methods in accuracy for challenging protein families.

Findings

Best out of 5 solutions improves accuracy by at least 0.1 over the first.

Method performs better than Evfold and PSICOV on test proteins.

Especially effective for proteins with few sequence homologs.

Abstract

Protein contacts contain important information for protein structure and functional study, but contact prediction from sequence information remains very challenging. Recently evolutionary coupling (EC) analysis, which predicts contacts by detecting co-evolved residues (or columns) in a multiple sequence alignment (MSA), has made good progress due to better statistical assessment techniques and high-throughput sequencing. Existing EC analysis methods predict only a single contact map for a given protein, which may have low accuracy especially when the protein under prediction does not have a large number of sequence homologs. Analogous to ab initio folding that usually predicts a few possible 3D models for a given protein sequence, this paper presents a novel structure learning method that can predict a set of diverse contact maps for a given protein sequence, in which the best solution usually has much better accuracy than the first one. Our experimental tests show that for many test proteins, the best out of 5 solutions generated by our method has accuracy at least 0.1 better than the first one when the top L/5 or L/10 (L is the sequence length) predicted long-range contacts are evaluated, especially for protein families with a small number of sequence homologs. Our best solutions also have better quality than those generated by the two popular EC methods Evfold and PSICOV.