Predicting protein inter-residue contacts using composite likelihood maximization and deep learning.

ABSTRACT:

Background

Accurate prediction of inter-residue contacts of a protein is important to calculating its tertiary structure. Analysis of co-evolutionary events among residues has been proved effective in inferring inter-residue contacts. The Markov random field (MRF) technique, although being widely used for contact prediction, suffers from the following dilemma: the actual likelihood function of MRF is accurate but time-consuming to calculate; in contrast, approximations to the actual likelihood, say pseudo-likelihood, are efficient to calculate but inaccurate. Thus, how to achieve both accuracy and efficiency simultaneously remains a challenge.

Results

In this study, we present such an approach (called clmDCA) for contact prediction. Unlike plmDCA using pseudo-likelihood, i.e

SUBMITTER: Zhang H 

PROVIDER: S-EPMC6821021 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature