Protein contact prediction by integrating deep multiple sequence alignments, coevolution and machine learning.

ABSTRACT: In this study, we report the evaluation of the residue-residue contacts predicted by our three different methods in the CASP12 experiment, focusing on studying the impact of multiple sequence alignment, residue coevolution, and machine learning on contact prediction. The first method (MULTICOM-NOVEL) uses only traditional features (sequence profile, secondary structure, and solvent accessibility) with deep learning to predict contacts and serves as a baseline. The second method (MULTICOM-CONSTRUCT) uses our new alignment algorithm to generate deep multiple sequence alignment to derive coevolution-based features, which are integrated by a neural network method to predict contacts. The third method (MULTICOM-CLUSTER) is a consensus combination of the predictions of the first two methods. We

SUBMITTER: Adhikari B 

PROVIDER: S-EPMC5820155 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature