Improved contact predictions using the recognition of protein like contact patterns.

ABSTRACT: Given sufficient large protein families, and using a global statistical inference approach, it is possible to obtain sufficient accuracy in protein residue contact predictions to predict the structure of many proteins. However, these approaches do not consider the fact that the contacts in a protein are neither randomly, nor independently distributed, but actually follow precise rules governed by the structure of the protein and thus are interdependent. Here, we present PconsC2, a novel method that uses a deep learning approach to identify protein-like contact patterns to improve contact predictions. A substantial enhancement can be seen for all contacts independently on the number of aligned sequences, residue separation or secondary structure type, but is largest for β-sheet containing p

SUBMITTER: Skwark MJ 

PROVIDER: S-EPMC4222596 | biostudies-literature | 2014 Nov

REPOSITORIES: biostudies-literature