High precision in protein contact prediction using fully convolutional neural networks and minimal sequence features.

ABSTRACT:

Motivation

In addition to substitution frequency data from protein sequence alignments, many state-of-the-art methods for contact prediction rely on additional sources of information, or features, of protein sequences in order to predict residue-residue contacts, such as solvent accessibility, predicted secondary structure, and scores from other contact prediction methods. It is unclear how much of this information is needed to achieve state-of-the-art results. Here, we show that using deep neural network models, simple alignment statistics contain sufficient information to achieve state-of-the-art precision. Our prediction method, DeepCov, uses fully convolutional neural networks operating on amino-acid pair frequency or covariance data derived directly from sequence alignments, w

SUBMITTER: Jones DT 

PROVIDER: S-EPMC6157083 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature