Accurate prediction of protein torsion angles using evolutionary signatures and recurrent neural network.

ABSTRACT: The amino acid sequence of a protein contains all the necessary information to specify its shape, which dictates its biological activities. However, it is challenging and expensive to experimentally determine the three-dimensional structure of proteins. The backbone torsion angles play a critical role in protein structure prediction, and accurately predicting the angles can considerably advance the tertiary structure prediction by accelerating efficient sampling of the large conformational space for low energy structures. Here we first time propose evolutionary signatures computed from protein sequence profiles, and a novel recurrent architecture, termed ESIDEN, that adopts a straightforward architecture of recurrent neural networks with a small number of learnable parameters. The ESIDEN c

SUBMITTER: Xu YC 

PROVIDER: S-EPMC8548351 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature