Protein contact map refinement for improving structure prediction using generative adversarial networks.

ABSTRACT:

Motivation

Protein structure prediction remains as one of the most important problems in computational biology and biophysics. In the past few years, protein residue-residue contact prediction has undergone substantial improvement, which has made it a critical driving force for successful protein structure prediction. Boosting the accuracy of contact predictions has, therefore, become the forefront of protein structure prediction.

Results

We show a novel contact map refinement method, ContactGAN, which uses Generative Adversarial Networks (GAN). ContactGAN was able to make a significant improvement over predictions made by recent contact prediction methods when tested on three datasets including protein structure modeling targets in CASP13 and CASP14. We show improvement of

SUBMITTER: Maddhuri Venkata Subramaniya SR 

PROVIDER: S-EPMC8504630 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature