InterPepScore: a deep learning score for improving the FlexPepDock refinement protocol.

ABSTRACT:

Motivation

Interactions between peptide fragments and protein receptors are vital to cell function yet difficult to experimentally determine in structural details of. As such, many computational methods have been developed to aid in peptide-protein docking or structure prediction. One such method is Rosetta FlexPepDock which consistently refines coarse peptide-protein models into sub-Ångström precision using Monte-Carlo simulations and statistical potentials. Deep learning has recently seen increased use in protein structure prediction, with graph neural networks used for protein model quality assessment.

Results

Here, we introduce a graph neural network, InterPepScore, as an additional scoring term to complement and improve the Rosetta FlexPepDock refinement protocol. Inter

SUBMITTER: Johansson-Akhe I 

PROVIDER: S-EPMC9191208 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature