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

Journal: Bioinformatics (Oxford, England)
Published Date:

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.

Authors

  • Isak Johansson-Åkhe
    Division of Bioinformatics, Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden.
  • Björn Wallner
    Division of Bioinformatics, Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden.