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- W4200301384 abstract "Abstract Motivation Interactions between peptide fragments and protein receptors are vital to cell function yet difficult to experimentally determine the 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 network seeing use in 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. InterPepScore is trained on simulation trajectories from FlexPepDock refinement starting from thousands of peptide-protein complexes generated by a wide variety of docking schemes. The addition of InterPepScore into the refinement protocol consistently improves the quality of models created, and on an independent benchmark on 109 peptide-protein complexes its inclusion results in an increase in the number of complexes for which the top-scoring model had a DockQ-score of 0.49 (Medium quality) or better from 14.8% to 26.1%. Availability InterPepScore is available online at http://wallnerlab.org/InterPepScore ." @default.
- W4200301384 created "2021-12-31" @default.
- W4200301384 creator A5010890480 @default.
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- W4200301384 date "2021-12-10" @default.
- W4200301384 modified "2023-10-16" @default.
- W4200301384 title "InterPepScore: A Deep Learning Score for Improving the FlexPepDock Refinement Protocol" @default.
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- W4200301384 doi "https://doi.org/10.1101/2021.12.09.471890" @default.
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