Open protocols for docking and MD-based scoring of peptide substrates

Abstract

The study of protein-peptide interactions is an active research field from an experimental and computational perspective, with the latest presenting challenges to model and simulate the peptides' intrinsic flexibility. Predicting affinities towards protein systems of interest, such as proteases, is crucial to understand the specificity of the interactions and support the discovery of novel substrates. Here we provide a set of computational protocols to run structural and dynamical analysis of protein-peptide complexes from a binding perspective. The protocols are based on state-of-the-art methods, but the code is open and can be customized depending on the user needs. These include a fragment-growing peptide docking protocol to predict bound conformations of flexible peptides, a protocol to extract descriptors from protein-peptide molecular dynamics trajectories, and a workflow to build and test machine learning regression models. As a toy example, we applied the protocols to a serine protease structure with a set of known peptide substrates and random sequences to illustrate the use of the code, which is publicly available at: https://github.com/rochoa85/Protocols-Peptide-Binding

Keywords

• Peptide
• Docking
• Molecular dynamics
• Machine learning