Re-ranking of computational protein-peptide docking solutions with amino acid profiles of rigid-body docking results

Abstract

Protein-peptide interactions, in which one partner is a globular protein and the other is a flexible linear peptide, are important for understanding cellular processes and regulatory pathways, and are therefore targets for drug discovery. In this study, I combined rigid-body protein-protein docking software (MEGADOCK) and global flexible protein-peptide docking software (CABS-dock) to establish a re-ranking method with amino acid contact profiles using rigid-body sampling decoys. I demonstrate that the correct complex structure cannot be predicted (< 10 [A] peptide RMSD) using the current version of CABS-dock alone. However, my newly proposed re-ranking method based on the amino acid contact profile using rigid-body search results (designated the decoy profile) demonstrated the possibility of improvement of predictions. Adoption of my proposed method along with continuous efforts for effective computational modeling of protein-peptide interactions can provide useful information to understand complex biological processes in molecular detail and modulate protein-protein interactions in disease treatment.