LAUSR

Protein-peptide docking, which predicts the complex structure between a protein and a peptide, is a valuable computational tool in peptide therapeutics development and mechanistic investigation of peptides involved cellular processes. Although current peptide docking approaches are often able to sample near-native peptide binding modes, correctly identifying those near-native modes from decoys is still challenging due to the extremely high complexity of peptide binding energy landscape. In this study, we have developed an efficient post-docking rescoring protocol using a combined scoring function of knowledge-based ITScorePP potentials and physics- based MM-GBSA energies. Tested on five benchmark/docking test sets, our post-docking strategy showed an overall significantly better performance in binding mode prediction and score-RMSD correlation than original docking approaches. Specifically, our post-docking protocol outperformed original docking approaches with the success rates of 15.8% vs. 10.5% for pepATTRACT on the Global 57 benchmark, 5.3% vs. 5.3% for CABS-dock on the Global 57 benchmark, 17.0% vs. 11.3% for FlexPepDock on the LEADS-PEP data set, 40.3% vs. 33.9% for HPEPDOCK on the Local 62 benchmark, and 64.2% vs. 52.8% for HPEPDOCK on the LEADS-PEP data set when the top prediction was considered. There results demonstrated the efficacy and robustness of our post-docking protocol.