Protein-peptide interactions play an important role in many cellular processes and in peptide-based therapeutic interventions. Major challenges in the prediction of protein-peptide complexes, particularly for relatively long peptides (e.g., peptides longer than 15 residues), include peptide flexibility and binding mode ranking. Recently, we developed a new strategy, MDockPeP2, which efficiently addresses these challenges by using the structural information embedded in abundant monomeric proteins with an exhaustive search strategy, in combination with an integrated global search, local flexible refinement method. Only the peptide sequence and the protein crystal structure are required. The method was systemically assessed using a newly constructed structural database of 89 non-redundant protein-peptide complexes with peptide sequence length ranging from 5 to 29, in which about half of the peptides are longer than 15 residues. MDockPeP2 generated near-native binding modes for 89.9% of the bound cases (i.e, with bound receptor structures and fully flexible peptides) and 84.5% of the unbound cases. The success rates for sampling were even higher when only short- and medium-size peptides (≤ 15 residues) were considered, 100% for the bound cases and 93.6% for the unbound cases. The overall success rate after ranking is 62.0% for bound docking and 40.0% for unbound docking for the top 10 models.<br/>

MDockPeP2: Predicting protein-peptide complex structures by accounting for peptide flexibility in long peptides

2019 Fall National Meeting

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