Protein-peptide interactions play an important role in many cellular processes. In silico prediction of protein-peptide complex structure is highly desirable for mechanistic investigation of these processes and for therapeutic design. However, predicting all-atom structures of protein-peptide complexes without any knowledge about the peptide binding site and the bound peptide conformation remains a big challenge. Here, we present a novel docking-based method for predicting protein-peptide complex structures, referred to as MDockPeP, which starts with the peptide sequence and globally docks the all-atom, flexible peptide onto the protein structure. MDockPeP was tested on the peptiDB benchmarking database, using both bound and unbound protein structures. The results show that MDockPeP successfully generated near-native peptide binding modes in 95.0% of the bound docking cases and in 92.2% of the unbound docking cases, respectively. At least one near-native peptide binding mode was ranked among top 10 (or top 100) in 54% (81%) of the bound cases, and in 38% (60%) of the unbound cases, respectively. MDockPeP is computationally efficient and can be used either as a standing-alone tool for large-scale protein-peptide docking or as an initial-stage sampling tool for protein-peptide structure refinement programs.

Predicting protein-peptide interactions based on the peptide sequence and the protein structure

254th National Meeting (2017)

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