title: Fully Blind Docking at the Atomic Level for Protein-Peptide Complex Structure Prediction.
abstract: 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 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. The performance is significantly better than other existing docking methods. MDockPeP is computationally efficient and suitable for large-scale applications.
authors.affiliation: Department of Physics and Astronomy, Dalton Cardiovascular Research Center, University of Missouri, Columbia, MO 65211, USA; Department of Biochemistry, Informatics Institute, University of Missouri, Columbia, MO 65211, USA.
authors.forename: Chengfei
authors.initials: C
authors.lastname: Yan
authors.affiliation: Department of Physics and Astronomy, Dalton Cardiovascular Research Center, University of Missouri, Columbia, MO 65211, USA; Department of Biochemistry, Informatics Institute, University of Missouri, Columbia, MO 65211, USA.
authors.forename: Xianjin
authors.initials: X
authors.lastname: Xu
authors.affiliation: Department of Physics and Astronomy, Dalton Cardiovascular Research Center, University of Missouri, Columbia, MO 65211, USA; Department of Biochemistry, Informatics Institute, University of Missouri, Columbia, MO 65211, USA. Electronic address: zoux@missouri.edu.
authors.forename: Xiaoqin
authors.initials: X
authors.lastname: Zou
mesh_terms: D008958:Models, Molecular; D062105:Molecular Docking Simulation / Q000379:methods*; D010455:Peptides / Q000737:chemistry / Q000378:metabolism*; D011485:Protein Binding; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism; D012984:Software
chemical_list: D010455:Peptides; D011506:Proteins