MDockPeP: An ab‐initio protein–peptide docking server. Issue 28 (23rd October 2018)

• Record Type: Journal Article
• Authors: Xu, Xianjin; Yan, Chengfei; Zou, Xiaoqin
• Is Part Of: Journal of computational chemistry. Volume 39:Issue 28(2018)
• Page Start: 2409
• Page End: 2413
• Publication Date: 2018-10-23
• Subjects: protein–peptide interactions -- complex structure prediction -- molecular docking -- molecular modeling -- web server; Chemistry -- Data processing -- Periodicals; 542.85
• DOI: 10.1002/jcc.25555
• Languages: English
• ISSNs: 0192-8651

Abstract

Abstract : Protein–peptide interactions play a crucial role in a variety of cellular processes. The protein–peptide complex structure is a key to understand the mechanisms underlying protein–peptide interactions and is critical for peptide therapeutic development. We present a user‐friendly protein–peptide docking server, MDockPeP. Starting from a peptide sequence and a protein receptor structure, the MDockPeP Server globally docks the all‐atom, flexible peptide to the protein receptor. The produced modes are then evaluated with a statistical potential‐based scoring function, ITScorePeP. This method was systematically validated using the peptiDB benchmarking database. At least one near‐native peptide binding mode was ranked among top 10 (or top 500) in 59% (85%) of the bound cases, and in 40.6% (71.9%) of the challenging unbound cases. The server can be used for both protein–peptide complex structure prediction and initial‐stage sampling of the protein–peptide binding modes for other docking or simulation methods. MDockPeP Server is freely available athttp://zougrouptoolkit.missouri.edu/mdockpep . © 2018 Wiley Periodicals, Inc. Abstract : MDockPeP is a publicly accessible web server (http://zougrouptoolkit.missouri.edu/mdockpep ) for predicting protein–peptide complex structures. The server requires only the peptide sequence and the protein structure. MDockPeP docks the all‐atom, flexible peptide onto the whole protein without the knowledge of the binding site. MDockPeP is computationally efficient, and achieves excellent performance on mode sampling and good performance on mode prediction.