henhaobio
Sign in

Proceedings of the National Academy of SciencesBioinformaticsPeer-reviewed journal article

PatchMAN docking: Modeling peptide-protein interactions in the context of the receptor surface

Alisa KhramushiniD, Ziv Ben-AharoniD, Tomer TsabaniD, Julia K. VargaiD, Orly AvrahamiD, Ora Schueler‐FurmaniD

PDFDOIPreprint

Abstract

Peptide docking can be perceived as a subproblem of protein-protein docking. However, due to the short length and flexible nature of peptides, many do not adopt one defined conformation prior to binding. Therefore, to tackle a peptide docking problem, not only the relative orientation between the two partners, but also the bound conformation of the peptide needs to be modeled. Traditional peptide-centered approaches use information about the peptide sequence to generate a representative conformer ensemble, which can then be rigid body docked to the receptor. Alternatively, one may look at this problem from the viewpoint of the receptor, namely that the protein surface defines the peptide bound conformation.We present PatchMAN (Patch-Motif AligNments), a novel peptide docking approach which uses structural motifs to map the receptor surface with backbone scaffolds extracted from protein structures. On a non-redundant set of protein-peptide complexes, starting from free receptor structures, PatchMAN successfully models and identifies near-native peptide-protein complexes in 62% / 81% within 2.5[A] / 5[A] RMSD, with corresponding sampling in 81% / 100% of the cases, outperforming other approaches. PatchMAN leverages the observation that structural units of peptides with their binding pocket can be found not only within interfaces, but also within monomers. We show that the conformation of the bound peptide is sampled based on the structural context of the receptor only, without taking into account any sequence information. Beyond peptide docking, this approach opens exciting new avenues to study principles of peptide-protein association, and to the design of new peptide binders.

Citations
41
FWCI
2.61
Field percentile
top 8%

Reproducibility ledger

Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.

Sign in to file a reproducibility report.

Similarity Map
SelectedSame fieldCross-field
THISTrabelsi '26Alam '17Johansson-Å '19Wang '19Tsaban '22Abdin '22
80% semantic matchBioinformatics
Nirit Trabelsi, Julia K. Varga, Alisa Khramushin+2 more·Journal of Chemical Information and Modeling·Jun 16, 2026