Skip to content

Furman-Lab/PatchMAN

Repository files navigation

PatchMAN protocol for blind peptide-protein docking









Description

PatchMAN (Patch-Motif AligNments) maps the receptor surface for local structural motif matches in structures of protein monomers and interfaces, to extract complementary fragments and derive templates for peptide-protein docking.

The protocol consists of 4 consecutive steps: (1) Definition of surface patches on the receptor; (2) Identification of structural motif matches in protein structures, and an interacting fragment that can be used as template for the bound peptide; (3) Generation of the peptide-protein complex template structure, by superimposing the interacting peptide back onto the receptor, and (4) Replacing side chains according to the peptide sequence (threading), refinement and scoring of the model.

Software prequisites

To run PatchMAN the following prequisites should be downloaded and installed:

  1. Python(3.5)
  2. PyRosetta
  3. MASTER v1.6
  4. Rosetta

Installation

  • Download and install PyRosetta and Rosetta
  • Set up MASTER
    • Download the source code of MASTER v1.6

    • In the Match.cpp file: Go to line 107 and add the following code (before return os;):

      double *T=((Match*)(&m))->getTranslation();
      double **R=((Match*)(&m))->getRotation();
      
      os << " T: " << T[0]    << " " << T[1]    << " " << T[2]    << " ";
      os << " U: " << R[0][0] << " " << R[0][1] << " " << R[0][2] << " "
                   << R[1][0] << " " << R[1][1] << " " << R[1][2] << " "
                   << R[2][0] << " " << R[2][1] << " " << R[2][2] << " ===" ;
                   
      
  • Follow the instruction in the INSTALL file to compile MASTER
  • Download MASTER database for template search

Running PatchMAN

A PatchMAN demo run can be found in the example_run folder

Citing PatchMAN

Matching protein surface structural patches for high-resolution blind peptide docking
A Khramushin, Z Ben-Aharon, T Tsaban, JK Varga, O Avraham, O Schueler-Furman. PNAS 119, 1–10, 2022.
https://doi.org/10.1073/pnas.2121153119