Literature DB >> 35482919

Matching protein surface structural patches for high-resolution blind peptide docking.

Alisa Khramushin1, Ziv Ben-Aharon1, Tomer Tsaban1, Julia K Varga1, Orly Avraham1, Ora Schueler-Furman1.   

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, but also the bound conformation of the peptide needs to be modeled. Traditional peptide-centered approaches use information about peptide sequences to generate representative conformer ensembles, 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. Here, we present PatchMAN (Patch-Motif AligNments), a global peptide-docking approach that uses structural motifs to map the receptor surface with backbone scaffolds extracted from protein structures. On a nonredundant set of protein–peptide complexes, starting from free receptor structures, PatchMAN successfully models and identifies near-native peptide–protein complexes in 58%/84% within 2.5 Å/5 Å interface backbone 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 bound peptide conformation 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. PatchMAN is available as a server at https://furmanlab.cs.huji.ac.il/patchman/.

Entities:  

Keywords:  peptide docking; protein structure; structural motifs; structure matching; surface complementation

Mesh:

Substances:

Year:  2022        PMID: 35482919      PMCID: PMC9170164          DOI: 10.1073/pnas.2121153119

Source DB:  PubMed          Journal:  Proc Natl Acad Sci U S A        ISSN: 0027-8424            Impact factor:   12.779


  40 in total

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Journal:  Nucleic Acids Res       Date:  2000-01-01       Impact factor: 16.971

2.  Structure of the ERM protein moesin reveals the FERM domain fold masked by an extended actin binding tail domain.

Authors:  M A Pearson; D Reczek; A Bretscher; P A Karplus
Journal:  Cell       Date:  2000-04-28       Impact factor: 41.582

Review 3.  Trends in peptide drug discovery.

Authors:  Markus Muttenthaler; Glenn F King; David J Adams; Paul F Alewood
Journal:  Nat Rev Drug Discov       Date:  2021-02-03       Impact factor: 84.694

4.  Modeling Peptide-Protein Structure and Binding Using Monte Carlo Sampling Approaches: Rosetta FlexPepDock and FlexPepBind.

Authors:  Nawsad Alam; Ora Schueler-Furman
Journal:  Methods Mol Biol       Date:  2017

5.  Protein-peptide complex prediction through fragment interaction patterns.

Authors:  Erik Verschueren; Peter Vanhee; Frederic Rousseau; Joost Schymkowitz; Luis Serrano
Journal:  Structure       Date:  2013-04-11       Impact factor: 5.006

6.  Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features.

Authors:  W Kabsch; C Sander
Journal:  Biopolymers       Date:  1983-12       Impact factor: 2.505

Review 7.  Intrinsically disordered proteins: regulation and disease.

Authors:  M Madan Babu; Robin van der Lee; Natalia Sanchez de Groot; Jörg Gsponer
Journal:  Curr Opin Struct Biol       Date:  2011-04-20       Impact factor: 6.809

8.  Rosetta FlexPepDock ab-initio: simultaneous folding, docking and refinement of peptides onto their receptors.

Authors:  Barak Raveh; Nir London; Lior Zimmerman; Ora Schueler-Furman
Journal:  PLoS One       Date:  2011-04-29       Impact factor: 3.240

9.  Generalized fragment picking in Rosetta: design, protocols and applications.

Authors:  Dominik Gront; Daniel W Kulp; Robert M Vernon; Charlie E M Strauss; David Baker
Journal:  PLoS One       Date:  2011-08-24       Impact factor: 3.240

10.  Highly accurate protein structure prediction with AlphaFold.

Authors:  John Jumper; Richard Evans; Alexander Pritzel; Tim Green; Michael Figurnov; Olaf Ronneberger; Kathryn Tunyasuvunakool; Russ Bates; Augustin Žídek; Anna Potapenko; Alex Bridgland; Clemens Meyer; Simon A A Kohl; Andrew J Ballard; Andrew Cowie; Bernardino Romera-Paredes; Stanislav Nikolov; Rishub Jain; Demis Hassabis; Jonas Adler; Trevor Back; Stig Petersen; David Reiman; Ellen Clancy; Michal Zielinski; Martin Steinegger; Michalina Pacholska; Tamas Berghammer; Sebastian Bodenstein; David Silver; Oriol Vinyals; Andrew W Senior; Koray Kavukcuoglu; Pushmeet Kohli
Journal:  Nature       Date:  2021-07-15       Impact factor: 49.962

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