Literature DB >> 24138780

Peptide docking and structure-based characterization of peptide binding: from knowledge to know-how.

Nir London1, Barak Raveh, Ora Schueler-Furman.   

Abstract

Peptide-mediated interactions are gaining increased attention due to their predominant roles in the many regulatory processes that involve dynamic interactions between proteins. The structures of such interactions provide an excellent starting point for their characterization and manipulation, and can provide leads for targeted inhibitor design. The relatively few experimentally determined structures of peptide-protein complexes can be complemented with an outburst of modeling approaches that have been introduced in recent years, with increasing accuracy and applicability to ever more systems. We review different methods to address the considerable challenges in modeling the binding of a short yet highly flexible peptide to its partner. These methods apply an array of sampling strategies and draw from a recent amassing of knowledge about the biophysical nature of peptide-protein interactions. We elaborate on applications of these structure-based approaches and in particular on the characterization of peptide binding specificity to different peptide-binding domains and enzymes. Such applications can identify new biological targets and thus complement our current view of protein-protein interactions in living organisms. Accurate peptide-protein docking is of particular importance in the light of increased appreciation of the crucial functional roles of disordered regions and the many linear binding motifs embedded within.
Copyright © 2013 Elsevier Ltd. All rights reserved.

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Year:  2013        PMID: 24138780     DOI: 10.1016/j.sbi.2013.07.006

Source DB:  PubMed          Journal:  Curr Opin Struct Biol        ISSN: 0959-440X            Impact factor:   6.809


  25 in total

1.  Detection of peptide-binding sites on protein surfaces: the first step toward the modeling and targeting of peptide-mediated interactions.

Authors:  Assaf Lavi; Chi Ho Ngan; Dana Movshovitz-Attias; Tanggis Bohnuud; Christine Yueh; Dmitri Beglov; Ora Schueler-Furman; Dima Kozakov
Journal:  Proteins       Date:  2013-10-17

2.  AutoDock CrankPep: combining folding and docking to predict protein-peptide complexes.

Authors:  Yuqi Zhang; Michel F Sanner
Journal:  Bioinformatics       Date:  2019-12-15       Impact factor: 6.937

3.  Flexible docking of peptides to proteins using CABS-dock.

Authors:  Mateusz Kurcinski; Aleksandra Badaczewska-Dawid; Michal Kolinski; Andrzej Kolinski; Sebastian Kmiecik
Journal:  Protein Sci       Date:  2019-11-11       Impact factor: 6.725

4.  GalaxyPepDock: a protein-peptide docking tool based on interaction similarity and energy optimization.

Authors:  Hasup Lee; Lim Heo; Myeong Sup Lee; Chaok Seok
Journal:  Nucleic Acids Res       Date:  2015-05-12       Impact factor: 16.971

5.  Enriching Peptide Libraries for Binding Affinity and Specificity Through Computationally Directed Library Design.

Authors:  Glenna Wink Foight; T Scott Chen; Daniel Richman; Amy E Keating
Journal:  Methods Mol Biol       Date:  2017

6.  Predicting Protein-Peptide Complex Structures by Accounting for Peptide Flexibility and the Physicochemical Environment.

Authors:  Xianjin Xu; Xiaoqin Zou
Journal:  J Chem Inf Model       Date:  2021-12-21       Impact factor: 6.162

7.  Protocol for iterative optimization of modified peptides bound to protein targets.

Authors:  Rodrigo Ochoa; Pilar Cossio; Thomas Fox
Journal:  J Comput Aided Mol Des       Date:  2022-10-19       Impact factor: 4.179

8.  PEP-SiteFinder: a tool for the blind identification of peptide binding sites on protein surfaces.

Authors:  Adrien Saladin; Julien Rey; Pierre Thévenet; Martin Zacharias; Gautier Moroy; Pierre Tufféry
Journal:  Nucleic Acids Res       Date:  2014-05-06       Impact factor: 16.971

Review 9.  Systematic Targeting of Protein-Protein Interactions.

Authors:  Ashley E Modell; Sarah L Blosser; Paramjit S Arora
Journal:  Trends Pharmacol Sci       Date:  2016-06-04       Impact factor: 14.819

10.  PepPro: A Nonredundant Structure Data Set for Benchmarking Peptide-Protein Computational Docking.

Authors:  Xianjin Xu; Xiaoqin Zou
Journal:  J Comput Chem       Date:  2019-12-02       Impact factor: 3.376

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