Literature DB >> 26295792

High-resolution protein-protein docking by global optimization: recent advances and future challenges.

Hahnbeom Park1, Hasup Lee2, Chaok Seok3.   

Abstract

A computational protein-protein docking method that predicts atomic details of protein-protein interactions from protein monomer structures is an invaluable tool for understanding the molecular mechanisms of protein interactions and for designing molecules that control such interactions. Compared to low-resolution docking, high-resolution docking explores the conformational space in atomic resolution to provide predictions with atomic details. This allows for applications to more challenging docking problems that involve conformational changes induced by binding. Recently, high-resolution methods have become more promising as additional information such as global shapes or residue contacts are now available from experiments or sequence/structure data. In this review article, we highlight developments in high-resolution docking made during the last decade, specifically regarding global optimization methods employed by the docking methods. We also discuss two major challenges in high-resolution docking: prediction of backbone flexibility and water-mediated interactions.
Copyright © 2015 Elsevier Ltd. All rights reserved.

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Year:  2015        PMID: 26295792     DOI: 10.1016/j.sbi.2015.08.001

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


  14 in total

1.  Application of docking methodologies to modeled proteins.

Authors:  Amar Singh; Taras Dauzhenka; Petras J Kundrotas; Michael J E Sternberg; Ilya A Vakser
Journal:  Proteins       Date:  2020-03-20

2.  Putative model for heat shock protein 70 complexation with receptor of advanced glycation end products through fluorescence proximity assays and normal mode analyses.

Authors:  Marcelo Sartori Grunwald; Rodrigo Ligabue-Braun; Cristiane Santos Souza; Luana Heimfarth; Hugo Verli; Daniel Pens Gelain; José Cláudio Fonseca Moreira
Journal:  Cell Stress Chaperones       Date:  2016-11-17       Impact factor: 3.667

Review 3.  Algorithms for protein design.

Authors:  Pablo Gainza; Hunter M Nisonoff; Bruce R Donald
Journal:  Curr Opin Struct Biol       Date:  2016-04-14       Impact factor: 6.809

4.  Structural modeling defines transmembrane residues in ADAM17 that are crucial for Rhbdf2-ADAM17-dependent proteolysis.

Authors:  Xue Li; Thorsten Maretzky; Jose Manuel Perez-Aguilar; Sébastien Monette; Gisela Weskamp; Sylvain Le Gall; Bruce Beutler; Harel Weinstein; Carl P Blobel
Journal:  J Cell Sci       Date:  2017-01-19       Impact factor: 5.285

5.  A benchmark testing ground for integrating homology modeling and protein docking.

Authors:  Tanggis Bohnuud; Lingqi Luo; Shoshana J Wodak; Alexandre M J J Bonvin; Zhiping Weng; Sandor Vajda; Ora Schueler-Furman; Dima Kozakov
Journal:  Proteins       Date:  2016-11-13

6.  Human and server docking prediction for CAPRI round 30-35 using LZerD with combined scoring functions.

Authors:  Lenna X Peterson; Hyungrae Kim; Juan Esquivel-Rodriguez; Amitava Roy; Xusi Han; Woong-Hee Shin; Jian Zhang; Genki Terashi; Matt Lee; Daisuke Kihara
Journal:  Proteins       Date:  2016-10-14

7.  An Integrated Approach for Determining a Protein-Protein Binding Interface in Solution and an Evaluation of Hydrogen-Deuterium Exchange Kinetics for Adjudicating Candidate Docking Models.

Authors:  Mengru Mira Zhang; Brett R Beno; Richard Y-C Huang; Jagat Adhikari; Ekaterina G Deyanova; Jing Li; Guodong Chen; Michael L Gross
Journal:  Anal Chem       Date:  2019-11-22       Impact factor: 6.986

8.  Assessment of the CASP14 assembly predictions.

Authors:  Burcu Ozden; Andriy Kryshtafovych; Ezgi Karaca
Journal:  Proteins       Date:  2021-08-31

9.  Introducing a Clustering Step in a Consensus Approach for the Scoring of Protein-Protein Docking Models.

Authors:  Edrisse Chermak; Renato De Donato; Marc F Lensink; Andrea Petta; Luigi Serra; Vittorio Scarano; Luigi Cavallo; Romina Oliva
Journal:  PLoS One       Date:  2016-11-15       Impact factor: 3.240

Review 10.  Macromolecular modeling and design in Rosetta: recent methods and frameworks.

Authors:  Julia Koehler Leman; Brian D Weitzner; Steven M Lewis; Jared Adolf-Bryfogle; Nawsad Alam; Rebecca F Alford; Melanie Aprahamian; David Baker; Kyle A Barlow; Patrick Barth; Benjamin Basanta; Brian J Bender; Kristin Blacklock; Jaume Bonet; Scott E Boyken; Phil Bradley; Chris Bystroff; Patrick Conway; Seth Cooper; Bruno E Correia; Brian Coventry; Rhiju Das; René M De Jong; Frank DiMaio; Lorna Dsilva; Roland Dunbrack; Alexander S Ford; Brandon Frenz; Darwin Y Fu; Caleb Geniesse; Lukasz Goldschmidt; Ragul Gowthaman; Jeffrey J Gray; Dominik Gront; Sharon Guffy; Scott Horowitz; Po-Ssu Huang; Thomas Huber; Tim M Jacobs; Jeliazko R Jeliazkov; David K Johnson; Kalli Kappel; John Karanicolas; Hamed Khakzad; Karen R Khar; Sagar D Khare; Firas Khatib; Alisa Khramushin; Indigo C King; Robert Kleffner; Brian Koepnick; Tanja Kortemme; Georg Kuenze; Brian Kuhlman; Daisuke Kuroda; Jason W Labonte; Jason K Lai; Gideon Lapidoth; Andrew Leaver-Fay; Steffen Lindert; Thomas Linsky; Nir London; Joseph H Lubin; Sergey Lyskov; Jack Maguire; Lars Malmström; Enrique Marcos; Orly Marcu; Nicholas A Marze; Jens Meiler; Rocco Moretti; Vikram Khipple Mulligan; Santrupti Nerli; Christoffer Norn; Shane Ó'Conchúir; Noah Ollikainen; Sergey Ovchinnikov; Michael S Pacella; Xingjie Pan; Hahnbeom Park; Ryan E Pavlovicz; Manasi Pethe; Brian G Pierce; Kala Bharath Pilla; Barak Raveh; P Douglas Renfrew; Shourya S Roy Burman; Aliza Rubenstein; Marion F Sauer; Andreas Scheck; William Schief; Ora Schueler-Furman; Yuval Sedan; Alexander M Sevy; Nikolaos G Sgourakis; Lei Shi; Justin B Siegel; Daniel-Adriano Silva; Shannon Smith; Yifan Song; Amelie Stein; Maria Szegedy; Frank D Teets; Summer B Thyme; Ray Yu-Ruei Wang; Andrew Watkins; Lior Zimmerman; Richard Bonneau
Journal:  Nat Methods       Date:  2020-06-01       Impact factor: 28.547

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