Literature DB >> 15048822

GEMDOCK: a generic evolutionary method for molecular docking.

Jinn-Moon Yang1, Chun-Chen Chen.   

Abstract

We have developed an evolutionary approach for flexible ligand docking. This approval, GEMDOCK, uses a Generic Evolutionary Method for molecular DOCKing and an empirical scoring function. The former combines both discrete and continuous global search strategies with local search strategies to speed up convergence, whereas the latter results in rapid recognition of potential ligands. GEMDOCK was tested on a diverse data set of 100 protein-ligand complexes from the Protein Data Bank. In 79% of these complexes, the docked lowest energy ligand structures had root-mean-square derivations (RMSDs) below 2.0 A with respect to the corresponding crystal structures. The success rate increased to 85% if the structure water molecules were retained. We evaluated GEMDOCK on two cross-docking experiments in which each ligand of a protein ensemble was docked into each protein of the ensemble. Seventy-six percent of the docked structures had RMSDs below 2.0 A when the ligands were docked into foreign structures. We analyzed and validated GEMDOCK with respect to various search spaces and scoring functions, and found that if the scoring function was perfect, then the predicted accuracy was also essentially perfect. This study suggests that GEMDOCK is a useful tool for molecular recognition and may be used to systematically evaluate and thus improve scoring functions. Copyright 2004 Wiley-Liss, Inc.

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Year:  2004        PMID: 15048822     DOI: 10.1002/prot.20035

Source DB:  PubMed          Journal:  Proteins        ISSN: 0887-3585


  108 in total

1.  Physicochemical and residue conservation calculations to improve the ranking of protein-protein docking solutions.

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Review 3.  Ligand discovery and virtual screening using the program LIDAEUS.

Authors:  P Taylor; E Blackburn; Y G Sheng; S Harding; K-Y Hsin; D Kan; S Shave; M D Walkinshaw
Journal:  Br J Pharmacol       Date:  2007-11-26       Impact factor: 8.739

4.  Assessment of programs for ligand binding affinity prediction.

Authors:  Ryangguk Kim; Jeffrey Skolnick
Journal:  J Comput Chem       Date:  2008-06       Impact factor: 3.376

5.  Identification of new potential Mycobacterium tuberculosis shikimate kinase inhibitors through molecular docking simulations.

Authors:  Carolina Pasa Vianna; Walter F de Azevedo
Journal:  J Mol Model       Date:  2011-05-19       Impact factor: 1.810

6.  A comparison of various optimization algorithms of protein-ligand docking programs by fitness accuracy.

Authors:  Liyong Guo; Zhiqiang Yan; Xiliang Zheng; Liang Hu; Yongliang Yang; Jin Wang
Journal:  J Mol Model       Date:  2014-06-17       Impact factor: 1.810

7.  Molecular Docking Study for Inhibitors of Aggregatibacter actinomycetamcomitans Toxins in Treatment of Aggressive Perioodontitis.

Authors:  Makesh Raj L S; Jude J; Kannan I; Sai Krishna P; Shankar K A
Journal:  J Clin Diagn Res       Date:  2014-11-20

8.  SiMMap: a web server for inferring site-moiety map to recognize interaction preferences between protein pockets and compound moieties.

Authors:  Yen-Fu Chen; Kai-Cheng Hsu; Shen-Rong Lin; Wen-Ching Wang; Yu-Chi Huang; Jinn-Moon Yang
Journal:  Nucleic Acids Res       Date:  2010-06-02       Impact factor: 16.971

9.  In silico modeling of the specific inhibitory potential of thiophene-2,3-dihydro-1,5-benzothiazepine against BChE in the formation of beta-amyloid plaques associated with Alzheimer's disease.

Authors:  Zaheer Ul-Haq; Waqasuddin Khan; Saima Kalsoom; Farzana L Ansari
Journal:  Theor Biol Med Model       Date:  2010-06-16       Impact factor: 2.432

10.  Molecular docking studies on tetrahydroimidazo-[4,5,1-jk][1,4]-benzodiazepinone (TIBO) derivatives as HIV-1 NNRT inhibitors.

Authors:  Nitin S Sapre; Swagata Gupta; Nilanjana Pancholi; Neelima Sapre
Journal:  J Comput Aided Mol Des       Date:  2007-12-28       Impact factor: 3.686

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