Literature DB >> 10483527

MCDOCK: a Monte Carlo simulation approach to the molecular docking problem.

M Liu1, S Wang.   

Abstract

Prediction of the binding mode of a ligand (a drug molecule) to its macromolecular receptor, or molecular docking, is an important problem in rational drug design. We have developed a new docking method in which a non-conventional Monte Carlo (MC) simulation technique is employed. A computer program, MCDOCK, was developed to carry out the molecular docking operation automatically. The current version of the MCDOCK program (version 1.0) allows for the full flexibility of ligands in the docking calculations. The scoring function used in MCDOCK is the sum of the interaction energy between the ligand and its receptor, and the conformational energy of the ligand. To validate the MCDOCK method, 19 small ligands, the binding modes of which had been determined experimentally using X-ray diffraction, were docked into their receptor binding sites. To produce statistically significant results, 20 MCDOCK runs were performed for each protein-ligand complex. It was found that a significant percentage of these MCDOCK runs converge to the experimentally observed binding mode. The root-mean-square (rms) of all non-hydrogen atoms of the ligand between the predicted and experimental binding modes ranges from 0.25 to 1.84 A for these 19 cases. The computational time for each run on an SGI Indigo2/R10000 varies from less than 1 min to 15 min, depending upon the size and the flexibility of the ligands. Thus MCDOCK may be used to predict the precise binding mode of ligands in lead optimization and to discover novel lead compounds through structure-based database searching.

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Year:  1999        PMID: 10483527     DOI: 10.1023/a:1008005918983

Source DB:  PubMed          Journal:  J Comput Aided Mol Des        ISSN: 0920-654X            Impact factor:   3.686


  23 in total

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Journal:  J Comput Aided Mol Des       Date:  1995-04       Impact factor: 3.686

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Authors:  H J Böhm
Journal:  J Comput Aided Mol Des       Date:  1994-06       Impact factor: 3.686

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Journal:  J Comput Aided Mol Des       Date:  1994-12       Impact factor: 3.686

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  21 in total

1.  Ligand-receptor docking with the Mining Minima optimizer.

Authors:  L David; R Luo; M K Gilson
Journal:  J Comput Aided Mol Des       Date:  2001-02       Impact factor: 3.686

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Journal:  J Comput Aided Mol Des       Date:  2001-10       Impact factor: 3.686

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Authors:  R D Taylor; P J Jewsbury; J W Essex
Journal:  J Comput Aided Mol Des       Date:  2002-03       Impact factor: 3.686

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Authors:  Badry D Bursulaya; Maxim Totrov; Ruben Abagyan; Charles L Brooks
Journal:  J Comput Aided Mol Des       Date:  2003-11       Impact factor: 3.686

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Journal:  Br J Pharmacol       Date:  2007-11-26       Impact factor: 8.739

Review 7.  Computational methods in drug discovery.

Authors:  Gregory Sliwoski; Sandeepkumar Kothiwale; Jens Meiler; Edward W Lowe
Journal:  Pharmacol Rev       Date:  2013-12-31       Impact factor: 25.468

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Authors:  Ling Kang; Honglin Li; Hualiang Jiang; Xicheng Wang
Journal:  J Comput Aided Mol Des       Date:  2008-09-06       Impact factor: 3.686

9.  A fast protein-ligand docking algorithm based on hydrogen bond matching and surface shape complementarity.

Authors:  Wenjia Luo; Jianfeng Pei; Yushan Zhu
Journal:  J Mol Model       Date:  2009-10-13       Impact factor: 1.810

10.  An effective docking strategy for virtual screening based on multi-objective optimization algorithm.

Authors:  Honglin Li; Hailei Zhang; Mingyue Zheng; Jie Luo; Ling Kang; Xiaofeng Liu; Xicheng Wang; Hualiang Jiang
Journal:  BMC Bioinformatics       Date:  2009-02-11       Impact factor: 3.169

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