Literature DB >> 16988956

Critical assessment of the automated AutoDock as a new docking tool for virtual screening.

Hwangseo Park1, Jinuk Lee, Sangyoub Lee.   

Abstract

A major problem in virtual screening concerns the accuracy of the binding free energy between a target protein and a putative ligand. Here we report an example supporting the outperformance of the AutoDock scoring function in virtual screening in comparison to the other popular docking programs. The original AutoDock program is in itself inefficient to be used in virtual screening because the grids of interaction energy have to be calculated for each putative ligand in chemical database. However, the automation of the AutoDock program with the potential grids defined in common for all putative ligands leads to more than twofold increase in the speed of virtual database screening. The utility of the automated AutoDock in virtual screening is further demonstrated by identifying the actual inhibitors of various target enzymes in chemical databases with accuracy higher than the other docking tools including DOCK and FlexX. These results exemplify the usefulness of the automated AutoDock as a new promising tool in structure-based virtual screening. (c) 2006 Wiley-Liss, Inc.

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Substances:

Year:  2006        PMID: 16988956     DOI: 10.1002/prot.21183

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


  42 in total

1.  Discriminating of HMG-CoA reductase inhibitors and decoys using self-organizing maps.

Authors:  Zhi Wang; Aixia Yan
Journal:  Mol Divers       Date:  2010-11-12       Impact factor: 2.943

2.  Potentiation of tumor necrosis factor-alpha-induced tumor cell apoptosis by a small molecule inhibitor for anti-apoptotic protein hPEBP4.

Authors:  Jianming Qiu; Jianfeng Xiao; Chaofeng Han; Nan Li; Xu Shen; Hualiang Jiang; Xuetao Cao
Journal:  J Biol Chem       Date:  2010-02-22       Impact factor: 5.157

3.  Differentiation of AmpC beta-lactamase binders vs. decoys using classification kNN QSAR modeling and application of the QSAR classifier to virtual screening.

Authors:  Jui-Hua Hsieh; Xiang S Wang; Denise Teotico; Alexander Golbraikh; Alexander Tropsha
Journal:  J Comput Aided Mol Des       Date:  2008-03-13       Impact factor: 3.686

Review 4.  The application of FAST-NMR for the identification of novel drug discovery targets.

Authors:  Robert Powers; Kelly A Mercier; Jennifer C Copeland
Journal:  Drug Discov Today       Date:  2008-02       Impact factor: 7.851

5.  Toward the virtual screening of Cdc25A phosphatase inhibitors with the homology modeled protein structure.

Authors:  Hwangseo Park; Young Ho Jeon
Journal:  J Mol Model       Date:  2008-05-27       Impact factor: 1.810

6.  Docking and molecular dynamics studies on triclosan derivatives binding to FabI.

Authors:  Xuyun Yang; Junrui Lu; Ming Ying; Jiangbei Mu; Peichun Li; Yue Liu
Journal:  J Mol Model       Date:  2017-01-07       Impact factor: 1.810

7.  Identification of novel inhibitors of mitogen-activated protein kinase phosphatase-1 with structure-based virtual screening.

Authors:  Hwangseo Park; Jeong-Yi Jeon; Song Yi Kim; Dae Gwin Jeong; Seong Eon Ryu
Journal:  J Comput Aided Mol Des       Date:  2011-05-13       Impact factor: 3.686

8.  Two-track virtual screening approach to identify both competitive and allosteric inhibitors of human small C-terminal domain phosphatase 1.

Authors:  Hwangseo Park; Hye Seon Lee; Bonsu Ku; Sang-Rae Lee; Seung Jun Kim
Journal:  J Comput Aided Mol Des       Date:  2017-06-26       Impact factor: 3.686

9.  Peptide aptamer identified by molecular docking targeting translationally controlled tumor protein in leukemia cells.

Authors:  Onat Kadioglu; Thomas Efferth
Journal:  Invest New Drugs       Date:  2016-03-14       Impact factor: 3.850

10.  Identification of common inhibitors of wild-type and T315I mutant of BCR-ABL through the parallel structure-based virtual screening.

Authors:  Hwangseo Park; Seunghee Hong; Sungwoo Hong
Journal:  J Comput Aided Mol Des       Date:  2012-08-11       Impact factor: 3.686

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