SKATE: A docking program that decouples systematic sampling from scoring

SKATE is a docking prototype that decouples systematic sampling from scoring. This novel approach removes any interdependence between sampling and scoring functions to achieve better sampling and, thus, improves docking accuracy. SKATE systematically samples a ligand's conformational, rotationa...

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Bibliographic Details
Published inJournal of computational chemistry Vol. 31; no. 14; pp. 2540 - 2554
Main Authors Feng, Jianwen A, Marshall, Garland R
Format Journal Article
LanguageEnglish
Published Hoboken Wiley Subscription Services, Inc., A Wiley Company 15.11.2010
Wiley Subscription Services, Inc
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Summary:SKATE is a docking prototype that decouples systematic sampling from scoring. This novel approach removes any interdependence between sampling and scoring functions to achieve better sampling and, thus, improves docking accuracy. SKATE systematically samples a ligand's conformational, rotational and translational degrees of freedom, as constrained by a receptor pocket, to find sterically allowed poses. Efficient systematic sampling is achieved by pruning the combinatorial tree using aggregate assembly, discriminant analysis, adaptive sampling, radial sampling, and clustering. Because systematic sampling is decoupled from scoring, the poses generated by SKATE can be ranked by any published, or in-house, scoring function. To test the performance of SKATE, ligands from the Asetex/CDCC set, the Surflex set, and the Vertex set, a total of 266 complexes, were redocked to their respective receptors. The results show that SKATE was able to sample poses within 2 Å RMSD of the native structure for 98, 95, and 98% of the cases in the Astex/CDCC, Surflex, and Vertex sets, respectively. Cross-docking accuracy of SKATE was also assessed by docking 10 ligands to thymidine kinase and 73 ligands to cyclin-dependent kinase.
Bibliography:http://dx.doi.org/10.1002/jcc.21545
ArticleID:JCC21545
ark:/67375/WNG-M2HV34BJ-H
istex:10284E06CD8706232502A80105EB96DEBF1E7E15
NIH - No. RO1 GM068460
Division of Biology and Biomedical Science of Washington University, Kauffman Foundation
Computational Biology Training - No. GM 008802
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0192-8651
1096-987X
DOI:10.1002/jcc.21545