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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Published in | Journal of computational chemistry Vol. 31; no. 14; pp. 2540 - 2554 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
Hoboken
Wiley Subscription Services, Inc., A Wiley Company
15.11.2010
Wiley Subscription Services, Inc |
Subjects | |
Online Access | Get full text |
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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. |
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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 |