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J Chem Inf Model. 2007 May-Jun;47(3):1258-62. Epub 2007 Apr 21.

Analysis of HIV wild-type and mutant structures via in silico docking against diverse ligand libraries.

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  • 1Bioinformatics Program, University of California-San Diego, La Jolla, California 92093, USA. mchang@ucsd.edu

Abstract

The FightAIDS@Home distributed computing project uses AutoDock for an initial virtual screen of HIV protease structures against a broad range of 1771 ligands including both known protease inhibitors and a diverse library of other ligands. The volume of results allows novel large-scale analyses of binding energy "profiles" for HIV structures. Beyond identifying potential lead compounds, these characterizations provide methods for choosing representative wild-type and mutant protein structures from the larger set. From the binding energy profiles of the PDB structures, a principal component analysis based analysis identifies seven "spanning" proteases. A complementary analysis finds that the wild-type protease structure 2BPZ best captures the central tendency of the protease set. Using a comparison of known protease inhibitors against the diverse ligand set yields an AutoDock binding energy "significance" threshold of -7.0 kcal/mol between significant, strongly binding ligands and other weak/nonspecific binding energies. This threshold captures nearly 98% of known inhibitor interactions while rejecting more than 95% of suspected noninhibitor interactions. These methods should be of general use in virtual screening projects and will be used to improve further FightAIDS@Home experiments.

PMID:
17447753
[PubMed - indexed for MEDLINE]
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