Exhaustive search and solvated interaction energy (SIE) for virtual screening and affinity prediction

  1. Get@NRC: Exhaustive search and solvated interaction energy (SIE) for virtual screening and affinity prediction (Opens in a new window)
DOIResolve DOI: http://doi.org/10.1007/s10822-011-9529-7
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Journal titleJournal of Computer Aided Molecular Design
Pages617633; # of pages: 17
SubjectVirtual screening; Docking; SIE; Solvated interaction energy; Binding affinity prediction; FiSH
AbstractWe carried out a prospective evaluation of the utility of the SIE (solvation interaction energy) scoring function for virtual screening and binding affinity prediction. Since experimental structures of the complexes were not provided, this was an exercise in virtual docking as well. We used our exhaustive docking program, Wilma, to provide high-quality poses that were rescored using SIE to provide binding affinity predictions. We also tested the combination of SIE with our latest solvation model, first shell of hydration (FiSH), which captures some of the discrete properties of water within a continuum model. We achieved good enrichment in virtual screening of fragments against trypsin, with an area under the curve of about 0.7 for the receiver operating characteristic curve. Moreover, the early enrichment performance was quite good with 50 percent of true actives recovered with a 15 percent false positive rate in a prospective calculation and with a 3 percent false positive rate in a retrospective application of SIE with FiSH. Binding affinity predictions for both trypsin and host–guest complexes were generally within 2 kcal/mol of the experimental values. However, the rank ordering of affinities differing by 2 kcal/mol or less was not well predicted. On the other hand, it was encouraging that the incorporation of a more sophisticated solvation model into SIE resulted in better discrimination of true binders from binders. This suggests that the inclusion of proper Physics in our models is a fruitful strategy for improving the reliability of our binding affinity predictions.
Publication date
AffiliationNRC Biotechnology Research Institute; National Research Council Canada
Peer reviewedYes
NRC number53158
NPARC number20217046
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Record identifier28616c11-7894-444b-9cd1-b14601f01c27
Record created2012-06-29
Record modified2016-05-09
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