Virtual Target Screening: Validation Using Kinase Inhibitors
Document Type
Article
Publication Date
2012
Digital Object Identifier (DOI)
https://doi.org/10.1021/ci300073m
Abstract
Computational methods involving virtual screening could potentially be employed to discover new biomolecular targets for an individual molecule of interest (MOI). However, existing scoring functions may not accurately differentiate proteins to which the MOI binds from a larger set of macromolecules in a protein structural database. An MOI will most likely have varying degrees of predicted binding affinities to many protein targets. However, correctly interpreting a docking score as a hit for the MOI docked to any individual protein can be problematic. In our method, which we term “Virtual Target Screening (VTS)”, a set of small drug-like molecules are docked against each structure in the protein library to produce benchmark statistics. This calibration provides a reference for each protein so that hits can be identified for an MOI. VTS can then be used as tool for: drug repositioning (repurposing), specificity and toxicity testing, identifying potential metabolites, probing protein structures for allosteric sites, and testing focused libraries (collection of MOIs with similar chemotypes) for selectivity. To validate our VTS method, twenty kinase inhibitors were docked to a collection of calibrated protein structures. Here, we report our results where VTS predicted protein kinases as hits in preference to other proteins in our database. Concurrently, a graphical interface for VTS was developed.
Was this content written or created while at USF?
Yes
Citation / Publisher Attribution
Journal of Chemical Information and Modeling, v. 52, issue 8, p. 2192-2203
Scholar Commons Citation
Santiago, Daniel N.; Pevzner, Yuri; Durand, Ashley A.; Tran, MinhPhuong; Scheerer, Rachel R.; Daniel, Kenyon; Sung, Shen-Shu; Woodcock, H. Lee III; Guida, Wayne C.; and Brooks, Wesley H., "Virtual Target Screening: Validation Using Kinase Inhibitors" (2012). Chemistry Faculty Publications. 177.
https://digitalcommons.usf.edu/chm_facpub/177
