J Chem Inf Model - Development of a minimal kinase ensemble receptor (MKER) for surrogate AutoShim.

Tópicos

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Resumo

The target-tailored 3-D virtual screening (VS) method "Surrogate AutoShim" adds pharmacophoric shims to a 16-kinase crystal structure "Universal Kinase Ensemble Receptor" (UKER) to generate highly predictive, target-customized docking models. Predocking a corporate archive of millions of compounds into the 16-structure ensemble takes months. However, since the 16 UKER structures are always the same, docking need only be done once. The predocked results are then "shimmed" to reproduce experimental training data for any number of additional kinases far more accurately than conventional docking. Training new kinase models and predicting activity for millions of predocked compounds against dozens of kinases takes only hours. However reducing the predocking time would make the method even more advantageous. Sequential Floating Forward Search (SFFS) was employed to rationally identify a reduced subset using only 8 of the 16 structures, a "Minimal Kinase Ensemble Receptor" (MKER) that preserved the predictive accuracy for 20 kinase models. Furthermore, a performance evaluation of this subset on an extended set of 52 kinase targets and 100,000 compounds showed statistical model performance comparable to the original UKER. The MKER has halved the time for predocking large databases of internal and commercial compounds. For ad hoc virtual libraries, where predocking is not possible, 2- or 3-kinases "Approximate Kinase Ensemble Receptors" (AKER) were also identified with only a modest loss of prediction accuracy.

Resumo Limpo

targettailor d virtual screen vs method surrog autoshim add pharmacophor shim kinas crystal structur univers kinas ensembl receptor uker generat high predict targetcustom dock model predock corpor archiv million compound structur ensembl take month howev sinc uker structur alway dock need done predock result shim reproduc experiment train data number addit kinas far accur convent dock train new kinas model predict activ million predock compound dozen kinas take hour howev reduc predock time make method even advantag sequenti float forward search sffs employ ration identifi reduc subset use structur minim kinas ensembl receptor mker preserv predict accuraci kinas model furthermor perform evalu subset extend set kinas target compound show statist model perform compar origin uker mker halv time predock larg databas intern commerci compound ad hoc virtual librari predock possibl kinas approxim kinas ensembl receptor aker also identifi modest loss predict accuraci

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