Knowledge-Based Methods to Train and Optimize Virtual Screening Ensembles

• Published in: Journal of Chemical Information and Modeling
• Main Author: Swift R.V.; Jusoh S.A.; Offutt T.L.; Li E.S.; Amaro R.E.
• Format: Article
• Language: English
• Published: American Chemical Society 2016
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84971219993&doi=10.1021%2facs.jcim.5b00684&partnerID=40&md5=e1c545b7d790cb97afa869b05c307181

Ensemble docking can be a successful virtual screening technique that addresses the innate conformational heterogeneity of macromolecular drug targets. Yet, lacking a method to identify a subset of conformational states that effectively segregates active and inactive small molecules, ensemble docking may result in the recommendation of a large number of false positives. Here, three knowledge-based methods that construct structural ensembles for virtual screening are presented. Each method selects ensembles by optimizing an objective function calculated using the receiver operating characteristic (ROC) curve: either the area under the ROC curve (AUC) or a ROC enrichment factor (EF). As the number of receptor conformations, N, becomes large, the methods differ in their asymptotic scaling. Given a set of small molecules with known activities and a collection of target conformations, the most resource intense method is guaranteed to find the optimal ensemble but scales as O(2N). A recursive approximation to the optimal solution scales as O(N2), and a more severe approximation leads to a faster method that scales linearly, O(N). The techniques are generally applicable to any system, and we demonstrate their effectiveness on the androgen nuclear hormone receptor (AR), cyclin-dependent kinase 2 (CDK2), and the peroxisome proliferator-Activated receptor (PPAR) drug targets. Conformations that consisted of a crystal structure and molecular dynamics simulation cluster centroids were used to form AR and CDK2 ensembles. Multiple available crystal structures were used to form PPAR- ensembles. For each target, we show that the three methods perform similarly to one another on both the training and test sets. © 2016 American Chemical Society.