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Knowledge-Based Methods To Train and Optimize Virtual Screening Ensembles.

ABSTRACT: 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(2(N)). A recursive approximation to the optimal solution scales as O(N(2)), 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.

SUBMITTER: Swift RV

PROVIDER: S-EPMC4881196 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature

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Knowledge-Based Methods To Train and Optimize Virtual Screening Ensembles.

Swift Robert V RV Jusoh Siti A SA Offutt Tavina L TL Li Eric S ES Amaro Rommie E RE

Journal of chemical information and modeling 20160503 5

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 select ...[more]

PMID: 27097522

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Project description:We present Ligity, a hybrid ligand-structure-based, non-superpositional method for virtual screening of large databases of small molecules. Ligity uses the relative spatial distribution of pharmacophoric interaction points (PIPs) derived from the conformations of small molecules. These are compared with the PIPs derived from key interaction features found in protein-ligand complexes and are used to prioritize likely binders. We investigated the effect of generating PIPs using the single lowest energy conformer versus an ensemble of conformers for each screened ligand, using different bin sizes for the distance between two features, utilizing triangular sets of pharmacophoric features (3-PIPs) versus chiral tetrahedral sets (4-PIPs), fusing data for targets with multiple protein-ligand complex structures, and applying different similarity measures. Ligity was benchmarked using the Directory of Useful Decoys-Enhanced (DUD-E). Optimal results were obtained using the tetrahedral PIPs derived from an ensemble of bound ligand conformers and a bin size of 1.5 Å, which are used as the default settings for Ligity. The high-throughput screening mode of Ligity, using only the lowest-energy conformer of each ligand, was used for benchmarking against the whole of the DUD-E, and a more resource-intensive, "information-rich" mode of Ligity, using a conformational ensemble of each ligand, were used for a representative subset of 10 targets. Against the full DUD-E database, mean area under the receiver operating characteristic curve (AUC) values ranged from 0.44 to 0.99, while for the representative subset they ranged from 0.61 to 0.86. Data fusion further improved Ligity's performance, with mean AUC values ranging from 0.64 to 0.95. Ligity is very efficient compared to a protein-ligand docking method such as AutoDock Vina: if the time taken for the precalculation of Ligity descriptors is included in the comparason, then Ligity is about 20 times faster than docking. A direct comparison of the virtual screening steps shows Ligity to be over 5000 times faster. Ligity highly ranks the lowest-energy conformers of DUD-E actives, in a statistically significant manner, behavior that is not observed for DUD-E decoys. Thus, our results suggest that active compounds tend to bind in relatively low-energy conformations compared to decoys. This may be because actives-and thus their lowest-energy conformations-have been optimized for conformational complementarity with their cognate binding sites.

Dataset Information

Knowledge-Based Methods To Train and Optimize Virtual Screening Ensembles.

Publications

Knowledge-Based Methods To Train and Optimize Virtual Screening Ensembles.

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