Docking and Scoring with Target-Specific Pose Classifier Succeeds in Native-Like Pose Identification But Not Binding Affinity Prediction in the CSAR 2014 Benchmark Exercise

作者：Politi Regina; Convertino Marino; Popov Konstantin; Dokholyan Nikolay V; Tropsha Alexander

来源：Journal of Chemical Information and Modeling, 2016, 56(6): 1032-1041.

DOI：10.1021/acs.jcim.5b00751

The CSAR 2014 exercise provided an important benchmark for testing current approaches for pose identification and ligand ranking using three X-ray characterized proteins: Factor Xa (FXa), Spleen Tyrosine Kinase (SYK), and tRNA Methyltransferase (TRMD). In Phase 1 of the exercise, we employed Glide and MedusaDock docking software, both individually and in combination, with the special target-specific pose classifier trained to discriminate native-like from decoy poses. All approaches succeeded in the accurate detection of native and native-like poses. We then used Glide SP and MedusaScore scoring functions individually and in combination with the pose-scoring approach to predict relative binding affinities of the congeneric series of ligands in Phase 2 of the exercise. Similar to other participants in the CSAR 2014 exercise, we found that our models showed modest prediction accuracy. Quantitative structure activity relationship (QSAR) models developed for the FXa ligands using available bioactivity data from ChEMBL showed relatively low prediction accuracy for the CSAR 2014 ligands of the same target. Interestingly, QSAR models built with CSAR data only yielded Spearman correlation coefficients as high as rho = 0.69 for FXa and rho = 0.79 for SYK based on 5-fold cross-validation. Virtual screening of the DUD library using the FXa structure was successful in discriminating between active compounds and decoys in spite of poor ranking accuracy of the underlying scoring functions. Our results suggest that two of the three common tasks associated with molecular docking, i.e., native-like pose identification and virtual screening, but not binding affinity prediction, could be accomplished successfully for the CSAR 2014 challenge data set.

• 出版日期2016-6