Target-Specific Support Vector Machine Scoring in Structure-Based Virtual Screening: Computational Validation In Vitro Testing in Kinases and Effects on Lung Cancer Cell Proliferation

摘要

We assess the performance of our previously reported structure-based support vector machine target-specific scoring function across 41 targets, 40 among them from the Directory of Useful Decoys (DUD). The area under the curve of receiver characteristic plots (ROC-AUC) revealed that scoring with SVMSP resulted in consistently better enrichment over all targets families and outperforming Glide and other scoring functions, most notably among kinases. In addition, SVM-SP performance showed little variation among protein classes, exhibited excellent performance in a test case using a homology model, and in some cases showed high enrichment even with few structures used to train a model. We put SVM-SP to the test by virtual screening 1,125 compounds against two kinases, EGFR and CaMKII. Among the top 25 EGFR compounds, three compounds (>1–>3) inhibited kinase activity in vitro with IC50 of 58, 2, and 10 μM. In cell culture, compounds >1–>3 inhibited non-small cell lung carcinoma (H1299) cancer cell proliferation with similar IC50 values for compound >3. For CaMKII, one compound inhibited kinase activity in a dose-dependent manner among 20 tested with an IC50 of 48 μM. These results are encouraging given that our in-house library consists of compounds that emerged from virtual screening of other targets with pockets that are different from typical ATP binding sites found in kinases. In light of the importance of kinases in chemical biology, these findings could have implications in future efforts to identify chemical probes of kinases within the human kinome.