Autogrid-based clustering of kinases: selection of representative conformations for docking purposes
Multitarget agents have been extensively explored for solving limited efficacies, poor safety, and resistant profiles of an individual target. Theoretical approaches for searching and designing multitarget agents are critically useful. Here, the performance of molecular docking to search dual-target inhibitors for four kinase pairs (CDK2-GSK3B, EGFR-Src, Lck-Src, and Lck-VEGFR2) was assessed. First, the representative structures for each kinase target were chosen by structural clustering of available crystal structures. Next, the performance of molecular docking to distinguish inhibitors from noninhibitors for each individual kinase target was evaluated. The results show that molecular docking-based virtual screening illustrates good capability to find known inhibitors for individual targets, but the prediction accuracy is structurally dependent. Finally, the performance of molecular docking to identify the dual-target kinase inhibitors for four kinase pairs was evaluated. The analyses show that molecular docking successfully filters out most noninhibitors and achieves promising performance for identifying dual-kinase inhibitors for CDK2-GSK3B and Lck-VEGFR2. But a high false-positive rate leads to low enrichment of true dual-target inhibitors in the final list. This study suggests that molecular docking serves as a useful tool in searching inhibitors against dual or even multiple kinase targets, but integration with other virtual screening tools is necessary for achieving better predictions.