Computational insights into the active site of human breast cancer resistance protein (BCRP/ABCG2): a similarity search approach

Abstract

Human breast cancer resistance protein (BCRP) belongs to the G subfamily of ATP-binding cassette transporter. Over-expression of BCRP in cancer cells limits the efficacy of many chemotherapeutic agents, resulting in multiple drug resistance. A clear understanding of inhibitor-binding mode will be useful in the design of BCRP-selective inhibitor. To gain structural insights into the active site of BCRP, three-dimensional quantitative structure–activity relationship (3D-QSAR) models were developed for two different chemical series (flavonoids and chalcones derivatives) of BCRP inhibitors. Stable and statistically reliable predictive CoMFA and CoMSIA models have shown significant $r_{\rm cv}^{2} > 0.7,\;r_{\rm ncv}^{2} > 0.9,\;r_{\rm bs}^{2} > 0.9,\;r_{\rm pred}^{2} > 0.6$ and a small standard deviation <0.2, indicating a better statistical relationship between the activity and descriptors. 3D contour maps generated from these models were analyzed individually, which provided the regions in space where interactive fields may influence the inhibitory activity of BCRP inhibitors. The similarity search between site points, generated from contour maps, and binding pockets identified using different pocket search tools, provided essential clues for characterization of the binding site. Molecular docking analysis reveals that Asp643, Asp650, and Val651 of the nucleotide-binding domain play an important role in binding of flavonoids and chalcones. The structural insights obtained from 3D-QSAR and molecular docking studies will serve as a guideline for designing and predicting novel BCRP inhibitors.