Effects of protein flexibility and active site water molecules on the prediction of sites of metabolism for cytochrome P450 2C19 substrates. Issue 3 (13th January 2016)

• Record Type: Journal Article
• Authors: Li, Junhao; Cai, Jinya; Su, Haixia; Du, Hanwen; Zhang, Juan; Ding, Shihui; Liu, Guixia; Tang, Yun; Li, Weihua
• Is Part Of: Molecular bioSystems. Volume 12:Issue 3(2016:Mar.)
• Page Start: 868
• Page End: 878
• Publication Date: 2016-01-13
• Subjects: Molecular biology -- Periodicals; Biochemistry -- Periodicals; 571.7405
• DOI: 10.1039/c5mb00784d
• Languages: English
• ISSNs: 1742-206X

Abstract

Abstract : Structure-based prediction of sites of metabolism (SOMs) mediated by cytochrome P450s (CYPs) is of great interest in drug discovery and development. Abstract : Structure-based prediction of sites of metabolism (SOMs) mediated by cytochrome P450s (CYPs) is of great interest in drug discovery and development. However, protein flexibility and active site water molecules remain a challenge for accurate SOM prediction. CYP2C19 is one of the major drug-metabolizing enzymes and has attracted considerable attention because of its polymorphism and capability of metabolizing ∼7% clinically used drugs. In this study, we systematically evaluated the effects of protein flexibility and active site water molecules on SOM prediction for CYP2C19 substrates. Multiple conformational sampling techniques including GOLD flexible residues sampling, molecular dynamics (MD) and tCONCOORD side-chain sampling were adopted for assessing the influence of protein flexibility on SOM prediction. The prediction accuracy could be significantly improved when protein flexibility was considered using the tCONCOORD sampling method, which indicated that the side-chain conformation was important for accurate prediction. However, the inclusion of the crystallographic or MD-derived water molecule(s) does not necessarily improve the prediction accuracy. Finally, a combination of docking results with SMARTCyp was found to be able to increase the SOM prediction accuracy.