Computational insight into crucial binding features for metabolic specificity of cytochrome P450 17A1. (2019)

• Record Type: Journal Article
• Authors: Ai, Chun-Zhi; Man, Hui-Zi; Saeed, Yasmeen; Chen, Du-Chu; Wang, Li-Hua; Jiang, Yi-Zhou
• Is Part Of: Informatics in medicine unlocked. Volume 15(2019)
• Publication Date: 2019
• Subjects: CYP17A1 -- Metabolic specificity -- Inhibitors and substrates -- 3D-QSAR -- Docking; Medical informatics -- Periodicals; 610.285
• DOI: 10.1016/j.imu.2019.100172
• Languages: English
• ISSNs: 2352-9148

Abstract

Abstract: In present study we aimed to explore the possible structural feature of Cytochrome P450 (CYP) 17A1 that contributes to the metabolic specificity. The predicable 3D-QSAR (Quantitative Structure-Activity Relationships) models were first developed with CoMFA (Comparative Molecular Field Analysis) and CoMSIA (Comparative Molecular Similarity Indices Analysis) methods based on a training set of 76 non-steroid inhibitors, then verified by a test set of 20 inhibitors. Our data demonstrates a cross-validation correlation coefficient q 2 s of 0.534 and 0.545, as well as a non-cross-validation correlation coefficient r 2 s of 0.904 and 0.889, respectively. The contours were generated to indicate the specific inhibitor feature. Further, molecular docking was used to probe the interacting feature between CYP17A1 and its non-steroid inhibitors or its natural substrates. We found the crucial binding features for the non-steroid inhibitor selection can be described as the suitable molecular length and the ability to fulfill the active pocket of CYP17A1, the hydrophobic parent body and the H-bond formed with special residues. Whereas, the distances between reaction site and the oxidative or the per-oxidative center played an important role in the substrate metabolism of 17α-hydroxylase and 17, 20-lyase. This study helps to design and screen potential candidates for therapy of prostate cancer and other androgen-dependent diseases.