Combination Computing of Support Vector Machine, Support Vector Regression and Molecular Docking for Potential Cytochrome P450 1A2 Inhibitors

Abstract

The computational approaches of support vector machine (SVM), support vector regression (SVR) and molecular docking were widely utilized for the computation of active compounds. In this work, to improve the accuracy and reliability of prediction, the strategy of combining the above three computational approaches was applied to predict potential cytochrome P450 1A2 (CYP1A2) inhibitors. The accuracy of the optimal SVM qualitative model was 99.432%, 97.727%, and 91.667% for training set, internal test set and external test set, respectively, showing this model had high discrimination ability. The R 2 and mean square error for the optimal SVR quantitative model were 0.763, 0.013 for training set, and 0.753, 0.056 for test set respectively, indicating that this SVR model has high predictive ability for the biological activities of compounds. According to the results of the SVM and SVR models, some types of descriptors were identified to be essential to bioactivity prediction of compounds, including the connectivity indices, constitutional descriptors and functional group counts. Moreover, molecular docking studies were used to reveal the binding poses and binding affinity of potential inhibitors interacting with CYP1A2. Wherein, the amino acids of THR124 and ASP320 could form key hydrogen bond interactions with active compounds. And the amino acids of ALA317 and GLY316 could form strong hydrophobic bond interactions with active compounds. The models obtained above were applied to discover potential CYP1A2 inhibitors from natural products, which could predict the CYPs-mediated drug-drug interactions and provide useful guidance and reference for rational drug combination therapy. A set of 20 potential CYP1A2 inhibitors were obtained. Part of the results was consistent with references, which further indicates the accuracy of these models and the reliability of this combinatorial computation strategy.

An In Vitro Study for Evaluating Permeability and Metabolism of Kurarinone

“…The results exhibited that kurarinone interacted with CYP1A2 (Asn312 and Asp320), CYP2C9 (Asp301), and CYP2D6 (Gln244 and Ser304) through formation of H-bonds. Asn312 and Asp320 are significant residues in the substrate and inhibitor recognition regions of CYP1A2 [ 39 , 40 ]. Gln244 and Ser304 in CYP2D6 are active site residues involved in hydrogen bond formation with substrates [ 41 ].…”