SVM Classification and CoMSIA Modeling of UGT1A6 Interacting Molecules

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• 作者：Leo Ghemtio ; Anne Soikkeli ; Marjo Yliperttula ; Jouni Hirvonen ; Moshe Finel ; Henri Xhaard
• 刊名：Journal of Chemical Information and Modeling
• 出版年：2014
• 出版时间：April 28, 2014
• 年：2014
• 卷：54
• 期：4
• 页码：1011-1026
• 全文大小：779K
• 年卷期：v.54,no.4(April 28, 2014)
• ISSN：1549-960X

文摘

The human UDP-glucuronosyltransferase 1A6 (UGT1A6) plays important roles in elimination of many xenobiotics, including drugs. We have experimentally assessed inhibitory properties of 46 compounds toward UGT1A6 catalyzing the glucuronidation of 1-naphthol and built models for predicting compounds interactions with the enzyme. The tested compounds were divided into a training set (n = 31; evaluated by 10-fold cross-validation) and an external test set (n = 15), both of which yielded similar accuracies (80鈥?1%) and Matthews correlation coefficients (0.61鈥?.63) when classified using support vector machines. Comparative molecular similarity index analysis (CoMSIA) modeling was conducted for nine methods of compound alignment. The most predictive CoMSIA model was analyzed in the light of a homology modeled UGT1A6 structure, with leave-one-out cross-validation, yielding a q² of 0.62 and r² of 0.91 on the training set and a r²_pred of 0.82 on the test set. The CoMSIA contour plots highlighted the importance of H-bond donors and electrostatic field interactions, accounting for 28% and 25% contribution of the model, respectively.