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In silico modeling of UDP-glucuronosyltransferase 1A10 substrates using the VolSurf approach.

UDP-glucuronosyltransferase 1A10 (UGT1A10) catalyzes glucuronidation of a wide range of chemicals including many drugs. Here, we report the first in silico model quantifying the substrate selectivity and binding affinity (as K(m)) for UGT1A10. The training set for model construction comprises 32 structurally diverse compounds, which are known substrates for UGT1A10. The model was derived by applying the standard VolSurf method involving calculation of VolSurf descriptors and partial least square (PLS) analyses. The yielded PLS model with two components shows statistical significance in both fitting and internal predicting (r(2) = 0.827, q(2) = 0.774). The model predictability was further validated against a test set of 11 external compounds. The activity values for all test substrates were predicted within 1 log unit. Moreover, the model reveals an overlay of chemical features influencing the enzyme-substrate binding. Those include the size and shape, capacity factors, hydrophilic regions, hydrophobic regions, and polarizability. In conclusion, the VolSurf approach is successfully utilized to establish a predictive model for UGT1A10. The derived model should be an efficient tool for high-throughput prediction of UGT1A10 metabolism.