Department of Pharmacological and Pharmaceutical Sciences, College of Pharmacy, University of Houston, Houston, Texas 77030, USA.
J Pharm Sci. 2012 Sep;101(9):3531-9. doi: 10.1002/jps.23100. Epub 2012 Mar 2.
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.
UDP-葡糖醛酸基转移酶 1A10（UGT1A10）催化广泛的化学物质的葡醛酸化，包括许多药物。在这里，我们报告了第一个用于定量计算 UGT1A10 底物选择性和结合亲和力（以 K(m)表示）的计算模型。该模型的训练集包含 32 种结构不同的化合物，这些化合物是 UGT1A10 的已知底物。该模型通过应用标准的 VolSurf 方法来构建，该方法涉及计算 VolSurf 描述符和偏最小二乘（PLS）分析。该模型具有两个分量，在拟合和内部预测方面均具有统计学意义（r(2) = 0.827，q(2) = 0.774）。该模型还通过 11 种外部化合物的测试集进行了验证。所有测试底物的活性值都在 1 个对数单位内预测。此外，该模型揭示了影响酶-底物结合的化学特征的叠加。这些特征包括大小和形状、容量因素、亲水区、疏水区和极化率。总之，成功地利用 VolSurf 方法建立了 UGT1A10 的预测模型。该模型应该是用于高通量预测 UGT1A10 代谢的有效工具。
