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基于可转移线性相互作用能模型的高通量对接和评分发现激酶抑制剂。

Discovery of kinase inhibitors by high-throughput docking and scoring based on a transferable linear interaction energy model.

作者信息

Kolb Peter, Huang Danzhi, Dey Fabian, Caflisch Amedeo

机构信息

Department of Biochemistry, University of Zurich, Zurich, Switzerland.

出版信息

J Med Chem. 2008 Mar 13;51(5):1179-88. doi: 10.1021/jm070654j. Epub 2008 Feb 14.

Abstract

The linear interaction energy method with continuum electrostatics (LIECE) is evaluated in depth on five kinases. The two multiplicative coefficients for the van der Waals energy and electrostatic free energy are shown to be transferable among different kinases. Moreover, good enrichment factors are obtained for a library of 40375 diverse compounds seeded with 73 known inhibitors of CDK2. Therefore, a general two-parameter LIECE model for kinases is derived by combining large data sets of inhibitors of CDK2, Lck, and p38. This two-parameter model is cross-validated on two kinases not used for fitting; it shows an average error of about 1.5 kcal/mol for the prediction of absolute binding affinity of 37 and 128 known inhibitors of EphB4 and EGFR, respectively. High-throughput docking and ranking by two-parameter LIECE models are shown to be able to identify novel low-micromolar EphB4 and CDK2 inhibitors of low-molecular weight (< or =355 g/mol).

摘要

采用连续介质静电模型的线性相互作用能方法(LIECE)在5种激酶上进行了深入评估。范德华能和静电自由能的两个乘法系数在不同激酶之间具有可转移性。此外,对于一个由40375种不同化合物组成且含有73种已知CDK2抑制剂的文库,获得了良好的富集因子。因此,通过合并CDK2、Lck和p38抑制剂的大数据集,推导出了一种通用的激酶双参数LIECE模型。该双参数模型在两个未用于拟合的激酶上进行了交叉验证;对于分别为37种和128种已知的EphB4和EGFR抑制剂的绝对结合亲和力预测,其平均误差约为1.5千卡/摩尔。通过双参数LIECE模型进行的高通量对接和排序能够识别新型的低分子量(≤355克/摩尔)的低微摩尔浓度EphB4和CDK2抑制剂。

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