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首页> 外文期刊>Journal of chemical information and modeling >Quantum Mechanical Pairwise Decomposition Analysis of Protein Kinase B Inhibitors: Validating a New Tool for Guiding Drug Design
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Quantum Mechanical Pairwise Decomposition Analysis of Protein Kinase B Inhibitors: Validating a New Tool for Guiding Drug Design

机译:蛋白激酶B抑制剂的量子力学成对分解分析:验证指导药物设计的新工具。

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摘要

Quantum mechanical semiempirical comparative binding energy analysis calculations have been carried out for a series of protein kinase B (PKB) inhibitors derived from fragment- and structure-based drug design. These protein ligand complexes were selected because they represent a consistent set of experimental data that includes both crystal structures and affinities. Seven scoring functions were evaluated based on both the PM3 and the AM1 Hamiltonians. The optimal models obtained by partial least-squares analysis of the aligned poses are predictive as measured by a number of standard statistical criteria and by validation with an external data set. An algorithm has been developed that provides residue-based contributions to the overall binding affinity. These residue-based binding contributions can be plotted in heat maps so as to highlight the most important residues for ligand binding. In the case of these PKB inhibitors, the maps show that Met166, Thr97, Gly43, Glu114, Ala116, and Va150, among other residues, play an important role in determining binding affinity. The interaction energy map makes it easy to identify the residues that have the largest absolute effect on ligand binding. The structure activity relationship (SAR) map highlights residues that are most critical to discriminating between more and less potent ligands. Taken together the interaction energy and the SAR maps provide useful insights into drug design that would he difficult to garner in any other way.
机译:已经对衍生自基于片段和结构的药物设计的一系列蛋白激酶B(PKB)抑制剂进行了量子力学半经验比较结合能分析计算。选择这些蛋白质配体复合物是因为它们代表了包括晶体结构和亲和力在内的一组一致的实验数据。基于PM3和AM1哈密顿量,评估了七个得分函数。通过对齐的姿势的局部最小二乘分析获得的最佳模型具有预测性,如通过许多标准统计标准和通过外部数据集进行的验证所测得的那样。已经开发了一种算法,该算法为整体结合亲和力提供了基于残基的贡献。这些基于残基的结合贡献可以绘制在热图中,以突出显示配体结合的最重要残基。在这些PKB抑制剂的情况下,图谱显示Met166,Thr97,Gly43,Glu114,Ala116和Va150以及其他残基在确定结合亲和力中起重要作用。相互作用能图使您很容易识别对配体结合具有最大绝对影响的残基。结构活性关系(SAR)图突出显示了对区分更多或更少有效配体最关键的残基。综合起来,相互作用能和SAR映射为药物设计提供了有用的见识,而他将很难以任何其他方式获得该见解。

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