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计算机辅助生化分析——预测大型酶家族的底物特异性

Biochemical profiling in silico--predicting substrate specificities of large enzyme families.

作者信息

Tyagi Sadhna, Pleiss Juergen

机构信息

Institute of Technical Biochemistry, University of Stuttgart, Germany.

出版信息

J Biotechnol. 2006 Jun 25;124(1):108-16. doi: 10.1016/j.jbiotec.2006.01.027. Epub 2006 Mar 7.

DOI:10.1016/j.jbiotec.2006.01.027
PMID:16519956
Abstract

A general high-throughput method for in silico biochemical profiling of enzyme families has been developed based on covalent docking of potential substrates into the binding sites of target enzymes. The method has been tested by systematically docking transition state--analogous intermediates of 12 substrates into the binding sites of 20 alpha/beta hydrolases from 15 homologous families. To evaluate the effect of side chain orientations to the docking results, 137 crystal structures were included in the analysis. A good substrate must fulfil two criteria: it must bind in a productive geometry with four hydrogen bonds between the substrate and the catalytic histidine and the oxyanion hole, and a high affinity of the enzyme-substrate complex as predicted by a high docking score. The modelling results in general reproduce experimental data on substrate specificity and stereoselectivity: the differences in substrate specificity of cholinesterases toward acetyl- and butyrylcholine, the changes of activity of lipases and esterases upon the size of the acid moieties, activity of lipases and esterases toward tertiary alcohols, and the stereopreference of lipases and esterases toward chiral secondary alcohols. Rigidity of the docking procedure was the major reason for false positive and false negative predictions, as the geometry of the complex and docking score may sensitively depend on the orientation of individual side chains. Therefore, appropriate structures have to be identified. In silico biochemical profiling provides a time efficient and cost saving protocol for virtual screening to identify the potential substrates of the members of large enzyme family from a library of molecules.

摘要

基于将潜在底物共价对接至目标酶结合位点,已开发出一种用于酶家族计算机生化分析的通用高通量方法。该方法通过将12种底物的过渡态类似中间体系统地对接至来自15个同源家族的20种α/β水解酶的结合位点进行了测试。为评估侧链取向对对接结果的影响,分析中纳入了137个晶体结构。一种良好的底物必须满足两个标准:它必须以有效的几何结构结合,在底物与催化组氨酸和氧负离子洞之间形成四个氢键,并且酶-底物复合物具有高亲和力,这由高对接分数预测得出。建模结果总体上重现了关于底物特异性和立体选择性的实验数据:胆碱酯酶对乙酰胆碱和丁酰胆碱的底物特异性差异、脂肪酶和酯酶活性随酸部分大小的变化、脂肪酶和酯酶对叔醇的活性以及脂肪酶和酯酶对手性仲醇的立体偏好。对接过程的刚性是出现假阳性和假阴性预测的主要原因,因为复合物的几何结构和对接分数可能敏感地取决于各个侧链的取向。因此,必须识别合适的结构。计算机生化分析为虚拟筛选提供了一种省时且经济的方案,以便从分子库中识别大型酶家族成员的潜在底物。

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