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用于底物识别的分子对接:短链脱氢酶/还原酶

Molecular docking for substrate identification: the short-chain dehydrogenases/reductases.

作者信息

Favia Angelo D, Nobeli Irene, Glaser Fabian, Thornton Janet M

机构信息

European Molecular Biology Laboratory-European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.

出版信息

J Mol Biol. 2008 Jan 18;375(3):855-74. doi: 10.1016/j.jmb.2007.10.065. Epub 2007 Nov 1.

Abstract

Protein ligand docking has recently been investigated as a tool for protein function identification, with some success in identifying both known and unknown substrates of proteins. However, identifying a protein's substrate when cross-docking a large number of enzymes and their cognate ligands remains a challenge. To explore a more limited yet practically important and timely problem in more detail, we have used docking for identifying the substrates of a single protein family with remarkable substrate diversity, the short-chain dehydrogenases/reductases. We examine different protocols for identifying candidate substrates for 27 short-chain dehydrogenase/reductase proteins of known catalytic function. We present the results of docking >900 metabolites from the human metabolome to each of these proteins together with their known cognate substrates and products, and we investigate the ability of docking to (a) reproduce a viable binding mode for the substrate and (b) to rank the substrate highly amongst the dataset of other metabolites. In addition, we examine whether our docking results provide information about the nature of the substrate, based on the best-scoring metabolites in the dataset. We compare two different docking methods and two alternative scoring functions for one of the docking methods, and we attempt to rationalise both successes and failures. Finally, we introduce a new protocol, whereby we dock only a set of representative structures (medoids) to each of the proteins, in the hope of characterising each binding site in terms of its ligand preferences, with a reduced computational cost. We compare the results from this protocol with our original docking experiments, and we find that although the rank of the representatives correlates well with the mean rank of the clusters to which they belong, a simple structure-based clustering is too naive for the purpose of substrate identification. Many clusters comprise ligands with widely varying affinities for the same protein; hence important candidates can be missed if a single representative is used.

摘要

蛋白质配体对接最近被作为一种蛋白质功能识别工具进行研究,在识别蛋白质已知和未知底物方面取得了一些成功。然而,当对大量酶及其同源配体进行交叉对接时,识别蛋白质的底物仍然是一项挑战。为了更详细地探讨一个更有限但实际重要且及时的问题,我们使用对接来识别具有显著底物多样性的单个蛋白质家族——短链脱氢酶/还原酶的底物。我们研究了用于识别27种已知催化功能的短链脱氢酶/还原酶蛋白质候选底物的不同方案。我们展示了将来自人类代谢组的900多种代谢物与这些蛋白质各自的已知同源底物和产物进行对接的结果,并研究对接在以下两方面的能力:(a)为底物重现可行的结合模式;(b)在其他代谢物数据集中将底物排在高位。此外,我们根据数据集中得分最高的代谢物,研究对接结果是否能提供有关底物性质的信息。我们比较了两种不同的对接方法以及其中一种对接方法的两种替代评分函数,并试图对成功和失败的情况做出合理的解释。最后,我们引入了一种新方案,即仅将一组代表性结构(质心)与每种蛋白质进行对接,以期以降低的计算成本,根据其配体偏好来表征每个结合位点。我们将该方案的结果与原始对接实验的结果进行比较,发现尽管代表性结构的排名与它们所属簇的平均排名相关性良好,但基于简单结构的聚类对于底物识别而言过于简单。许多簇包含对同一蛋白质亲和力差异很大的配体;因此,如果使用单个代表性结构,可能会错过重要的候选物。

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