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首页> 外文期刊>Computational Biology and Bioinformatics, IEEE/ACM Transactions on >A Novel Scoring Based Distributed Protein Docking Application to Improve Enrichment
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A Novel Scoring Based Distributed Protein Docking Application to Improve Enrichment

机译：一种基于评分的新型分布式蛋白质对接应用，可提高浓缩度

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摘要

Molecular docking is a computational technique which predicts the binding energy and the preferred binding mode of a ligand to a protein target. Virtual screening is a tool which uses docking to investigate large chemical libraries to identify ligands that bind favorably to a protein target. We have developed a novel scoring based distributed protein docking application to improve enrichment in virtual screening. The application addresses the issue of time and cost of screening in contrast to conventional systematic parallel virtual screening methods in two ways. Firstly, it automates the process of creating and launching multiple independent dockings on a high performance computing cluster. Secondly, it uses a Nȧi̇ve Bayes scoring function to calculate binding energy of un-docked ligands to identify and preferentially dock (Autodock predicted) better binders. The application was tested on four proteins using a library of 10,573 ligands. In all the experiments, (i). 200 of the 1,000 best binders are identified after docking only percent of the chemical library, (ii). 9 or 10 best-binders are identified after docking only percent of the chemical library, and (iii). no significant enrichment is observed after docking percent of the chemical library. The results show significant increase in enrichment of potential drug leads in early rounds of virtual screening.
机译：分子对接是一种计算技术，可预测配体与蛋白质靶标的结合能和优选结合方式。虚拟筛选是使用对接研究大型化学文库以鉴定与蛋白质靶标良好结合的配体的工具。我们已经开发了一种新颖的基于评分的分布式蛋白质对接应用程序，以改善虚拟筛选中的富集。与传统的系统并行虚拟筛选方法相比，该应用程序以两种方式解决了筛选的时间和成本问题。首先，它可以自动化在高性能计算集群上创建和启动多个独立对接的过程。其次，它使用简单贝叶斯（Bayes）评分功能计算未对接配体的结合能，以识别并优先对接（Autodock预测）更好的结合剂。使用10,573个配体库对四种蛋白质测试了该应用程序。在所有实验中，（i）。仅对接了化学文库的一部分后，就确定了1,000种最佳粘合剂中的200种（ii）。仅对接化学文库的一部分后，即可确定9或10个最佳结合，以及（iii）。停靠一定百分比的化学文库后，未观察到明显的富集。结果显示，在虚拟筛选的早期轮次中，潜在药物线索的富集度显着增加。

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