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Docking-undocking combination applied to the D3R Grand Challenge 2015

机译:对接与对接组合应用于D3R Grand Challenge 2015

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摘要

Novel methods for drug discovery are constantly under development and independent exercises to test and validate them for different goals are extremely useful. The drug discovery data resource (D3R) Grand Challenge 2015 offers an excellent opportunity as an external assessment and validation experiment for Computer-Aided Drug Discovery methods. The challenge comprises two protein targets and prediction tests: binding mode and ligand ranking. We have faced both of them with the same strategy: pharmacophore-guided docking followed by dynamic undocking (a new method tested experimentally here) and, where possible, critical assessment of the results based on pre-existing information. In spite of using methods that are qualitative in nature, our results for binding mode and ligand ranking were amongst the best on Hsp90. Results for MAP4K4 were less positive and we track the different performance across systems to the level of previous knowledge about accessible conformational states. We conclude that docking is quite effective if supplemented by dynamic undocking and empirical information (e.g. binding hot spots, productive protein conformations). This setup is well suited for virtual screening, a frequent application that was not explicitly tested in this edition of the D3R Grand Challenge 2015. Protein flexibility remains as the main cause for hard failures.
机译:不断开发新的药物发现方法,针对不同目标进行测试和验证的独立练习非常有用。 2015年药物发现数据资源(D3R)大挑战赛提供了极好的机会,可以作为计算机辅助药物发现方法的外部评估和验证实验。挑战包括两个蛋白质目标和预测测试:结合模式和配体排名。我们以相同的策略面对这两个问题:以药效团引导的对接,然后进行动态对接(此处通过实验对这种新方法进行了实验),并在可能的情况下,根据现有信息对结果进行了严格的评估。尽管使用的方法本质上是定性的,但结合模式和配体排名的结果在Hsp90上仍是最好的。 MAP4K4的结果不太乐观,我们追踪了系统之间不同的性能,达到了有关可访问构象状态的先前知识水平。我们得出的结论是,如果通过动态取消对接和经验信息(例如结合热点,生产性蛋白质构象)进行补充,对接将非常有效。此设置非常适合虚拟筛选,这是在本版D3R Grand Challenge 2015中未经过明确测试的常用应用程序。蛋白质的灵活性仍然是造成硬件失败的主要原因。

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