Automatic recognition of ligands in electron density by machine learning
Automatic recognition of ligands in electron density by machine learning
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DOI：
10.1093/bioinformatics/bty626
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发表时间：
2019-02-01
期刊：
影响因子：
5.8
通讯作者：
Minor, Wladek
中科院分区：
文献类型：
--
作者：
Kowiel, Marcin;Brzezinski, Dariusz;Minor, Wladek
动机：正确识别蛋白质复合物晶体结构中的配体是结构导向药物设计的基石。然而，认知偏差有时会误导研究人员在没有电子密度图的坚实支持的情况下模拟虚构的化合物。配体识别可以通过自动方法来辅助，但现有的方法是基于耗时的迭代fitting.Results：在这里，我们报告了一种新的机器学习算法，称为CheckMyBlob，从实验电子密度图识别配体。在基准测试的组合高达219 931配体结合位点，其中包含200个最流行的配体在蛋白质数据库中发现，CheckMyBlob明显优于现有的自动配体识别方法，在某些情况下，识别率翻了一番，而需要显着更少的时间。我们的工作表明，机器学习可以提高结构建模的自动化程度，显著加快大分子-配体复合物的药物筛选过程。
Motivation: The correct identification of ligands in crystal structures of protein complexes is the cornerstone of structure-guided drug design. However, cognitive bias can sometimes mislead investigators into modeling fictitious compounds without solid support from the electron density maps. Ligand identification can be aided by automatic methods, but existing approaches are based on time-consuming iterative fitting.Results: Here we report a new machine learning algorithm called CheckMyBlob that identifies ligands from experimental electron density maps. In benchmark tests on portfolios of up to 219 931 ligand binding sites containing the 200 most popular ligands found in the Protein Data Bank, CheckMyBlob markedly outperforms the existing automatic methods for ligand identification, in some cases doubling the recognition rates, while requiring significantly less time. Our work shows that machine learning can improve the automation of structure modeling and significantly accelerate the drug screening process of macromolecule-ligand complexes.