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利用大分子电子密度提高虚拟筛选中活性化合物的富集度。

Using macromolecular electron densities to improve the enrichment of active compounds in virtual screening.

作者信息

Ma Wenzhi, Zhang Wei, Le Yuan, Shi Xiaoxuan, Xu Qingbo, Xiao Yang, Dou Yueying, Wang Xiaoman, Zhou Wenbiao, Peng Wei, Zhang Hongbo, Huang Bo

机构信息

Beijing StoneWise Technology Co Ltd., Haidian Street #15, Haidian District, 100080, Beijing, China.

State Key Laboratory of Respiratory Disease, First Affiliated Hospital of Guangzhou Medical University, 510182, Guangzhou, China.

出版信息

Commun Chem. 2023 Aug 22;6(1):173. doi: 10.1038/s42004-023-00984-5.

Abstract

The quest for effective virtual screening algorithms is hindered by the scarcity of training data, calling for innovative approaches. This study presents the use of experimental electron density (ED) data for improving active compound enrichment in virtual screening, supported by ED's ability to reflect the time-averaged behavior of ligands and solvents in the binding pocket. Experimental ED-based grid matching score (ExptGMS) was developed to score compounds by measuring the degree of matching between their binding conformations and a series of multi-resolution experimental ED grids. The efficiency of ExptGMS was validated using both in silico tests with the Directory of Useful Decoys-Enhanced dataset and wet-lab tests on Covid-19 3CLpro-inhibitors. ExptGMS improved the active compound enrichment in top-ranked molecules by approximately 20%. Furthermore, ExptGMS identified four active inhibitors of 3CLpro, with the most effective showing an IC value of 1.9 µM. We also developed an online database containing experimental ED grids for over 17,000 proteins to facilitate the use of ExptGMS for academic users.

摘要

有效虚拟筛选算法的探索因训练数据的稀缺而受阻,这就需要创新方法。本研究提出利用实验电子密度(ED)数据来改善虚拟筛选中活性化合物的富集,这得益于ED能够反映配体和溶剂在结合口袋中的时间平均行为。基于实验ED的网格匹配分数(ExptGMS)得以开发,通过测量化合物结合构象与一系列多分辨率实验ED网格之间的匹配程度来对化合物进行评分。ExptGMS的效率通过使用具有增强有用诱饵目录数据集的计算机模拟测试以及针对新冠病毒3CL蛋白酶抑制剂的湿实验室测试进行了验证。ExptGMS使排名靠前的分子中活性化合物的富集提高了约20%。此外,ExptGMS鉴定出了四种3CL蛋白酶的活性抑制剂,其中最有效的抑制剂的IC值为1.9 μM。我们还开发了一个在线数据库,其中包含超过17,000种蛋白质的实验ED网格,以方便学术用户使用ExptGMS。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b2d/10444862/38718ca00416/42004_2023_984_Fig1_HTML.jpg

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