A novel machine learning-based screening identifies statins as inhibitors of the calcium pump SERCA.
A novel machine learning-based screening identifies statins as inhibitors of the calcium pump SERCA.
复制标题
DOI：
10.1016/j.jbc.2023.104681
复制
发表时间：
2023-05
影响因子：
4.8
通讯作者：
Espinoza-Fonseca, L. Michel
中科院分区：
文献类型：
--
作者：
Cruz-Cortes, Carlos;Velasco-Saavedra, M. Andres;Gortari, Eli Fernandez-de;Guerrero-Serna, Guadalupe;Aguayo-Ortiz, Rodrigo;Espinoza-Fonseca, L. Michel
我们报告了一种新的小分子筛选方法，该方法结合了数据增强和机器学习，以识别食品和药物管理局（FDA）批准的与骨骼（SERCA1a）和心脏（SERCA2a）肌肉钙泵（肌浆网Ca2+- atp酶，SERCA）相互作用的药物。这种方法利用关于小分子效应物的信息来绘制和探测药理学靶点的化学空间，从而可以高精度地筛选小分子的大型数据库，包括已批准的和正在研究的药物。我们之所以选择SERCA，是因为它在肌肉的兴奋-收缩-松弛循环中起着重要作用，它代表了骨骼肌和心肌的主要靶点。机器学习模型预测SERCA1a和SERCA2a是7种他汀类药物的药理学靶点，这是一组经fda批准的3-羟基-3-甲基戊二酰辅酶a还原酶抑制剂，在临床用作降脂药物。我们通过体外atp酶测定验证了机器学习预测，表明几种fda批准的他汀类药物是SERCA1a和SERCA2a的部分抑制剂。互补原子模拟预测这些药物结合到泵的两个不同的变构位点。我们的研究结果表明，serca介导的Ca2+转运可能被一些他汀类药物（如阿托伐他汀）靶向，从而提供了一种分子途径来解释文献中报道的他汀类药物相关毒性。这些研究表明，数据增强和基于机器学习的筛选作为识别脱靶相互作用的通用平台的适用性，并且这种方法的适用性扩展到药物发现。
We report a novel small-molecule screening approach that combines data augmentation and machine learning to identify Food and Drug Administration (FDA)-approved drugs interacting with the calcium pump (Sarcoplasmic reticulum Ca2+-ATPase, SERCA) from skeletal (SERCA1a) and cardiac (SERCA2a) muscle. This approach uses information about small-molecule effectors to map and probe the chemical space of pharmacological targets, thus allowing to screen with high precision large databases of small molecules, including approved and investigational drugs. We chose SERCA because it plays a major role in the excitation-contraction-relaxation cycle in muscle and it represents a major target in both skeletal and cardiac muscle. The machine learning model predicted that SERCA1a and SERCA2a are pharmacological targets for seven statins, a group of FDA-approved 3-hydroxy-3-methylglutaryl coenzyme A reductase inhibitors used in the clinic as lipid-lowering medications. We validated the machine learning predictions by using in vitro ATPase assays to show that several FDA-approved statins are partial inhibitors of SERCA1a and SERCA2a. Complementary atomistic simulations predict that these drugs bind to two different allosteric sites of the pump. Our findings suggest that SERCA-mediated Ca2+ transport may be targeted by some statins (e.g., atorvastatin), thus providing a molecular pathway to explain statin-associated toxicity reported in the literature. These studies show the applicability of data augmentation and machine learning-based screening as a general platform for the identification of off-target interactions and the applicability of this approach extends to drug discovery.
登录
查看更多内容
DOI：
10.1021/ci010132r
发表时间：
2002-11-01
期刊：
JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES
影响因子：
--
作者：
Durant, JL;Leland, BA;Nourse, JG
通讯作者：
Nourse, JG
影响因子：
4.9
作者：
Bleeker NP;Cornea RL;Thomas DD;Xing C
通讯作者：
Xing C
影响因子：
8.5
作者：
Flexa, Caio;Gomes, Walisson;Sales, Claudomiro
通讯作者：
Sales, Claudomiro
影响因子：
5.3
作者：
Hill, BJF;Dixon, JL;Sturek, M
通讯作者：
Sturek, M
影响因子：
9.8
作者：
Ciociola, Arthur A.;Cohen, Lawrence B.;Kulkarni, Prasad
通讯作者：
Kulkarni, Prasad