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首页> 外文期刊>Assay and drug development technologies >Using Chemoinformatics, Bioinformatics, and Bioassay to Predict and Explain the Antibacterial Activity of Nonantibiotic Food and Drug Administration Drugs
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Using Chemoinformatics, Bioinformatics, and Bioassay to Predict and Explain the Antibacterial Activity of Nonantibiotic Food and Drug Administration Drugs

机译：使用化疗系体，生物信息学和生物测定来预测和解释非纤百病食品和药物管理药物的抗菌活性

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摘要

Discovering of new and effective antibiotics is a major issue facing scientists today. Luckily, the development of computer science offers new methods to overcome this issue. In this study, a set of computer software was used to predict the antibacterial activity of nonantibiotic Food and Drug Administration (FDA)approved drugs, and to explain their action by possible binding to well-known bacterial protein targets, along with testing their antibacterial activity against Gram-positive and Gram-negative bacteria. A three-dimensional virtual screening method that relies on chemical and shape similarity was applied using rapid overlay of chemical structures (ROCS) software to select candidate compounds from the FDA-approved drugs database that share similarity with 17 known antibiotics. Then, to check their antibacterial activity, disk diffusion test was applied on Staphylococcus aureus and Escherichia coli. Finally, a protein docking method was applied using HYBRID software to predict the binding of the active candidate to the target receptor of its similar antibiotic. Of the 1,991 drugs that were screened, 34 had been selected and among them 10 drugs showed antibacterial activity, whereby drotaverine and metoclopramide activities were without precedent reports. Furthermore, the docking process predicted that diclofenac, drotaverine, (S)-flurbiprofen, (S)ibuprofen, and indomethacin could bind to the protein target of their similar antibiotics. Nevertheless, their antibacterial activities are weak compared with those of their similar antibiotics, which can be potentiated further by performing chemical modifications on their structure.
机译：发现新和有效的抗生素是今天科学家面临的主要问题。幸运的是，计算机科学的发展提供了克服这个问题的新方法。在这项研究中，通过测试其抗菌活性以及测试它们的抗菌活性以及测试它们的抗菌活性，预测一组计算机软件来预测非纤维食品和药物管理（FDA）批准的药物的抗菌活性，并通过与众所周知的细菌蛋白靶标进行结合，以及测试其抗菌活性针对革兰氏阳性和革兰氏阴性细菌。使用化学结构（ROCS）软件的快速覆盖来施加依赖于化学和形状相似性的三维虚拟筛选方法，以从FDA批准的药物数据库中选择与17名已知抗生素相似性的候选化合物。然后，为了检查它们的抗菌活性，在金黄色葡萄球菌和大肠杆菌上施加盘扩散试验。最后，使用杂种软件施加蛋白酶对接方法，以预测活性候选的结合到其相似抗生素的目标受体。在筛选的1,991种药物中，选择了34种药物，其中10种药物显示出抗菌活性，由此德拉维犬和甲丙醇普拉胺活性没有先例的报告。此外，对接过程预测Diclofenac，Drotaverine，（S）-Flurbrofofen，（S）布洛芬和吲哚美辛可与其相似抗生素的蛋白质靶标结合。然而，与它们类似的抗生素相比，它们的抗菌活性较弱，这可以通过对其结构进行化学修饰来进一步加强。

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