V.P. Kukhar Institute of Bioorganic Chemistry and Petrochemistry, National Academy of Sciences of Ukraine, 02094, Kyiv-94, Murmanska Str,1, Kyiv, Ukraine.
V.P. Kukhar Institute of Bioorganic Chemistry and Petrochemistry, National Academy of Sciences of Ukraine, 02094, Kyiv-94, Murmanska Str,1, Kyiv, Ukraine.
Comput Biol Chem. 2020 Apr;85:107224. doi: 10.1016/j.compbiolchem.2020.107224. Epub 2020 Jan 24.
Spread of multidrug-resistant Escherichia coli clinical isolates is a main problem in the treatment of infectious diseases. Therefore, the modern scientific approaches in decision this problem require not only a prevention strategy, but also the development of new effective inhibitory compounds with selective molecular mechanism of action and low toxicity. The goal of this work is to identify more potent molecules active against E. coli strains by using machine learning, docking studies, synthesis and biological evaluation. A set of predictive QSAR models was built with two publicly available structurally diverse data sets, including recent data deposited in PubChem. The predictive ability of these models tested by a 5-fold cross-validation, resulted in balanced accuracies (BA) of 59-98% for the binary classifiers. Test sets validation showed that the models could be instrumental in predicting the antimicrobial activity with an accuracy (with BA = 60-99 %) within the applicability domain. The models were applied to screen a virtual chemical library, which was designed to have activity against resistant E. coli strains. The eight most promising compounds were identified, synthesized and tested. All of them showed the different levels of anti-E. coli activity and acute toxicity. The docking results have shown that all studied compounds are potential DNA gyrase inhibitors through the estimated interactions with amino acid residues and magnesium ion in the enzyme active center The synthesized compounds could be used as an interesting starting point for further development of drugs with low toxicity and selective molecular action mechanism against resistant E. coli strains. The developed QSAR models are freely available online at OCHEM http://ochem.eu/article/112525 and can be used to virtual screening of potential compounds with anti-E. coli activity.
多药耐药大肠杆菌临床分离株的传播是治疗传染病的主要问题。因此，解决这个问题的现代科学方法不仅需要预防策略，还需要开发新的具有选择性分子作用机制和低毒性的有效抑制化合物。这项工作的目标是通过使用机器学习、对接研究、合成和生物评估来识别针对大肠杆菌菌株更有效的分子。使用两个公开的结构多样的数据集（包括最近在 PubChem 中存储的数据）构建了一组预测性 QSAR 模型。通过 5 倍交叉验证测试这些模型的预测能力，得到了 59-98%的二元分类器平衡准确性（BA）。测试集验证表明，这些模型可以在适用于预测抗菌活性的范围内（BA 在 60-99%之间），具有预测准确性。将模型应用于筛选虚拟化学库，该库旨在针对耐药大肠杆菌菌株具有活性。确定了八个最有希望的化合物，进行了合成和测试。它们都显示出不同程度的抗大肠杆菌活性和急性毒性。对接结果表明，所有研究的化合物都是潜在的 DNA 拓扑异构酶抑制剂，通过与酶活性中心的氨基酸残基和镁离子的估计相互作用。合成的化合物可以作为进一步开发低毒性和选择性分子作用机制针对耐药大肠杆菌菌株的药物的有趣起点。开发的 QSAR 模型可在 OCHEM 上免费获得，网址为 http://ochem.eu/article/112525，并可用于具有抗大肠杆菌活性的潜在化合物的虚拟筛选。
