Machine learning and docking models for Mycobacterium tuberculosis topoisomerase I
Machine learning and docking models for Mycobacterium tuberculosis topoisomerase I
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DOI：
10.1016/j.tube.2017.01.005
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发表时间：
2017-03-01
期刊：
影响因子：
3.2
通讯作者：
Nagaraja, Valakunja
中科院分区：
文献类型：
--
作者：
Ekins, Sean;Godbole, Adwait Anand;Nagaraja, Valakunja
除了FDA批准的用于抗结核分枝杆菌的药物外，针对新靶点的化合物短缺。拓扑异构酶I(Mttopo I)是一种重要的分枝杆菌酶，在这方面具有广阔的应用前景。然而，它面临着已知抑制剂短缺的问题。我们之前已经使用同源建模和对接等计算方法提出了38种FDA批准的药物进行测试，并鉴定了几个活性分子。接下来，我们将描述一个包含639种化合物的库的体外测试。这些数据被用来为Mttopo I创建机器学习模型，并进一步进行验证。组合Mttopo I贝叶斯模型的5重交叉验证接收器特征为0.74，灵敏度、特异度和符合值均大于0.76，并用于筛选可用于体外测试的商用化合物。最近描述的Mttopo I的晶体结构也与先前描述的同源模型进行了比较，然后用于对接Mttopo I活性物质去甲氯丙咪胺和丙咪嗪。总之，我们描述了我们使用机器学习建模和对接研究结合筛选所选分子进行酶抑制来识别Mttopo I的小分子抑制剂的努力。我们证明了小分子抑制剂对Mttopo I的实验抑制作用，并表明这种酶很容易成为铅分子开发的靶点。(C)2017爱思唯尔有限公司。保留所有权利。
There is a shortage of compounds that are directed towards new targets apart from those targeted by the FDA approved drugs used against Mycobacterium tuberculosis. Topoisomerase I (Mttopo I) is an essential mycobacterial enzyme and a promising target in this regard. However, it suffers from a shortage of known inhibitors. We have previously used computational approaches such as homology modeling and docking to propose 38 FDA approved drugs for testing and identified several active molecules. To follow on from this, we now describe the in vitro testing of a library of 639 compounds. These data were used to create machine learning models for Mttopo I which were further validated. The combined Mttopo I Bayesian model had a 5 fold cross validation receiver operator characteristic of 0.74 and sensitivity, specificity and concordance values above 0.76 and was used to select commercially available compounds for testing in vitro. The recently described crystal structure of Mttopo I was also compared with the previously described homology model and then used to dock the Mttopo I actives norclomipramine and imipramine. In summary, we describe our efforts to identify small molecule inhibitors of Mttopo I using a combination of machine learning modeling and docking studies in conjunction with screening of the selected molecules for enzyme inhibition. We demonstrate the experimental inhibition of Mttopo I by small molecule inhibitors and show that the enzyme can be readily targeted for lead molecule development. (C) 2017 Elsevier Ltd. All rights reserved.