a State Key Laboratory of Microbial Metabolism & School of Life Sciences and Biotechnology, Shanghai Jiao Tong University , Shanghai , P. R. China.
J Biomol Struct Dyn. 2019 Sep;37(15):4051-4069. doi: 10.1080/07391102.2018.1537896. Epub 2018 Nov 18.
Phenazine compounds have good activity against (MTB). Based on the reported activities that were obtained in MTB H37Rv, a three-dimensional quantitative structure-activity relationship (3D-QSAR) model was built to design novel compounds against MTB. A fivefold cross-validation method and external validation were used to analyze the accuracy of forecasting. The model has a cross-validation coefficient =0.7 and a non-cross-validation coefficient = 0.903, indicating that the model has good predictive possibility. The design of anti-pneumococcus MTB compounds was guided by the obtained 3D-QSAR model, and several compounds with better activity were obtained. To test the activity of these compounds, molecular docking, molecular dynamics simulation, and post-simulation analysis of the already reported drug targets in MTB were carried out. Among the total 15 drug targets, only three targets (Rv2361c, Rv2965c, and Rv3048c) were selected based on the docking results. Initial results reported that these compounds possessed good inhibition activity for Rv2361c. The top nine complexes of Rv2361 ligands were only subjected to MD simulation which resulted in a stable dynamics of the structures and showed a residual fluctuation in inhibitors binding pocket. Free energy reported that overall, the derivatives hold strong energy against the protein target. Energetic contribution results showed that residues, Asp76, Arg80, Asn124, Arg127, Arg244, and Arg250, play a major role in total energy. Systems biology approach validates shortlisted drug effect on the entire system which might be useful to predict potential drug in wet lab as well. Communicated by Ramaswamy H. Sarma.
吩嗪类化合物对 (MTB)具有良好的活性。基于在 MTB H37Rv 中获得的报道活性,构建了一个三维定量构效关系(3D-QSAR)模型,以设计针对 MTB 的新型化合物。使用五倍交叉验证方法和外部验证来分析预测的准确性。该模型的交叉验证系数 =0.7,非交叉验证系数 = 0.903,表明该模型具有良好的预测可能性。通过获得的 3D-QSAR 模型指导抗肺炎链球菌 MTB 化合物的设计,并获得了一些具有更好活性的化合物。为了测试这些化合物的活性,对已经报道的 MTB 药物靶点进行了分子对接、分子动力学模拟和模拟后分析。在总共 15 个药物靶点中,仅根据对接结果选择了三个靶点(Rv2361c、Rv2965c 和 Rv3048c)。初步结果表明,这些化合物对 Rv2361c 具有良好的抑制活性。仅对 Rv2361 配体的前九个复合物进行 MD 模拟,结果表明结构动力学稳定,并显示抑制剂结合口袋中存在残留波动。报告的自由能表明,总体而言,这些衍生物对蛋白质靶标具有很强的能量。能量贡献结果表明,残基 Asp76、Arg80、Asn124、Arg127、Arg244 和 Arg250 在总能量中起主要作用。系统生物学方法验证了候选药物对整个系统的作用,这可能有助于在湿实验室中预测潜在药物。由 Ramaswamy H. Sarma 交流。