a School of Chemistry , University of Hyderabad , Hyderabad 500046 , India.
b Computer Aided Drug Design Laboratory, Department of Pharmacy , Birla Institute of Technology & Science-Pilani, Hyderabad Campus , Hyderabad 500078 , India.
J Biomol Struct Dyn. 2018 Sep;36(12):3184-3198. doi: 10.1080/07391102.2017.1384398. Epub 2017 Oct 17.
MurG (Rv2153c) is a key player in the biosynthesis of the peptidoglycan layer in Mycobacterium tuberculosis (Mtb). This work is an attempt to highlight the structural and functional relationship of Mtb MurG, the three-dimensional (3D) structure of protein was constructed by homology modelling using Discovery Studio 3.5 software. The quality and consistency of generated model was assessed by PROCHECK, ProSA and ERRAT. Later, the model was optimized by molecular dynamics (MD) simulations and the optimized model complex with substrate Uridine-diphosphate-N-acetylglucosamine (UD1) facilitated us to employ structure-based virtual screening approach to obtain new hits from Asinex database using energy-optimized pharmacophore modelling (e-pharmacophore). The pharmacophore model was validated using enrichment calculations, and finally, validated model was employed for high-throughput virtual screening and molecular docking to identify novel Mtb MurG inhibitors. This study led to the identification of 10 potential compounds with good fitness, docking score, which make important interactions with the protein active site. The 25 ns MD simulations of three potential lead compounds with protein confirmed that the structure was stable and make several non-bonding interactions with amino acids, such as Leu290, Met310 and Asn167. Hence, we concluded that the identified compounds may act as new leads for the design of Mtb MurG inhibitors.
MurG(Rv2153c)是结核分枝杆菌(Mtb)肽聚糖层生物合成的关键参与者。本工作旨在强调 Mtb MurG 的结构和功能关系,使用 Discovery Studio 3.5 软件通过同源建模构建蛋白质的三维(3D)结构。通过 PROCHECK、ProSA 和 ERRAT 评估生成模型的质量和一致性。然后,通过分子动力学(MD)模拟对模型进行优化,并与底物尿苷二磷酸-N-乙酰葡萄糖胺(UD1)的优化模型复合物结合,使我们能够使用能量优化的药效团建模(e-pharmacophore)从 Asinex 数据库中获得新的命中。使用富集计算验证药效团模型,最后,使用验证后的模型进行高通量虚拟筛选和分子对接,以鉴定新型 Mtb MurG 抑制剂。这项研究鉴定了 10 种具有良好适应性、对接评分的潜在化合物,它们与蛋白质的活性位点有重要的相互作用。与蛋白质的三种潜在先导化合物的 25ns MD 模拟证实了结构的稳定性,并与氨基酸(如 Leu290、Met310 和 Asn167)形成了几种非键相互作用。因此,我们得出结论,鉴定出的化合物可能作为 Mtb MurG 抑制剂设计的新先导化合物。