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结核分枝杆菌 H37Rv 未鉴定蛋白(RipD)的计算机模拟和体外研究:对结核病治疗的深入了解。

In silico and in vitro study of Mycobacterium tuberculosis H37Rv uncharacterized protein (RipD): an insight on tuberculosis therapeutics.

机构信息

School of Biotechnology, KIIT Deemed to Be University, Bhubaneswar, Odisha, India.

National Veterinary Institute, Debre Zeit, Ethiopia.

出版信息

J Mol Model. 2022 May 27;28(6):171. doi: 10.1007/s00894-022-05148-1.

DOI:10.1007/s00894-022-05148-1
PMID:35624324
Abstract

Tuberculosis caused by Mycobacterium tuberculosis (Mtb) is responsible for the highest global health problem, with the deaths of millions of people. With prevalence of multiple drug resistance (MDR) strains and extended therapeutic times, it is important to discover small molecule inhibitors against novel hypothetical proteins of the pathogen. In this study, a virtual screening protocol was carried out against MtbH37Rv hypothetical protein RipD (Rv1566c) for the identification of potential small molecule inhibitors. The 3D model of the protein structure binding site was used for virtual screening (VS) of inhibitors from the Pathogen Box, followed by its validation through a molecular docking study. The stability of the protein-ligand complex was assessed using a 150 ns molecular dynamics simulation. MM-PBSA and MM-GBSA are the two approaches that were used to perform the trajectory analysis and determine the binding free energies, respectively. The ligand binding was observed to be stable across the entire time frame with an approximate binding free energy of -22.9916 kcal/mol. The drug-likeness of the inhibitors along with a potential anti-tuberculosis compound was validated by ADMET prediction software. Furthermore, a CFU inhibition assay was used to validate the best hit compound's in vitro inhibitory efficacy against a non-pathogenic Mycobacterium smegmatis MC2155 under low nutrient culture conditions. The study reported that the compound proposed in our study (Pathogen Box ID: MMV687700) will be useful for the identification of potential inhibitors against Mtb in future.

摘要

结核分枝杆菌(Mtb)引起的结核病是全球面临的最大健康问题之一,导致数百万人死亡。由于耐多药(MDR)菌株的流行和延长的治疗时间,发现针对病原体新假定蛋白的小分子抑制剂非常重要。在这项研究中,针对 MtbH37Rv 假定蛋白 RipD(Rv1566c)进行了虚拟筛选协议,以鉴定潜在的小分子抑制剂。使用蛋白结构结合位点的 3D 模型进行抑制剂的虚拟筛选(VS),然后通过分子对接研究对其进行验证。使用 150ns 的分子动力学模拟评估蛋白-配体复合物的稳定性。MM-PBSA 和 MM-GBSA 是两种用于进行轨迹分析和确定结合自由能的方法。观察到配体结合在整个时间范围内稳定,结合自由能约为-22.9916 kcal/mol。通过 ADMET 预测软件验证了抑制剂的类药性和潜在的抗结核化合物。此外,还使用 CFU 抑制测定法在低营养培养条件下验证了最佳命中化合物对非致病性耻垢分枝杆菌 MC2155 的体外抑制效果。该研究报告称,我们研究中提出的化合物(病原体框 ID:MMV687700)将有助于未来鉴定针对 Mtb 的潜在抑制剂。

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