Mugumbate Grace, Nyathi Brilliant, Zindoga Albert, Munyuki Gadzikano
Department of Chemical Sciences, Midlands State University, Gweru, Zimbabwe.
Department of Chemistry, Chinhoyi University of Technology, Chinhoyi, Zimbabwe.
Front Mol Biosci. 2021 Sep 28;8:643849. doi: 10.3389/fmolb.2021.643849. eCollection 2021.
The emergence of drug-resistant strains of () impedes the End TB Strategy by the World Health Organization aiming for zero deaths, disease, and suffering at the hands of tuberculosis (TB). Mutations within anti-TB drug targets play a major role in conferring drug resistance within ; hence, computational methods and tools are being used to understand the mechanisms by which they facilitate drug resistance. In this article, computational techniques such as molecular docking and molecular dynamics are applied to explore point mutations and their roles in affecting binding affinities for anti-TB drugs, often times lowering the protein's affinity for the drug. Advances and adoption of computational techniques, chemoinformatics, and bioinformatics in molecular biosciences and resources supporting machine learning techniques are in abundance, and this has seen a spike in its use to predict mutations in . This article highlights the importance of molecular modeling in deducing how point mutations in proteins confer resistance through destabilizing binding sites of drugs and effectively inhibiting the drug action.
（）耐药菌株的出现阻碍了世界卫生组织旨在实现结核病（TB）导致的死亡、疾病和痛苦归零的终结结核病战略。抗结核药物靶点内的突变在赋予（）耐药性方面起主要作用；因此，正在使用计算方法和工具来了解它们促进耐药性的机制。在本文中，诸如分子对接和分子动力学等计算技术被用于探索点突变及其在影响抗结核药物结合亲和力方面的作用，通常会降低蛋白质对药物的亲和力。计算技术、化学信息学和生物信息学在分子生物科学中的进展和应用以及支持机器学习技术的资源丰富，这使得其在预测（）突变方面的使用激增。本文强调了分子建模在推断蛋白质中的点突变如何通过破坏药物结合位点的稳定性并有效抑制药物作用来赋予耐药性方面的重要性。
