Kingdon Alexander D H, Alderwick Luke J
Institute of Microbiology and Infection, School of Biosciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, United Kingdom.
Comput Struct Biotechnol J. 2021 Jun 24;19:3708-3719. doi: 10.1016/j.csbj.2021.06.034. eCollection 2021.
is the causative agent of TB and was estimated to cause 1.4 million death in 2019, alongside 10 million new infections. Drug resistance is a growing issue, with multi-drug resistant infections representing 3.3% of all new infections, hence novel antimycobacterial drugs are urgently required to combat this growing health emergency. Alongside this, increased knowledge of gene essentiality in the pathogenic organism and larger compound databases can aid in the discovery of new drug compounds. The number of protein structures, X-ray based and modelled, is increasing and now accounts for greater than > 80% of all predicted proteins; allowing novel targets to be investigated. This review will focus on structure-based approaches for drug discovery, covering a range of complexities and computational demands, with associated antimycobacterial examples. This includes molecular docking, molecular dynamic simulations, ensemble docking and free energy calculations. Applications of machine learning onto each of these approaches will be discussed. The need for experimental validation of computational hits is an essential component, which is unfortunately missing from many current studies. The future outlooks of these approaches will also be discussed.
结核分枝杆菌是结核病的病原体，据估计，2019年它导致了140万人死亡，同时还有1000万新感染病例。耐药性问题日益严重，耐多药感染占所有新感染病例的3.3%，因此迫切需要新型抗分枝杆菌药物来应对这一日益严重的健康危机。与此同时，对致病生物体中基因必需性的了解增加以及更大的化合物数据库有助于发现新的药物化合物。基于X射线和建模的蛋白质结构数量不断增加，现在占所有预测蛋白质的比例超过80%，这使得能够研究新的靶点。本综述将重点关注基于结构的药物发现方法，涵盖一系列复杂性和计算需求，并列举相关的抗分枝杆菌实例。这包括分子对接、分子动力学模拟、整合对接和自由能计算。将讨论机器学习在这些方法中的应用。对计算命中结果进行实验验证的必要性是一个重要组成部分，但遗憾的是，目前许多研究都缺少这一点。还将讨论这些方法的未来展望。
