期刊名称：Computational and Structural Biotechnology Journal
印刷版ISSN：2001-0370
出版年度：2021
卷号：19
页码：3708-3719
DOI：10.1016/j.csbj.2021.06.034
出版社：Computational and Structural Biotechnology Journal
摘要：Mycobacterium tuberculosis 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 M. tuberculosis proteins; allowing novel targets to be investigated. This review will focus on structure-based in silico 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.
关键词：Drug discovery ; Mycobacterium tuberculosis ; In silico ; Docking ; Machine learning ; cMD Classical Molecular Dynamic ; cryo-EM cryogenic electron microscopy ; CV collective variable ; LIE Linear Interaction Energy ; MD Molecular Dynamic ; MDR multi-drug resistant ; MMPB(GB)SA Molecular Mechanics with Poisson Boltzmann (or generalised Born) and Surface Area solvation ; Mt Mycobacterium tuberculosis ; ns nanosecond ; PTC peptidyl transferase centre ; RMSD root-mean square-deviation ; Tuberculosis TB
