Structure-based in silico approaches for drug discovery against Mycobacterium tuberculosis

Abstract

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.

Keywords: CV, collective variable; Docking; Drug discovery; In silico; 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; Machine learning; Mt, Mycobacterium tuberculosis; Mycobacterium tuberculosis; PTC, peptidyl transferase centre; RMSD, root-mean square-deviation; Tuberculosis, TB; cMD, Classical Molecular Dynamic; cryo-EM, cryogenic electron microscopy; ns, nanosecond.