Structural analyses and classification of novel isoniazid resistance coupled mutational landscapes in Mycobacterium tuberculosis: a combined molecular docking and MD simulation study

Abstract

Drug resistance in Mycobacterium tuberculosis has become a major challenge to the current regime of treatment as well as to the containment of the disease globally. The molecular and genetic studies identified frequently occurring point mutations in the virulent protein such as KatG of M. tuberculosis resulted in the development of isoniazid tolerance in the pathogen. This study aims to analyze the structural basis of the disease mutations available in the literature as well as to predict novel alteration in the KatG which may cause similar deleterious effects. Around 15 experimentally derived mutations were included in this study and pathogenic mutational landscapes containing 60 site-specific alterations were predicted using the available in silico techniques. The effects of these mutations on the stability of the protein were studied and an exhaustive docking study was conducted for each classified perturbations, which identify the highest changes in the binding energies in p.Meth255Ile among experimental and p.Ala222Arg in computationally predicted mutations. Furthermore, the structural effects on these substitutions were analyzed using the principles of molecular dynamic simulations each for a 100 ns time scale, which validated the interaction studies. The outcome of this study may enable the identification of the novel drug resistance-associated point mutations which were not previously reported and may contribute significantly in a variety of experimental studies as well as facilitate the process of drug design and discovery.

Communicated by Ramaswamy H. Sarma

Keywords:

• Mycobacterium tuberculosis
• drug resistance
• isoniazid tolerance
• mutational landscape
• molecular docking
• molecular dynamics simulations

Acknowledgments

The authors would like to acknowledge the Centre for High-Performance Computing (CHPC), South Africa, for providing the computational infrastructure. The authors are very thankful for Taif University researcher supporting project Number TURSP/91, Taif University, Taif, Saudi Arabia.

Disclosure statement

Authors declare no competing interests.