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Research Articles

Computational approach identifies protein off-targets for Isoniazid-NAD adduct: hypothesizing a possible drug resistance mechanism in Mycobacterium tuberculosis

, , , , , & show all
Pages 1697-1710 | Received 07 Mar 2019, Accepted 29 Apr 2019, Published online: 16 May 2019
 

Abstract

Isoniazid is an important antitubercular molecule identified as a drug of choice in tuberculosis treatment. As such, INH is an inactive prodrug; it acquires an active conformation by forming an adduct with NAD. The adduct targets inhA protein, a reductase responsible for fatty acid chain elongation in the cell wall of Mycobacterium tuberculosis. Resistance to INH is majorly contributed by mutations in inhA, katG and geneic and non-geneic regions associated with efflux genes. Despite being widespread, the mechanism of resistance remains unknown in ∼15% of INH-resistant strains. Studies report that an intracellular increase in NADH concentration prevents inhA inhibition, leading to INH resistance. In the pursuit of finding possible resistance mechanisms, we set out to find NAD binding proteins to explore similarities in structure and NAD binding property of these proteins with that of inhA. We identified 172 NAD binding proteins, of which 53 were identified to have sequence or structural similarity to inhA. By performing docking analysis on selected proteins, we identified INH-adduct to have good binding affinity despite very minimal structural similarity to inhA. This analysis was further supported by principal component analysis, which identified 65 proteins with NAD binding conformation similar to that of inhA. These findings prompt us to hypothesize that upon exposure to INH, bacteria tries to reduce inhA susceptibility by inducing expression of these NAD binding proteins through increase in NADH concentration. This in turn favours off-target binding and leads to decreased binding and potency of INH, thus contributing indirectly to INH resistance.

Communicated by Ramaswamy H. Sarma

Additional information

Funding

The authors wish to acknowledge Indian Council of Medical Research (ICMR) – Biomedical Informatics and National Institute for Research in Tuberculosis for financial support.

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