Modeling the Structural Origins of Drug Resistance to Isoniazid via key Mutations in Mycobacterium tuberculosis Catalase-Peroxidase, KatG

Author:

Marney Matthew W.,Metzger Robert P.,Hecht David,Valafar Faramarz

Abstract

AbstractWHO reported 10.4 million new tuberculosis (TB) cases and 1.8 million deaths in 2015, making M. tuberculosis the most successful human pathogen with highest mortality among infectious diseases.[1,2] Drug-resistant TB is a major threat to global TB control.[2,3] Recently Torres et al. [4] identified 14 novel substitutions in M. tuberculosis-KatG (the enzyme associated with resistance to isoniazid—an important first-line anti-TB drug) and demonstrated that 12 of the 14 can cause INH-resistance in M. smegmatis. This study presents an in silico structure-based analysis of these 14 amino acid substitutions using homology models and x-ray crystal structures (when available) in M. tuberculosis. Our models demonstrate that several of these mutations cluster around three openings in the KatG tertiary structure which appear to initiate channels to the heme group at the catalytic center of the enzyme. We studied the effects of these mutations on the tertiary structure of KatG, focusing on conformational changes in the three channels in the protein structure. Our results suggest that the 14 novel mutations sufficiently restrict one or more of these access channels, thus potentially preventing INH from reaching the catalytic heme. These observations provide valuable insights into the structure-based origins of INH resistance and provide testable hypotheses for future experimental studies.

Publisher

Cold Spring Harbor Laboratory

Reference28 articles.

1. World Health Organization. WHO Global tuberculosis report 2015. vol. 1. Geneva, Switzerland: 2015.

2. World Health Organization. WHO | Global Tuberculosis Report 2016. 2016.

3. WHO | Tuberculosis Fact Sheet 104 n.d.

4. Novel katG mutations causing isoniazid resistance in clinical M. tuberculosis isolates

5. The Global Burden of Latent Tuberculosis Infection: A Re-estimation Using Mathematical Modelling

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