Karthik Raman, Preethi Rajagopalan, Nagasuma Chandra

Mycobacterium tuberculosis is the focus of several investigations for design of newer drugs, as tuberculosis remains a major epidemic despite the availability of several drugs and a vaccine. Mycobacteria owe many of their unique qualities to mycolic acids, which are known to be important for their growth, survival, and pathogenicity. Mycolic acid biosynthesis has therefore been the focus of a number of biochemical and genetic studies. It also turns out to be the pathway inhibited by front-line anti-tubercular drugs such as isoniazid and ethionamide. Recent years have seen the emergence of systems-based methodologies that can be used to study microbial metabolism. Here, we seek to apply insights from flux balance analyses of the mycolic acid pathway (MAP) for the identification of anti-tubercular drug targets. We present a comprehensive model of mycolic acid synthesis in the pathogen M. tuberculosis involving 197 metabolites participating in 219 reactions catalysed by 28 proteins. Flux balance analysis (FBA) has been performed on the MAP model, which has provided insights into the metabolic capabilities of the pathway. In silico systematic gene deletions and inhibition of InhA by isoniazid, studied here, provide clues about proteins essential for the pathway and hence lead to a rational identification of possible drug targets. Feasibility studies using sequence analysis of the M. tuberculosis H37Rv and human proteomes indicate that, apart from the known InhA, potential targets for anti-tubercular drug design are AccD3, Fas, FabH, Pks13, DesA1/2, and DesA3. Proteins identified as essential by FBA correlate well with those previously identified experimentally through transposon site hybridisation mutagenesis. This study demonstrates the application of FBA for rational identification of potential anti-tubercular drug targets, which can indeed be a general strategy in drug design. The targets, chosen based on the critical points in the pathway, form a ready shortlist for experimental testing.