HARP: a database of structural impacts of systematic missense mutations in drug targets of Mycobacterium leprae.

Computational Saturation Mutagenesis is an in-silico approach that employs systematic mutagenesis of each amino acid residue in the protein to all other amino acid types, and predicts changes in thermodynamic stability and affinity to the other subunits/protein counterparts, ligands and nucleic acid molecules. The data thus generated are useful in understanding the functional consequences of mutations in antimicrobial resistance phenotypes. In this study, we applied computational saturation mutagenesis to three important drug-targets in Mycobacterium leprae (M. leprae) for the drugs dapsone, rifampin and ofloxacin namely Dihydropteroate Synthase (DHPS), RNA Polymerase (RNAP) and DNA Gyrase (GYR), respectively. M. leprae causes leprosy and is an obligate intracellular bacillus with limited protein structural information associating mutations with phenotypic resistance outcomes in leprosy. Experimentally solved structures of DHPS, RNAP and GYR of M. leprae are not available in the Protein Data Bank, therefore, we modelled the structures of these proteins using template-based comparative modelling and introduced systematic mutations in each model generating 80,902 mutations and mutant structures for all the three proteins. Impacts of mutations on stability and protein-subunit, protein-ligand and protein-nucleic acid affinities were computed using various in-house developed and other published protein stability and affinity prediction software. A consensus impact was estimated for each mutation using qualitative scoring metrics for physicochemical properties and by a categorical grouping of stability and affinity predictions. We developed a web database named HARP (a database of Hansen's Disease Antimicrobial Resistance Profiles), which is accessible at the URL - https://harp-leprosy.org and provides the details to each of these predictions.

Computational saturation mutagenesis to predict structural consequences of systematic mutations in the beta subunit of RNA polymerase in Mycobacterium leprae.

Rifampin resistance in leprosy may remain undetected due to the lack of rapid and effective diagnostic tools. A quick and reliable method is essential to determine the impacts of emerging detrimental mutations in the drug targets. The functional consequences of missense mutations in the ß-subunit of RNA polymerase (RNAP) in Mycobacterium leprae (M. leprae) contribute to phenotypic resistance to rifampin in leprosy. Here, we report in-silico saturation mutagenesis of all residues in the ß-subunit of RNAP to all other 19 amino acid types (generating 21,394 mutations for 1126 residues) and predict their impacts on overall thermodynamic stability, on interactions at subunit interfaces, and on ß-subunit-RNA and rifampin affinities (only for the rifampin binding site) using state-of-the-art structure, sequence and normal mode analysis-based methods. Mutations in the conserved residues that line the active-site cleft show largely destabilizing effects, resulting in increased relative solvent accessibility and a concomitant decrease in residue-depth (the extent to which a residue is buried in the protein structure space) of the mutant residues. The mutations at residue positions S437, G459, H451, P489, K884 and H1035 are identified as extremely detrimental as they induce highly destabilizing effects on the overall protein stability, and nucleic acid and rifampin affinities. Destabilizing effects were predicted for all the clinically/experimentally identified rifampin-resistant mutations in M. leprae indicating that this model can be used as a surveillance tool to monitor emerging detrimental mutations that destabilise RNAP-rifampin interactions and confer rifampin resistance in leprosy. AUTHOR SUMMARY: The emergence of primary and secondary drug resistance to rifampin in leprosy is a growing concern and poses a threat to the leprosy control and elimination measures globally. In the absence of an effective in-vitro system to detect and monitor phenotypic resistance to rifampin in leprosy, diagnosis mainly relies on the presence of mutations in drug resistance determining regions of the rpoB gene that encodes the ß-subunit of RNAP in M. leprae. Few labs in the world perform mouse food pad propagation of M. leprae in the presence of drugs (rifampin) to determine growth patterns and confirm resistance, however the duration of these methods lasts from 8 to 12 months making them impractical for diagnosis. Understanding molecular mechanisms of drug resistance is vital to associating mutations to clinically detected drug resistance in leprosy. Here we propose an in-silico saturation mutagenesis approach to comprehensively elucidate the structural implications of any mutations that exist or that can arise in the ß-subunit of RNAP in M. leprae. Most of the predicted mutations may not occur in M. leprae due to fitness costs but the information thus generated by this approach help decipher the impacts of mutations across the structure and conversely enable identification of stable regions in the protein that are least impacted by mutations (mutation coolspots) which can be a potential choice for small molecule binding and structure guided drug discovery.

Mycobacterial genomics and structural bioinformatics: opportunities and challenges in drug discovery.

Of the more than 190 distinct species of Mycobacterium genus, many are economically and clinically important pathogens of humans or animals. Among those mycobacteria that infect humans, three species namely Mycobacterium tuberculosis (causative agent of tuberculosis), Mycobacterium leprae (causative agent of leprosy) and Mycobacterium abscessus (causative agent of chronic pulmonary infections) pose concern to global public health. Although antibiotics have been successfully developed to combat each of these, the emergence of drug-resistant strains is an increasing challenge for treatment and drug discovery. Here we describe the impact of the rapid expansion of genome sequencing and genome/pathway annotations that have greatly improved the progress of structure-guided drug discovery. We focus on the applications of comparative genomics, metabolomics, evolutionary bioinformatics and structural proteomics to identify potential drug targets. The opportunities and challenges for the design of drugs for M. tuberculosis, M. leprae and M. abscessus to combat resistance are discussed.