Tuberculosis (TB), an infectious disease caused by Mycobacterium tuberculosis (M.tb), causes highest number of deaths globally for any bacterial disease necessitating novel diagnosis and treatment strategies. High-throughput sequencing methods generate a large amount of data which could be exploited in determining multi-drug resistant (MDR-TB) associated mutations. The present work is a computational framework that uses artificial intelligence (AI) based machine learning (ML) approaches for predicting resistance in the genes rpoB, inhA, katG, pncA, gyrA and gyrB for the drugs rifampicin, isoniazid, pyrazinamide and fluoroquinolones. The single nucleotide variations were represented by several sequence and structural features that indicate the influence of mutations on the target protein coded by each gene. We used ML algorithms-naïve bayes, k nearest neighbor, support vector machine, and artificial neural network, to build the prediction models. The classification models had an average accuracy of 85% across all examined genes and were evaluated on an external unseen dataset to demonstrate their application. Further, molecular docking and molecular dynamics simulations were performed for wild type and predicted resistance causing mutant protein and anti-TB drug complexes to study their impact on the conformation of proteins to confirm the observed phenotype.

No abstract

Background: Mycobacterium tuberculosis (M.tb), the etiological agent of Tuberculosis (TB), is the second leading cause of mortality after COVID-19, with a global death toll of 1.5 million in 2020. The escalating cases of drug-resistant TB are further worsening the current situation and making TB treatment extremely challenging. Thus, it is crucial to look for new anti-TB drugs with novel mechanisms of action and high efficacy. The DnaG of M.tb replication machinery is an essential protein for pathogen survival. Also, its imperative primase activity and lack of structural homology to human proteins, make it a possible target for drug development. Methods: In this presented study, using a computational structure-based drug repurposing approach, Food and drug administration (FDA) approved drugs were virtually screened against M.tb DnaG to identify potential inhibitors. Five drugs viz. Caspofungin, Doxorubicin, Mitoxantrone, Vapreotide, and Zanamivir showed higher molecular docking scores. Further RMSD, RMSF, Rg, SASA, H-bond, and PCA analysis of these drugs and DnaG complexes. Alamar Blue Assay further evaluated the anti-TB activity of these drugs in vitro using H37Ra and H37Rv M.tb strains. Results: The top results for DnaG binding included several FDA-approved drugs, out of which five were selected and subjected to Molecular dynamic simulation and displayed their high binding affinity, stable interaction, more compactness, and reduced atomic motion. The minimum inhibitory concentration of Doxorubicin, Mitoxantrone, and Vapreotide were detected in the range of 0.19-25 µg/ml for both H37Ra, and H37Rv, respectively. Conclusions: Our findings from the study present potential repurposed drug candidates that target DnaG and inhibit M.tb survival. Thorough investigations of these compounds may lead to the discovery of new anti-TB therapeutics.