Research Projects

Tuberculosis (TB), caused by the bacterium Mycobacterium tuberculosis (Mtb), continues to pose a major public health challenge worldwide. Rising incidence of drug-resistant strains with limited arsenals for effective treatment of TB underscores the need of identifying novel therapeutic scaffolds to combat TB. Mtb is continuously exposed to various stressors i.e., reactive oxygen species (ROS), reactive nitrogen species (RNS), acidic environments and, nutrient deprivation, which hinder the bacterium's growth and survival. Iron-sulfur (Fe-S) clusters are recognized as some of the ancient proteins vulnerable to damage from ROS, RNS, as well as iron deficiency. Mtb harbours more than 50 Fe-S proteins indispensable for energy production and conversion, transcription regulation, DNA repair, antibiotic resistance and persistence in hostile environments. The Fe-S protein, Rv0338c is a novel transmembrane heterodisulfide reductase vital for Mtb survival and virulence and is perceived as a promising therapeutic target for novel anti-TB drug development. 6,11-Dioxobenzo[f]pyrido[1,2-a]indoles, a novel class of polycyclic compounds bearing notable pharmacophoric features with potential anti-mycobacterial properties target Rv0338c protein. Its unique structural scaffold offers new avenues for optimization to improve specificity and potency against Mtb. Unlike conventional screening techniques, recently developed generative-AI approaches emphasizes creation of new molecules with targeted pharmacological properties, without relying on existing templates or molecular structures have transformed the drug discovery process, offering powerful methodologies for the design, optimization, and activity prediction of novel compounds covering significantly broader chemical space. Generative-AI is particularly advantageous towards identifying novel classes of compounds (de novo) that are not present in current libraries, especially for target proteins that lack initial hit compounds or have developed resistance to existing treatments like Mtb. The proposed study aimed to develop and optimize novel 6,11-Dioxobenzo[f]pyrido[1,2-a] indole (DBPI) derivatives targeting essential protein Rv0338c of MTB by leveraging the principles of medicinal chemistry and generative-AI. Density function theory, machine-learning guided docking and advanced molecular dynamics simulations will be used to assess the ability of newly developed compounds to inhibit Mtb Rv0338c in-silico. The in silico-prioritized molecules will be subjected to synthesis, spectral-characterization and in vitro assays in order to assess their efficacy against different sensitive and drug resistant strains of Mtb. The proposed study combines the strengths of AI/ML and experimental validation to develop novel anti-TB agents, offering a gateway for the development of potent compounds aimed at tackling the challenge of drug resistance against Mtb, and thus setting a benchmark for future antimicrobial research.