Mycobacterium tuberculosis is the causative agent of tuberculosis (TB), a highly contagious disease affecting millions of individuals globally. The emergence of drug-resistance becomes a significant obstacle in the treatment of tuberculosis (TB). The Mycobacterium tuberculosis protein Rv1979c has been implicated in drug resistance, yet its function and potential inhibitors remain largely unexplored. In this study, we employed a comprehensive computational approach to unravel the role of Rv1979c and identify potential inhibitors from natural sources.

The investigation was initiated by revealing the three-dimensional structure of Rv1979c via the Threading/Fold recognition method. Subsequently, a functional analysis of its molecular mechanism using a variety of computational tools yielded valuable insights. In addition, we screened a diverse collection of 11,708 phytochemicals against the Rv1979c target to identify potential inhibitors followed by molecular dynamic simulation. This exhaustive screening and simulation led to the identification of several prospective phytochemical candidates with significant binding affinity to the protein, indicating their potential as Rv1979c inhibitors.

This interdisciplinary study combines computational biology and drug discovery techniques to cast light on the function of Rv1979c and reveal potential strategies for combating pathogenesis in M. tuberculosis. The findings provide a foundation for further experimental validation and the development of novel therapeutic agents to address the evolving challenges of TB drug resistance.