Gen-AI Methods, Molecular Docking and Molecular Dynamics Simulations for Identification of Novel Inhibitors of MmPL3 Transporter of Mycobacterium tuberculosis

Mycobacterium tuberculosis (Mtb), the bacterium responsible for tuberculosis (TB), employs mycolic acids to build its cell wall. This robust structure plays a vital role in protecting the bacterium from external threats and contributes to its resistance against antibiotics. Mycobacterial membrane protein Large 3 (MmpL3), a secondary resistance nodulation division transporter, is essential in mycolic acid biosynthesis, transporting mycolic acid precursors into the periplasm using the proton motive force. Due to its role in bacterial cell wall formation, it is a promising target for new tuberculosis treatments. In this study, starting with 85 known MmPL3 compounds, the artificial intelligence (AI)-assisted tool "Design of Druglike Analogues (DeLA-Drug)" was employed to generate about 15,000 novel molecules. These compounds were then subjected to structure-based high-throughput virtual screening and molecular dynamics (MD) simulations to identify potential novel inhibitors of MmpL3. The binding affinity was obtained by docking the above molecules at the SQ109 binding site in MmPL3, followed by pharmacokinetics and toxicity, which were used to reduce the chemical space. Finally, five ligands were subjected to 100 ns MD simulations to investigate the binding energetics of inhibitors to MmpL3. These compounds demonstrated stable binding and favorable drug-like properties, indicating that they could serve as potential novel inhibitors of MmpL3 for Mtb.