Free energy perturbation and machine learning-assisted identification of novel molecules for the Mycobacterium tuberculosis KasA protein: a fragment-based drug design approach.

KasA is an essential enzyme of Mycobacterium tuberculosis (Mtb). It plays a critical role in synthesizing long-chain mycolic acids, the major components of the bacterial cell wall, by regulating the FAS-I and FAS-II fatty acid synthesis pathways. Inhibiting KasA offers a promising strategy for treating tuberculosis (TB). This study used fragment-based drug design (FBDD) to design novel small molecules targeting KasA. Fragments from known KasA inhibitors were generated with the MacFrag tool and then combined with Fragmenstein to create potential hit compounds. A multi-tiered molecular docking approach was used to evaluate their binding affinity and interactions with KasA. Selected candidates underwent pharmacokinetic analysis and molecular dynamics (MD) simulations. Five promising molecules, namely KasA_FB1, KasA_FB2, KasA_FB3, KasA_FB4 and KasA_FB5, were identified. Their molecular docking binding energies were - 7.80, - 8.30, - 9.00, - 7.80, and - 9.00 kcal/mol, respectively, all superior to the reference co-crystal ligand TLM (- 7.20 kcal/mol). MD simulations showed that their dynamic stability was comparable to or better than TLM. MM-GBSA and free energy perturbation (FEP) analyses further confirmed their superior binding affinity for KasA. These compounds represent promising candidates for the development of new anti-TB drugs targeting KasA and warrant experimental validation.