In-silico analysis, molecular docking and dynamic simulation of anti-cancer metabolites derived from Bacillus velezensis

The complex nature of cancer poses significant challenges in deciphering its underlying molecular mechanisms. To facilitate advancements in biological research and therapeutic development, computational models have emerged as powerful tools for elucidating cancer biology. In the present study, in-silico approaches were employed to identify metabolites from Bacillus velezensis strain with anticancer potential by targeting protein molecules associated with malignancies. Genome mining revealed biosynthetic potential of the strain, and target prediction was conducted using BindingDB (p≥0.7). Pathway enrichment analysis was performed using STRING and KEGG databases, which showed involvement of compounds in modulation of multiple pathways involved in cancer. Molecular docking simulations showed that fengycin, a secondary metabolite derived from B. velezensis , exhibited strongest binding affinity toward MAPK9, with a docking energy of −17.1 kcal/mol. This interaction was validated through Molecular Dynamics (MD) simulation. The mean RMSD values for the APO and MAPK9 complexes were 0.29 ± 0.03 nm and 0.32 ± 0.03 nm, respectively, confirming structural stability throughout the simulation. The mean radius of gyration (Rg) values were 2.33 ± 0.02 nm for APO and 2.35 ± 0.02 nm for MAPK9, indicating compactness of the protein-ligand complex. RMSF values were recorded as 0.15 ± 0.09 nm for APO and 0.14 ± 0.07 nm for MAPK9, reflecting minimal residue-level fluctuations. Solvent Accessible Surface Area (SASA) remained stable at 177.63 ± 5.8 nm 2 for APO and 179.67 ± 3.4 nm 2 for MAPK9, suggesting no significant conformational alterations. Additionally, MAPK9 displayed a van der Waals energy of −493.903 ± 17.608 kJ/mol, electrostatic energy of −118.975 ± 26.098 kJ/mol, polar solvation energy of 480.496 ± 21.551 kJ/mol, and a final binding energy of −189.266 ± 33.873 kJ/mol, indicating a strong and stable interaction with the drug candidate. Overall, metabolites derived from B. velezensis , particularly fengycin, demonstrated potential anticancer attributes and warrant further experimental validation. • Network pharmacology predicted anti-cancer metabolites from Bacillus velezensis by simulating interactions with cancer targets, aiding candidate prioritization. • Molecular docking of derived compounds ( B. velezensis ) targeted cancer pathways. • Genomic and metagenomic tools identify anti-cancer genes and microbial communities for novel therapies.