American foulbrood (AFB), caused by Paenibacillus larvae, remains one of the most devastating bacterial diseases affecting honeybee populations worldwide, posing a serious threat to pollination services and global food security.
Current antibiotic-based control strategies are limited by inefficacy against persistent spores and the emergence of antimicrobial resistance, highlighting the urgent need for alternative therapeutic approaches.
In this study, we developed an integrated in silico pipeline combining quantitative structure–activity relationship (QSAR) modeling, virtual screening, molecular docking, ADMET profiling, and molecular dynamics (MD) simulations to identify novel phytochemical inhibitors targeting β-ketoacyl-acyl carrier protein synthase III (FabH), a key enzyme in bacterial fatty acid biosynthesis absent in eukaryotic hosts.
Density functional theory (DFT) calculations further confirmed the electronic stability and reactivity profile of hinokiflavone, supporting its favorable interaction potential with the target protein.
These results provide theoretical insights that may guide future experimental investigations and the development of alternative strategies for controlling P. larvae infections in apiculture.
American foulbrood (AFB), caused by Paenibacillus larvae, remains one of the most devastating bacterial diseases affecting honeybee populations worldwide, posing a serious threat to pollination services and global food security. Current antibiotic-based control strategies are limited by inefficacy against persistent spores and the emergence of antimicrobial resistance, highlighting the urgent need for alternative therapeutic approaches. In this study, we developed an integrated in silico pipeline combining quantitative structure–activity relationship (QSAR) modeling, virtual screening, molecular docking, ADMET profiling, and molecular dynamics (MD) simulations to identify novel phytochemical inhibitors targeting β-ketoacyl-acyl carrier protein synthase III (FabH), a key enzyme in bacterial fatty acid biosynthesis absent in eukaryotic hosts. QSAR models built from curated ChEMBL datasets (n = 360 compounds) demonstrated satisfactory predictive performance (R² = 0.643, RMSE = 0.587 pIC₅₀ units, AUC = 0.780), with Y-randomization (100 permutations) and applicability domain analysis (Williams plot) confirming model robustness and the absence of chance correlations, supporting their suitability for virtual screening applications. Virtual screening of an in-house phytochemical library identified hinokiflavone as the most promising candidate, exhibiting strong binding affinity toward FabH (− 9.398 kcal/mol) and forming a stable network of hydrogen bonding and hydrophobic interactions within the catalytic pocket. Density functional theory (DFT) calculations further confirmed the electronic stability and reactivity profile of hinokiflavone, supporting its favorable interaction potential with the target protein. ADMET analysis revealed an acceptable pharmacokinetic profile with notable limitations in solubility and bioavailability. Subsequent 200 ns molecular dynamics simulations confirmed the stability of the FabH–hinokiflavone complex, as evidenced by low structural deviation (RMSD ≈ 0.23 nm), limited residue fluctuations, stable radius of gyration, and consistent solvent-accessible surface area, indicating a well-maintained compact structure and stable binding interface. Overall, hinokiflavone emerges as a computationally identified potential FabH-binding compound. These results provide theoretical insights that may guide future experimental investigations and the development of alternative strategies for controlling P. larvae infections in apiculture.
