Data-driven optimization of siderophore fermentation by Bacillus altitudinis AS19 and investigation of AS19’s antifungal properties against Candida albicans

Candida albicans is a common opportunistic pathogenic fungus capable of causing severe infections when host immunity is compromised or when the normal microbial flora is disrupted. The widespread use of antifungal agents has led to the increasing emergence of drug resistance, underscoring the urgent need for novel antifungal strategies. Bacillus altitudinis AS19 has been found to produce a substance with specific antimicrobial activity against C. albicans under iron-deficient conditions. In this study, the fermentation medium and culture conditions for siderophore production by AS19 were optimized using response surface methodology (RSM) and an artificial neural network-genetic algorithm (ANN-GA) model. The ANN-GA model demonstrated superior optimization performance compared with RSM. The optimal conditions were pH 6.363, sucrose 29.730 g/L, and KH ₂ PO ₄ 0.185 g/L, under which the yield increased more than twofold compared with the pre-optimization level of 26.53%. To further investigate the antimicrobial mechanism, molecular docking and dynamics simulations were employed to virtually screen siderophores with anti- C . albicans activity and identify their potential targets. Among the candidates, Nocardamin Glucuronide was prioritized as the top hit, and the presence of desferrioxamine-type siderophores produced by AS19 was subsequently confirmed by LC-MS, validating the in silico predictions. In addition, in vitro assays were performed to determine the minimum inhibitory concentration, minimum fungicidal concentration, and time-kill kinetics of the crude siderophore extract. The time-kill curve clearly demonstrated its potent antimicrobial activity against C. albicans . Overall, this study provides both a theoretical basis and experimental evidence for the development of novel siderophore-based antimicrobial agents targeting C. albicans .