Molecular dynamics modeling of the Candida albicans membrane for the discovery of membrane-binding antifungal peptides.

Fungal infections caused by Candida albicans (C. albicans) pose a significant clinical challenge, especially due to the rise of multidrug-resistant strains. Antifungal peptides (AFPs) represent promising alternatives because of their high efficacy, low toxicity, and reduced risk of resistance. In this study, we integrated computational modeling and experimental validation to identify novel AFPs targeting the fungal membrane. Four candidate peptides-HIV-F9170 (GWEALKYLWNLLQYW), PinA (WRWWKWWK), PinB (WPTKWWK), and XYP1 (KIKWFKAMKSIAKFIAKDQLKKHL)-were evaluated using AlphaFold2 structure prediction, molecular dynamics (MD) simulations, and in vitro antifungal assays. MD simulations revealed that PinA and XYP1 exhibited strong binding and stable interactions with a model C. albicans membrane. Correspondingly, in vitro assays confirmed that these two peptides effectively inhibited fungal growth without cytotoxicity toward HEK-293 cells. Our findings provide a mechanistic basis for peptide antifungal activity and identify promising candidates for further therapeutic development.