Candida auris is a pathogenic fungus associated with high-mortality infections and forms resilient biofilms on various surfaces. In this study, we introduced a novel antifungal strategy against C. auris by integrating an AI-powered protein design tool, ProteinMPNN, with classical molecular dynamics (MD) simulations to design artificial proteins from a miniprotein library. This combined approach accelerated and enhanced the design process, enabling the rapid development of effective miniprotein inhibitors specifically targeting C. auris biofilm formation. The miniproteins developed in this study exhibited potent inhibitory effects on C. auris biofilms, representing a significant advancement in antifungal therapy. Notably, the combined application of these miniproteins enhanced suppression of biofilm formation. These findings highlight not only the strong therapeutic potential of these designed miniproteins but also the power of combining AI-driven protein design with MD simulations to advance biomedical research.
Keywords: AI-driven protein engineering; Candida auris; Proteindesign; antifungal.