Harnessing Sequence Embedding and Ensemble Learning to Identify Antifungal Peptides with Low Hemolytic Risk

The increasing prevalence of fungal infections represents a growing threat to human health, driven in part by the misuse of antibiotics and the rising incidence of resistance to conventional antifungal agents. Antifungal peptides (AFPs) have emerged as promising alternatives due to their diverse mechanisms of action and their relatively low propensity to develop resistance. To facilitate the systematic discovery of AFPs, we developed AI4AFP. This computational framework integrates curated antifungal peptide resources with advanced machine learning approaches to predict antifungal potential directly from peptide sequences. Using a comprehensive data set, we constructed a seven-model ensemble that combines multiple sequence encoding strategies, including ProtBERT-BFD, PC6, and Doc2Vec, with diverse learning algorithms, including random forests, support vector machines, convolutional neural networks, and fine-tuned BERT models. This ensemble demonstrated robust performance on an independent test set, achieving 0.94 in accuracy and 0.89 in Matthews correlation coefficient, outperforming existing AFP prediction methods. Importantly, the predicted AFP score is intended to reflect the general antifungal potential rather than species-specific potency. Experimental validation against representative fungal pathogens, including Candida albicans, Candida glabrata, and Cryptococcus neoformans, revealed that peptides with high predicted AFP scores exhibited context-dependent antifungal activity. Several candidates displayed pronounced inhibitory effects against specific species, despite limited activity against others, highlighting the inherent species dependence of antifungal efficacy and supporting the role of AI4AFP as a prioritization tool rather than a species-specific predictor. To complement antifungal prediction, we further developed a hemolysis classifier that incorporates both peptide sequence and applied concentration as continuous inputs, enabling explicit modeling of the dose-dependent nature of hemolytic toxicity. Experimental determination of the minimum concentration inducing 10% hemolysis (MHC₁₀) provided an empirical safety reference, enabling antifungal activity to be interpreted alongside concentration-dependent toxicity. All models and validation results are implemented on a user-friendly web server, AI4AFP (https://axp.iis.sinica.edu.tw/AI4AFP), providing an accessible platform for the discovery and prioritization of antifungal peptides, with consideration of both efficacy and safety.