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With the increase in immunocompromised patients in the recent years, fungal infections have emerged as new and serious threat in hospitals. This, and the insufficiency of current antifungal therapies alongside their toxic effects on patients, has led to the increased interest in seeking new antifungal peptides. In the present study, we have developed a prediction method for screening of antifungal peptides. For this, we have chosen Chou's pseudo amino acid composition (PseAAC) to translate peptide sequences into numeric values. Thus, the SVM classifier was performed for binomial classification of antifungal peptides. The performance of the classifier was evaluated via ten-fold cross-validation and an independent dataset. For further validation of the model developed, 22 P24-derived peptides were predicted using the classifier and in vitro assays were performed on the three peptides with the highest prediction score. The results showed that the PseAAC [Formula: see text] SVM method is able to predict AFPs with ACC of 94.76%. In vitro results also validate the SEN and SPC of the classifier. The results suggest that the computational approach used in this study is highly efficient for prediction of antifungal peptides, which can save time and money in AFP screening and synthesis of novel peptides.

Using computational approaches, especially data mining techniques, we can develop a precise prediction model for antifungal peptides. The model developed in this study using SVM can be considered a powerful tool for the prediction of antifungal peptides, which can be the first step in synthesis and discovery of novel fungi targeting agents.

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