Mousavizadegan Maryam, Mohabatkar Hassan
Department of Biotechnology, Faculty of Advanced Sciences and Technologies, University of Isfahan, Isfahan, Iran.
J Bioinform Comput Biol. 2018 Aug;16(4):1850016. doi: 10.1142/S0219720018500166. Epub 2018 May 29.
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 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.
近年来，随着免疫功能低下患者数量的增加，真菌感染已成为医院中新出现的严重威胁。此外，当前抗真菌疗法存在不足，且对患者有副作用，这使得人们对寻找新的抗真菌肽的兴趣日益浓厚。在本研究中，我们开发了一种筛选抗真菌肽的预测方法。为此，我们选择了周的伪氨基酸组成（PseAAC）将肽序列转化为数值。因此，使用支持向量机（SVM）分类器对抗真菌肽进行二项式分类。通过十折交叉验证和一个独立数据集对分类器的性能进行评估。为了进一步验证所开发的模型，使用该分类器预测了22种P24衍生肽，并对预测得分最高的三种肽进行了体外试验。结果表明，PseAAC-SVM方法能够以94.76%的准确率预测抗真菌肽。体外结果也验证了分类器的敏感性（SEN）和特异性（SPC）。结果表明，本研究中使用的计算方法在抗真菌肽预测方面非常高效，可在抗真菌肽筛选和新型肽合成中节省时间和金钱。
