Many lines of evidences have indicated that computational approaches can provide useful information for both drug discovery and basic research in a timely manner [15] , such as protein subcellular location prediction [16, 17] , structural bioinformatics [18] , identification of proteases and their types [19] , identification of membrane proteins and their types [20] , molecular docking [21] [22] [23] , identification of enzymes and their functional classes [24] , and signal peptide prediction [25, 26] . Up until now, there are few studies using computational approaches to discriminate AFPs and non-AFPs. Kandaswamy et al. [27] investigated this problem using the predictor of Random Forest. That is the first and the only method utilizing machine learning technique to deal with the prediction of AFPs. With the model AFP-Pred, they obtained 81.33% accuracy from training and 83.38% from testing. Although high accuracy has been achieved, the problem is worthy of further investigation because the performance of the aforementioned method is still not fully satisfactory and they do not provide an online web server for predicting antifreeze proteins.