In this study, we focus on developing a new antifreeze protein predictor by seeking a more informative encoding scheme. After a preliminary evaluation of different encoding schemes, we found that the evolutionary information in the form of PSSM profiles is suitable for representing the antifreeze protein sequence. Then a predictor called AFP_PSSM is established using the feature PSSM-400 as the input of support vector machine (SVM). AFP_PSSM yields 82.67% accuracy from training dataset and 93.01% accuracy from test dataset. This indicates that our predictor is very promising and may at least play an important complementary role to existing methods. The proposed predictor is freely available at the web server AFP_PSSM [28] . For a query protein sequence of 500 amino acids, it will take about 20 s for the web server to yield the predicted result; the longer the sequence is, the more time it needs.