Living organisms including fishes, microbes, and animals can live in extremely cold weather. To stay alive in cold environments, these species generate antifreeze proteins (AFPs), also referred to as ice binding proteins. Moreover, AFPs are extensively utilized in many important fields including medical, agricultural, industrial, and biotechnological. Several predictors were constructed to identify AFPs. However, due to the sequence and structural heterogeneity of AFPs, correct identification is still a challenging task. In this study, deals with several bio-informatical or computational tools which have been proposed for prediction of AFPs more precisely can predict AFPs more accurately and can participate in a significant role in medical, agricultural, industrial, and biotechnological fields.

Key words: Antifreeze protein, bioinformatics, physicochemical properties, homology modeling