Comparative analysis of the existing methods for prediction of antifreeze proteins

Antifreeze proteins (AFPs) are found in different living organisms like plants, insects, and fish. AFPs avoid the formation of ice crystals in these organisms and make them able to survive in high cold regions. AFPs are widely deployed in metabolic genetic engineering, food technology, yogurt making, and cryopreservation. Considering the significance of AFPs, several predictors were proposed to identify AFPs. However, due to the unsatisfactory results of the predictors, more accurate predictors are critical. We carried out a thorough survey and summarized AFPs predictors that were developed for identification of AFPs. We provided a brief description of applied datasets, feature descriptors, model training classifiers, performance assessment parameters, and web servers. In this review article, the drawbacks of the proposed predictors and the best predictors were highlighted. We explained the future insights and more effective feature descriptors, appropriate feature selection techniques, and efficient classifiers that can enhance the performance of novel predictors for fast and accurate identification of AFPs.

Automated summary

Antifreeze proteins (AFPs) are found in various organisms and play a crucial role in preventing the formation of ice crystals. The development of accurate predictors for identifying AFPs is essential. This review article provides a comprehensive summary of existing AFP predictors, including their applied datasets, feature descriptors, model training classifiers, performance assessment parameters, and web servers. The drawbacks of current predictors are highlighted, and suggestions for future improvements, such as more effective feature descriptors and efficient classifiers, are discussed.