Identification of thermophilic proteins by incorporating evolutionary and acid dissociation information into Chou's general pseudo amino acid composition

Thermophilic proteins can thrive stalely at the high temperatures. Identification of thermophilic protein could be helpful to learn the function of protein. Automated prediction of thermophilic protein is an important tool for genome annotation. In this work, a powerful predictor is proposed by combining amino acid composition, evolutionary information, and acid dissociation constant. The overall prediction accuracy of 93.53% was obtained for using the algorithm of support vector machine. In order to check the performance of our method, two low-similarity independent testing datasets are used to test the proposed method. Comparisons with other methods show that the prediction results were better than other existing methods in literature. This indicates that our approach was effective to predict thermophilic proteins.