Using Support Vector Machine and Evolutionary Profiles to Predict Antifreeze Protein Sequences

International Journal of Molecular Sciences

Xiaowei Zhao¹  Zhiqiang Ma¹ 

[1] College of Computer Science and Information Technology, Northeast Normal University, 2555 Jingyue Street, Changchun 130117, China; E-Mail:

关键词: antifreeze proteins;    support vector machine;    position specific scoring matrix;    web sever;    evolutionary information;   

DOI  :  10.3390/ijms13022196

来源: mdpi

【 摘 要 】

Antifreeze proteins (AFPs) are ice-binding proteins. Accurate identification of new AFPs is important in understanding ice-protein interactions and creating novel ice-binding domains in other proteins. In this paper, an accurate method, called AFP_PSSM, has been developed for predicting antifreeze proteins using a support vector machine (SVM) and position specific scoring matrix (PSSM) profiles. This is the first study in which evolutionary information in the form of PSSM profiles has been successfully used for predicting antifreeze proteins. Tested by 10-fold cross validation and independent test, the accuracy of the proposed method reaches 82.67% for the training dataset and 93.01% for the testing dataset, respectively. These results indicate that our predictor is a useful tool for predicting antifreeze proteins. A web server (AFP_PSSM) that implements the proposed predictor is freely available.

【 授权许可】

CC BY   
© 2012 by the authors; licensee Molecular Diversity Preservation International, Basel, Switzerland.