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Int. J. Mol. Sci. 2012, 13(2), 2196-2207; doi:10.3390/ijms13022196
Article

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

1,2,3
, 1,2,3,*  and 1,2,*
1 College of Computer Science and Information Technology, Northeast Normal University, 2555 Jingyue Street, Changchun 130117, China 2 Key Laboratory of Intelligent Information Processing of Jilin Universities, Northeast Normal University, Changchun 130117, China 3 College of Life Science, Northeast Normal University, 5268 Renmin Street, Changchun 130024, China
* Authors to whom correspondence should be addressed.
Received: 5 January 2012 / Revised: 29 January 2012 / Accepted: 29 January 2012 / Published: 17 February 2012
(This article belongs to the Section Bioinorganic Chemistry)
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Abstract

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.
Keywords: antifreeze proteins; support vector machine; position specific scoring matrix; web sever; evolutionary information antifreeze proteins; support vector machine; position specific scoring matrix; web sever; evolutionary information
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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MDPI and ACS Style

Zhao, X.; Ma, Z.; Yin, M. Using Support Vector Machine and Evolutionary Profiles to Predict Antifreeze Protein Sequences. Int. J. Mol. Sci. 2012, 13, 2196-2207.

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