Next Article in Journal
A Novel LMP1 Antibody Synergizes with Mitomycin C to Inhibit Nasopharyngeal Carcinoma Growth in Vivo Through Inducing Apoptosis and Downregulating Vascular Endothelial Growth Factor
Previous Article in Journal
Computational Studies of Difference in Binding Modes of Peptide and Non-Peptide Inhibitors to MDM2/MDMX Based on Molecular Dynamics Simulations
Article Menu

Export Article

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

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 Biochemistry, Molecular Biology and Biophysics)
View Full-Text   |   Download PDF [302 KB, uploaded 19 June 2014]   |  

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. View Full-Text
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 (CC BY 3.0).

Supplementary material

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

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.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Int. J. Mol. Sci. EISSN 1422-0067 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top