TargetFreeze: Identifying Antifreeze Proteins via a Combination of Weights using Sequence Evolutionary Information and Pseudo Amino Acid Composition

TargetFreeze: Identifying Antifreeze Proteins via a Combination of Weights using Sequence... Antifreeze proteins (AFPs) are indispensable for living organisms to survive in an extremely cold environment and have a variety of potential biotechnological applications. The accurate prediction of antifreeze proteins has become an important issue and is urgently needed. Although considerable progress has been made, AFP prediction is still a challenging problem due to the diversity of species. In this study, we proposed a new sequence-based AFP predictor, called TargetFreeze. TargetFreeze utilizes an enhanced feature representation method that weightedly combines multiple protein features and takes the powerful support vector machine as the prediction engine. Computer experiments on benchmark datasets demonstrate the superiority of the proposed TargetFreeze over most recently released AFP predictors. We also implemented a user-friendly web server, which is openly accessible for academic use and is available at http://csbio.njust.edu.cn/bioinf/TargetFreeze . TargetFreeze supplements existing AFP predictors and will have potential applications in AFP-related biotechnology fields. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The Journal of Membrane Biology Springer Journals

TargetFreeze: Identifying Antifreeze Proteins via a Combination of Weights using Sequence Evolutionary Information and Pseudo Amino Acid Composition

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Publisher
Springer US
Copyright
Copyright © 2015 by Springer Science+Business Media New York
Subject
Life Sciences; Biochemistry, general; Human Physiology
ISSN
0022-2631
eISSN
1432-1424
D.O.I.
10.1007/s00232-015-9811-z
Publisher site
See Article on Publisher Site

Abstract

Antifreeze proteins (AFPs) are indispensable for living organisms to survive in an extremely cold environment and have a variety of potential biotechnological applications. The accurate prediction of antifreeze proteins has become an important issue and is urgently needed. Although considerable progress has been made, AFP prediction is still a challenging problem due to the diversity of species. In this study, we proposed a new sequence-based AFP predictor, called TargetFreeze. TargetFreeze utilizes an enhanced feature representation method that weightedly combines multiple protein features and takes the powerful support vector machine as the prediction engine. Computer experiments on benchmark datasets demonstrate the superiority of the proposed TargetFreeze over most recently released AFP predictors. We also implemented a user-friendly web server, which is openly accessible for academic use and is available at http://csbio.njust.edu.cn/bioinf/TargetFreeze . TargetFreeze supplements existing AFP predictors and will have potential applications in AFP-related biotechnology fields.

Journal

The Journal of Membrane BiologySpringer Journals

Published: Jun 10, 2015

References

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