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Identification of Phage Virion Proteins by Using the g-gap Tripeptide Composition

[ Vol. 16 , Issue. 4 ]

Author(s):

Liangwei Yang, Hui Gao*, Zhen Liu and Lixia Tang   Pages 332 - 339 ( 8 )

Abstract:


Phages are widely distributed in locations populated by bacterial hosts. Phage proteins can be divided into two main categories, that is, virion and non-virion proteins with different functions. In practice, people mainly use phage virion proteins to clarify the lysis mechanism of bacterial cells and develop new antibacterial drugs. Accurate identification of phage virion proteins is therefore essential to understanding the phage lysis mechanism. Although some computational methods have been focused on identifying virion proteins, the result is not satisfying which gives more room for improvement. In this study, a new sequence-based method was proposed to identify phage virion proteins using g-gap tripeptide composition. In this approach, the protein features were firstly extracted from the ggap tripeptide composition. Subsequently, we obtained an optimal feature subset by performing incremental feature selection (IFS) with information gain. Finally, the support vector machine (SVM) was used as the classifier to discriminate virion proteins from non-virion proteins. In 10-fold crossvalidation test, our proposed method achieved an accuracy of 97.40% with AUC of 0.9958, which outperforms state-of-the-art methods. The result reveals that our proposed method could be a promising method in the work of phage virion proteins identification.

Keywords:

Phage virion proteins, g-gap tripeptide composition, SVM, IFS, information gain, 10-fold cross validation.

Affiliation:

School of Computer Science and Engineering, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, School of Computer Science and Engineering, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, School of Computer Science and Engineering, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054

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