Predicting the Types of J-Proteins Using Clustered Amino Acids
Joint Authors
Zuo, Yongchun
Lin, Hao
Chen, Wei
Feng, Peng-Mian
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-04-02
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
J-proteins are molecular chaperones and present in a wide variety of organisms from prokaryote to eukaryote.
Based on their domain organizations, J-proteins can be classified into 4 types, that is, Type I, Type II, Type III, and Type IV.
Different types of J-proteins play distinct roles in influencing cancer properties and cell death.
Thus, reliably annotating the types of J-proteins is essential to better understand their molecular functions.
In the present work, a support vector machine based method was developed to identify the types of J-proteins using the tripeptide composition of reduced amino acid alphabet.
In the jackknife cross-validation, the maximum overall accuracy of 94% was achieved on a stringent benchmark dataset.
We also analyzed the amino acid compositions by using analysis of variance and found the distinct distributions of amino acids in each family of the J-proteins.
To enhance the value of the practical applications of the proposed model, an online web server was developed and can be freely accessed.
American Psychological Association (APA)
Feng, Peng-Mian& Lin, Hao& Chen, Wei& Zuo, Yongchun. 2014. Predicting the Types of J-Proteins Using Clustered Amino Acids. BioMed Research International،Vol. 2014, no. 2014, pp.1-8.
https://search.emarefa.net/detail/BIM-509512
Modern Language Association (MLA)
Feng, Peng-Mian…[et al.]. Predicting the Types of J-Proteins Using Clustered Amino Acids. BioMed Research International No. 2014 (2014), pp.1-8.
https://search.emarefa.net/detail/BIM-509512
American Medical Association (AMA)
Feng, Peng-Mian& Lin, Hao& Chen, Wei& Zuo, Yongchun. Predicting the Types of J-Proteins Using Clustered Amino Acids. BioMed Research International. 2014. Vol. 2014, no. 2014, pp.1-8.
https://search.emarefa.net/detail/BIM-509512
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-509512