Chinese Journal of Tissue Engineering Research ›› 2011, Vol. 15 ›› Issue (21): 3983-3986.doi: 10.3969/j.issn.1673-8225.2011.21.044

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Subcellular localization prediction of proteins containing fibronectin domains using collocation of amino acid pairs

Li Li-qi1, Zhang Yuan1, Zhou Yue1, Wang Kai-fa2   

  1. 1Department of Orthopedics, Xinqiao Hospital, Third Military Medical University of Chinese PLA, Chongqing   400037, China
    2Department of Computer, Third Military Medical University of Chinese PLA, Chongqing   400038, China
  • Received:2010-10-16 Revised:2010-11-18 Online:2011-05-21 Published:2011-05-21
  • Contact: Zhou Yue, Professor, Chief physician, Doctoral supervisor, Department of Orthopedics, Xinqiao Hospital, Third Military Medical University of Chinese PLA, Chongqing 400037, China lcq788@163.com
  • About author:Li Li-qi☆, Studying for doctorate, Physician, Department of Orthopedics, Xinqiao Hospital, Third Military Medical University of Chinese PLA, Chongqing 400037, China liliqi198610@163.com
  • Supported by:

    the National Natural Science Foundation of China, No. 30901512*

Abstract:

BACKGROUND: Proteins containing fibronectin domains play an important role in cell migration, adhesion, growth and differentiation and have been widely applied to a variety of new biological materials. Subcellular localization prediction of proteins containing fibronectin domains can promote protein function research and development of new biomaterials.
OBJECTIVE: To realize subcellular localization prediction of proteins containing fibronectin domains.
METHODS: A total of 80 human proteins were randomly selected from Uniprot database. The amino acid pairs for each protein were collocated to form 400 dimensional input feature vectors. The feature vectors were then trained and tested using support vector machine and k-nearest neighbor separately. The prediction quality was examined by the jackknife test.
RESULTS AND CONCLUSION: The prediction accuracy was 92.5% and 95% for support vector machine and k-nearest neighbor methods respectively. This suggests that support vector machine and k-nearest neighbor methods are of important significance for predicting subcellular localization of proteins containing fibronectin domains and contribute to functional research of such proteins and surface modification of new biomaterials.

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