Amino Acids

, Volume 38, Issue 3, pp 915–921 | Cite as

Recognition of β-hairpin motifs in proteins by using the composite vector

Original Article
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Abstract

A composite vector method for predicting β-hairpin motifs in proteins is proposed by combining the score of matrix, increment of diversity, the value of distance and auto-correlation information to express the information of sequence. The prediction is based on analysis of data from 3,088 non-homologous protein chains including 6,035 β-hairpin motifs and 2,738 non-β-hairpin motifs. The overall accuracy of prediction and Matthew’s correlation coefficient are 83.1% and 0.59, respectively. In addition, by using the same methods, the accuracy of 80.7% and Matthew’s correlation coefficient of 0.61 are obtained for other dataset with 2,878 non-homologous protein chains, which contains 4,884 β-hairpin motifs and 4,310 non-β-hairpin motifs. Better results are also obtained in the prediction of the β-hairpin motifs of proteins by analysis of the CASP6 dataset.

Keywords

β-Hairpin Scoring matrix Increment of diversity Auto-correlation function 
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Notes

Acknowledgments

This work was supported by National Natural Science Foundation of China (30560039) and Project for Excellent Subject-directors of Inner Mongolia Autonomous Region.

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Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  1. 1.College of SciencesInner Mongolia University of TechnologyHohhotPeople’s Republic of China
  2. 2.Laboratory of Theoretical Biophysics, College of Physical Science and TechnologyInner Mongolia UniversityHohhotPeople’s Republic of China

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