Identification of β-barrel membrane proteins based on amino acid composition properties and predicted secondary structure

  • Authors:

  • Qi Liu;Yisheng Zhu;Baohua Wang;Yixue Li

  • Affiliations:

  • Department of Biomedical Engineering, Shanghai Jiaotong University, P.O. Box 134, 1954 Huashan Road, Shanghai 200030, People's Republic of China and Bioinformation Center, Shanghai Institutes for ...;Department of Biomedical Engineering, Shanghai Jiaotong University, P.O. Box 134, 1954 Huashan Road, Shanghai 200030, People's Republic of China;Department of Biomedical Engineering, Shanghai Jiaotong University, P.O. Box 134, 1954 Huashan Road, Shanghai 200030, People's Republic of China;Bioinformation Center, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, People's Republic of China

  • Venue:
  • Computational Biology and Chemistry
  • Year:
  • 2003

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Abstract

Unlike all-helices membrane proteins, @b-barrel membrane proteins can not be successfully discriminated from other proteins, especially from all-@b soluble proteins. This paper performs an analysis on the amino acid composition in membrane parts of 12 @b-barrel membrane proteins versus @b-strands of 79 all-@b soluble proteins. The average and variance of the amino acid composition in these two classes are calculated. Amino acids such as Gly, Asn, Val that are most likely associated with classification are selected based on Fishers discriminant ratio. A linear classifier built with these selected amino acids composition in observed @b-strands achieves 100% classification accuracy for 12 membrane proteins and 79 soluble proteins in a four-fold cross-validation experiment. Since at present the accuracy of secondary structure prediction is quite high, a promising method to identify @b-barrel membrane proteins is presented based on the linear classifier coupled with predicted secondary structure. Applied to 241 @b-barrel membrane proteins and 3855 soluble proteins with various structures, the method achieves 85.48% (206/241) sensitivity and 92.53% specificity (3567/3855).