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

Abstract

Unlike all-helices membrane proteins, β-barrel membrane proteins can not be successfully discriminated from other proteins, especially from all-β soluble proteins. This paper performs an analysis on the amino acid composition in membrane parts of 12 β-barrel membrane proteins versus β-strands of 79 all-β 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 β-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 β-barrel membrane proteins is presented based on the linear classifier coupled with predicted secondary structure. Applied to 241 β-barrel membrane proteins and 3855 soluble proteins with various structures, the method achieves 85.48% (206/241) sensitivity and 92.53% specificity (3567/3855).