The membrane protein type is an important feature in characterizing the overall
topological folding type of a protein or its domains therein. Many investigators have put their
efforts to the prediction of membrane protein type. Here, we propose a new approach, the
bootstrap aggregating method or bragging learner, to address this problem based on the protein
amino acid composition. As a demonstration, the benchmark dataset constructed by K.C. Chou
and D.W. Elrod (Proteins, 1999, 34, 137-153) was used to test the new method. The overall
success rate thus obtained by jackknife cross-validation was over 84%, indicating that the
bragging learner as presented in this paper holds a quite high potential in predicting the
attributes of proteins, or at least can play a complementary role to many existing algorithms in
this area. It is anticipated that the prediction quality can be further enhanced if the pseudo
amino acid composition (K.C. Chou, Proteins, 2001, 43, 246-255) can be effectively
incorporated into the current predictor. An online membrane protein type prediction web server
developed in our lab is available at http://chemdata.shu.edu.cn/protein/protein.jsp.
Keywords: Bagging, neural network, SVM, membrane protein types, jackknife
cross-validation.
