This article describes a method developed for predicting transmembrane 
beta-barrel regions in membrane proteins using machine learning techniques: 
artificial neural network (ANN) and support vector machine (SVM). The ANN used 
in this study is a feed-forward neural network with a standard back-propagation 
training algorithm. The accuracy of the ANN-based method improved significantly, 
from 70.4% to 80.5%, when evolutionary information was added to a single 
sequence as a multiple sequence alignment obtained from PSI-BLAST. We have also 
developed an SVM-based method using a primary sequence as input and achieved an 
accuracy of 77.4%. The SVM model was modified by adding 36 physicochemical 
parameters to the amino acid sequence information. Finally, ANN- and SVM-based 
methods were combined to utilize the full potential of both techniques. The 
accuracy and Matthews correlation coefficient (MCC) value of SVM, ANN, and 
combined method are 78.5%, 80.5%, and 81.8%, and 0.55, 0.63, and 0.64, 
respectively. These methods were trained and tested on a nonredundant data set 
of 16 proteins, and performance was evaluated using "leave one out 
cross-validation" (LOOCV). Based on this study, we have developed a Web server, 
TBBPred, for predicting transmembrane beta-barrel regions in proteins (available 
at http://www.imtech.res.in/raghava/tbbpred).