BACKGROUND: Beta-barrel transmembrane (bbtm) proteins are a functionally 
important and diverse group of proteins expressed in the outer membranes of 
bacteria (both gram negative and acid fast gram positive), mitochondria and 
chloroplasts. Despite recent publications describing reasonable levels of 
accuracy for discriminating between bbtm proteins and other proteins, screening 
of entire genomes remains troublesome as these molecules only constitute a small 
fraction of the sequences screened. Therefore, novel methods are still required 
capable of detecting new families of bbtm protein in diverse genomes.
RESULTS: We present TMB-Hunt, a program that uses a k-Nearest Neighbour (k-NN) 
algorithm to discriminate between bbtm and non-bbtm proteins on the basis of 
their amino acid composition. By including differentially weighted amino acids, 
evolutionary information and by calibrating the scoring, an accuracy of 92.5% 
was achieved, with 91% sensitivity and 93.8% positive predictive value (PPV), 
using a rigorous cross-validation procedure. A major advantage of this approach 
is that because it does not rely on beta-strand detection, it does not require 
resolved structures and thus larger, more representative, training sets could be 
used. It is therefore believed that this approach will be invaluable in 
complementing other, physicochemical and homology based methods. This was 
demonstrated by the correct reassignment of a number of proteins which other 
predictors failed to classify. We have used the algorithm to screen several 
genomes and have discussed our findings.
CONCLUSION: TMB-Hunt achieves a prediction accuracy level better than other 
approaches published to date. Results were significantly enhanced by use of 
evolutionary information and a system for calibrating k-NN scoring. Because the 
program uses a distinct approach to that of other discriminators and thus 
suffers different liabilities, we believe it will make a significant 
contribution to the development of a consensus approach for bbtm protein 
detection.