BACKGROUND: Alpha-helical transmembrane (TM) proteins are involved in a wide 
range of important biological processes such as cell signaling, transport of 
membrane-impermeable molecules, cell-cell communication, cell recognition and 
cell adhesion. Many are also prime drug targets, and it has been estimated that 
more than half of all drugs currently on the market target membrane proteins. 
However, due to the experimental difficulties involved in obtaining high quality 
crystals, this class of protein is severely under-represented in structural 
databases. In the absence of structural data, sequence-based prediction methods 
allow TM protein topology to be investigated.
RESULTS: We present a support vector machine-based (SVM) TM protein topology 
predictor that integrates both signal peptide and re-entrant helix prediction, 
benchmarked with full cross-validation on a novel data set of 131 sequences with 
known crystal structures. The method achieves topology prediction accuracy of 
89%, while signal peptides and re-entrant helices are predicted with 93% and 44% 
accuracy respectively. An additional SVM trained to discriminate between 
globular and TM proteins detected zero false positives, with a low false 
negative rate of 0.4%. We present the results of applying these tools to a 
number of complete genomes. Source code, data sets and a web server are freely 
available from http://bioinf.cs.ucl.ac.uk/psipred/.
CONCLUSION: The high accuracy of TM topology prediction which includes detection 
of both signal peptides and re-entrant helices, combined with the ability to 
effectively discriminate between TM and globular proteins, make this method 
ideally suited to whole genome annotation of alpha-helical transmembrane 
proteins.