MOTIVATION: Many important biological processes such as cell signaling, 
transport of membrane-impermeable molecules, cell-cell communication, cell 
recognition and cell adhesion are mediated by membrane proteins. Unfortunately, 
as these proteins are not water soluble, it is extremely hard to experimentally 
determine their structure. Therefore, improved methods for predicting the 
structure of these proteins are vital in biological research. In order to 
improve transmembrane topology prediction, we evaluate the combined use of both 
integrated signal peptide prediction and evolutionary information in a single 
algorithm.
RESULTS: A new method (MEMSAT3) for predicting transmembrane protein topology 
from sequence profiles is described and benchmarked with full cross-validation 
on a standard data set of 184 transmembrane proteins. The method is found to 
predict both the correct topology and the locations of transmembrane segments 
for 80% of the test set. This compares with accuracies of 62-72% for other 
popular methods on the same benchmark. By using a second neural network 
specifically to discriminate transmembrane from globular proteins, a very low 
overall false positive rate (0.5%) can also be achieved in detecting 
transmembrane proteins.
AVAILABILITY: An implementation of the described method is available both as a 
web server (http://www.psipred.net) and as downloadable source code from 
http://bioinf.cs.ucl.ac.uk/memsat. Both the server and source code files are 
free to non-commercial users. Benchmark and training data are also available 
from http://bioinf.cs.ucl.ac.uk/memsat.