An inherent problem in transmembrane protein topology prediction and signal 
peptide prediction is the high similarity between the hydrophobic regions of a 
transmembrane helix and that of a signal peptide, leading to cross-reaction 
between the two types of predictions. To improve predictions further, it is 
therefore important to make a predictor that aims to discriminate between the 
two classes. In addition, topology information can be gained when successfully 
predicting a signal peptide leading a transmembrane protein since it dictates 
that the N terminus of the mature protein must be on the non-cytoplasmic side of 
the membrane. Here, we present Phobius, a combined transmembrane protein 
topology and signal peptide predictor. The predictor is based on a hidden Markov 
model (HMM) that models the different sequence regions of a signal peptide and 
the different regions of a transmembrane protein in a series of interconnected 
states. Training was done on a newly assembled and curated dataset. Compared to 
TMHMM and SignalP, errors coming from cross-prediction between transmembrane 
segments and signal peptides were reduced substantially by Phobius. False 
classifications of signal peptides were reduced from 26.1% to 3.9% and false 
classifications of transmembrane helices were reduced from 19.0% to 7.7%. 
Phobius was applied to the proteomes of Homo sapiens and Escherichia coli. Here 
we also noted a drastic reduction of false classifications compared to 
TMHMM/SignalP, suggesting that Phobius is well suited for whole-genome 
annotation of signal peptides and transmembrane regions. The method is available 
at as well as at