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Abstract
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