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A combined transmembrane topology and signal peptide prediction method

Käll, Lukas (author)
Ctr. for Genomics and Bioinformatics, Karolinska Institutet
Krogh, Anders (author)
Sonnhammer, Erik L. L. (author)
Ctr for Genomics and Bioinformatics, Karolinska Institutet (creator_code:org_t)
Elsevier BV, 2004
2004
English.
In: Journal of Molecular Biology. - : Elsevier BV. - 0022-2836 .- 1089-8638. ; 338:5, s. 1027-1036
  • Journal article (peer-reviewed)
Abstract Subject headings
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  • 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 trans' membrane 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 Honzo 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 http://phobius.cgb.ki.se/ as well as at http://phobius.binf.ku.dk/.

Subject headings

NATURVETENSKAP  -- Data- och informationsvetenskap -- Bioinformatik (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences -- Bioinformatics (hsv//eng)

Keyword

transmembrane protein
signal peptide
topology prediction
hidden Markov model
machine learning

Publication and Content Type

ref (subject category)
art (subject category)

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