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A combined transmem...
A combined transmembrane topology and signal peptide prediction method
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- Käll, Lukas (author)
- Ctr. for Genomics and Bioinformatics, Karolinska Institutet
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Krogh, Anders (author)
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Sonnhammer, Erik L. L. (author)
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Ctr for Genomics and Bioinformatics, Karolinska Institutet (creator_code:org_t)
- Elsevier BV, 2004
- 2004
- English.
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In: Journal of Molecular Biology. - : Elsevier BV. - 0022-2836 .- 1089-8638. ; 338:5, s. 1027-1036
- Related links:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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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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