A Combined Transmembrane Topology and Signal Peptide Prediction Method. Käll, L., Krogh, A., & Sonnhammer, E. L. L Journal of Molecular Biology, 338(5):1027–1036, May, 2004. ![A Combined Transmembrane Topology and Signal Peptide Prediction Method [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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 http://phobius.cgb.ki.se/ as well as at http://phobius.binf.ku.dk/
@article{kall_combined_2004,
title = {A {Combined} {Transmembrane} {Topology} and {Signal} {Peptide} {Prediction} {Method}},
volume = {338},
issn = {0022-2836},
url = {http://www.sciencedirect.com/science/article/pii/S0022283604002943},
doi = {10.1016/j.jmb.2004.03.016},
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 http://phobius.cgb.ki.se/ as well as at http://phobius.binf.ku.dk/},
number = {5},
urldate = {2017-11-06TZ},
journal = {Journal of Molecular Biology},
author = {Käll, Lukas and Krogh, Anders and Sonnhammer, Erik L. L},
month = may,
year = {2004},
keywords = {hidden Markov model, machine learning, signal peptide, topology prediction, transmembrane protein},
pages = {1027--1036}
}
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