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Combined prediction of Tat and Sec signal peptides with hidden Markov models

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Combined prediction of Tat and Sec signal peptides with hidden Markov models. / Bagos, Pantelis G; Nikolaou, Elisanthi P; Liakopoulos, Theodore D; Tsirigos, Konstantinos D.

I: Bioinformatics (Oxford, England), Bind 26, Nr. 22, 15.11.2010, s. 2811-7.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

Harvard

Bagos, PG, Nikolaou, EP, Liakopoulos, TD & Tsirigos, KD 2010, 'Combined prediction of Tat and Sec signal peptides with hidden Markov models', Bioinformatics (Oxford, England), bind 26, nr. 22, s. 2811-7. https://doi.org/10.1093/bioinformatics/btq530

APA

Bagos, P. G., Nikolaou, E. P., Liakopoulos, T. D., & Tsirigos, K. D. (2010). Combined prediction of Tat and Sec signal peptides with hidden Markov models. Bioinformatics (Oxford, England), 26(22), 2811-7. https://doi.org/10.1093/bioinformatics/btq530

Vancouver

Bagos PG, Nikolaou EP, Liakopoulos TD, Tsirigos KD. Combined prediction of Tat and Sec signal peptides with hidden Markov models. Bioinformatics (Oxford, England). 2010 nov 15;26(22):2811-7. https://doi.org/10.1093/bioinformatics/btq530

Author

Bagos, Pantelis G ; Nikolaou, Elisanthi P ; Liakopoulos, Theodore D ; Tsirigos, Konstantinos D. / Combined prediction of Tat and Sec signal peptides with hidden Markov models. I: Bioinformatics (Oxford, England). 2010 ; Bind 26, Nr. 22. s. 2811-7.

Bibtex

@article{2f23bbe3464d4736853ccb73c172364c,
title = "Combined prediction of Tat and Sec signal peptides with hidden Markov models",
abstract = "MOTIVATION: Computational prediction of signal peptides is of great importance in computational biology. In addition to the general secretory pathway (Sec), Bacteria, Archaea and chloroplasts possess another major pathway that utilizes the Twin-Arginine translocase (Tat), which recognizes longer and less hydrophobic signal peptides carrying a distinctive pattern of two consecutive Arginines (RR) in the n-region. A major functional differentiation between the Sec and Tat export pathways lies in the fact that the former translocates secreted proteins unfolded through a protein-conducting channel, whereas the latter translocates completely folded proteins using an unknown mechanism. The purpose of this work is to develop a novel method for predicting and discriminating Sec from Tat signal peptides at better accuracy.RESULTS: We report the development of a novel method, PRED-TAT, which is capable of discriminating Sec from Tat signal peptides and predicting their cleavage sites. The method is based on Hidden Markov Models and possesses a modular architecture suitable for both Sec and Tat signal peptides. On an independent test set of experimentally verified Tat signal peptides, PRED-TAT clearly outperforms the previously proposed methods TatP and TATFIND, whereas, when evaluated as a Sec signal peptide predictor compares favorably to top-scoring predictors such as SignalP and Phobius. The method is freely available for academic users at http://www.compgen.org/tools/PRED-TAT/.",
keywords = "Computational Biology/methods, Databases, Protein, Markov Chains, Membrane Transport Proteins/chemistry, Protein Folding, Protein Sorting Signals, Secretory Pathway",
author = "Bagos, {Pantelis G} and Nikolaou, {Elisanthi P} and Liakopoulos, {Theodore D} and Tsirigos, {Konstantinos D}",
year = "2010",
month = "11",
day = "15",
doi = "10.1093/bioinformatics/btq530",
language = "English",
volume = "26",
pages = "2811--7",
journal = "Bioinformatics (Online)",
issn = "1367-4811",
publisher = "Oxford University Press",
number = "22",

}

RIS

TY - JOUR

T1 - Combined prediction of Tat and Sec signal peptides with hidden Markov models

AU - Bagos, Pantelis G

AU - Nikolaou, Elisanthi P

AU - Liakopoulos, Theodore D

AU - Tsirigos, Konstantinos D

PY - 2010/11/15

Y1 - 2010/11/15

N2 - MOTIVATION: Computational prediction of signal peptides is of great importance in computational biology. In addition to the general secretory pathway (Sec), Bacteria, Archaea and chloroplasts possess another major pathway that utilizes the Twin-Arginine translocase (Tat), which recognizes longer and less hydrophobic signal peptides carrying a distinctive pattern of two consecutive Arginines (RR) in the n-region. A major functional differentiation between the Sec and Tat export pathways lies in the fact that the former translocates secreted proteins unfolded through a protein-conducting channel, whereas the latter translocates completely folded proteins using an unknown mechanism. The purpose of this work is to develop a novel method for predicting and discriminating Sec from Tat signal peptides at better accuracy.RESULTS: We report the development of a novel method, PRED-TAT, which is capable of discriminating Sec from Tat signal peptides and predicting their cleavage sites. The method is based on Hidden Markov Models and possesses a modular architecture suitable for both Sec and Tat signal peptides. On an independent test set of experimentally verified Tat signal peptides, PRED-TAT clearly outperforms the previously proposed methods TatP and TATFIND, whereas, when evaluated as a Sec signal peptide predictor compares favorably to top-scoring predictors such as SignalP and Phobius. The method is freely available for academic users at http://www.compgen.org/tools/PRED-TAT/.

AB - MOTIVATION: Computational prediction of signal peptides is of great importance in computational biology. In addition to the general secretory pathway (Sec), Bacteria, Archaea and chloroplasts possess another major pathway that utilizes the Twin-Arginine translocase (Tat), which recognizes longer and less hydrophobic signal peptides carrying a distinctive pattern of two consecutive Arginines (RR) in the n-region. A major functional differentiation between the Sec and Tat export pathways lies in the fact that the former translocates secreted proteins unfolded through a protein-conducting channel, whereas the latter translocates completely folded proteins using an unknown mechanism. The purpose of this work is to develop a novel method for predicting and discriminating Sec from Tat signal peptides at better accuracy.RESULTS: We report the development of a novel method, PRED-TAT, which is capable of discriminating Sec from Tat signal peptides and predicting their cleavage sites. The method is based on Hidden Markov Models and possesses a modular architecture suitable for both Sec and Tat signal peptides. On an independent test set of experimentally verified Tat signal peptides, PRED-TAT clearly outperforms the previously proposed methods TatP and TATFIND, whereas, when evaluated as a Sec signal peptide predictor compares favorably to top-scoring predictors such as SignalP and Phobius. The method is freely available for academic users at http://www.compgen.org/tools/PRED-TAT/.

KW - Computational Biology/methods

KW - Databases, Protein

KW - Markov Chains

KW - Membrane Transport Proteins/chemistry

KW - Protein Folding

KW - Protein Sorting Signals

KW - Secretory Pathway

U2 - 10.1093/bioinformatics/btq530

DO - 10.1093/bioinformatics/btq530

M3 - Journal article

C2 - 20847219

VL - 26

SP - 2811

EP - 2817

JO - Bioinformatics (Online)

JF - Bioinformatics (Online)

SN - 1367-4811

IS - 22

ER -

ID: 238681882