SignalP 5.0 improves signal peptide predictions using deep neural networks

Jose Juan Almagro Armenteros, Konstantinos D. Tsirigos, Casper Kaae Sønderby, Thomas Nordahl Petersen, Ole Winther, Søren Brunak, Gunnar von Heijne, Henrik Nielsen*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

Signal peptides (SPs) are short amino acid sequences in the amino terminus of many newly synthesized proteins that target proteins into, or across, membranes. Bioinformatic tools can predict SPs from amino acid sequences, but most cannot distinguish between various types of signal peptides. We present a deep neural network-based approach that improves SP prediction across all domains of life and distinguishes between three types of prokaryotic SPs.
Original languageEnglish
JournalNature Biotechnology
Volume37
Pages (from-to)420-423
ISSN1087-0156
DOIs
Publication statusPublished - 2019

Cite this

Almagro Armenteros, Jose Juan ; Tsirigos, Konstantinos D. ; Sønderby, Casper Kaae ; Petersen, Thomas Nordahl ; Winther, Ole ; Brunak, Søren ; von Heijne, Gunnar ; Nielsen, Henrik. / SignalP 5.0 improves signal peptide predictions using deep neural networks. In: Nature Biotechnology. 2019 ; Vol. 37. pp. 420-423.
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abstract = "Signal peptides (SPs) are short amino acid sequences in the amino terminus of many newly synthesized proteins that target proteins into, or across, membranes. Bioinformatic tools can predict SPs from amino acid sequences, but most cannot distinguish between various types of signal peptides. We present a deep neural network-based approach that improves SP prediction across all domains of life and distinguishes between three types of prokaryotic SPs.",
author = "{Almagro Armenteros}, {Jose Juan} and Tsirigos, {Konstantinos D.} and S{\o}nderby, {Casper Kaae} and Petersen, {Thomas Nordahl} and Ole Winther and S{\o}ren Brunak and {von Heijne}, Gunnar and Henrik Nielsen",
year = "2019",
doi = "10.1038/s41587-019-0036-z",
language = "English",
volume = "37",
pages = "420--423",
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SignalP 5.0 improves signal peptide predictions using deep neural networks. / Almagro Armenteros, Jose Juan; Tsirigos, Konstantinos D.; Sønderby, Casper Kaae; Petersen, Thomas Nordahl; Winther, Ole; Brunak, Søren; von Heijne, Gunnar; Nielsen, Henrik.

In: Nature Biotechnology, Vol. 37, 2019, p. 420-423.

Research output: Contribution to journalJournal articleResearchpeer-review

TY - JOUR

T1 - SignalP 5.0 improves signal peptide predictions using deep neural networks

AU - Almagro Armenteros, Jose Juan

AU - Tsirigos, Konstantinos D.

AU - Sønderby, Casper Kaae

AU - Petersen, Thomas Nordahl

AU - Winther, Ole

AU - Brunak, Søren

AU - von Heijne, Gunnar

AU - Nielsen, Henrik

PY - 2019

Y1 - 2019

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AB - Signal peptides (SPs) are short amino acid sequences in the amino terminus of many newly synthesized proteins that target proteins into, or across, membranes. Bioinformatic tools can predict SPs from amino acid sequences, but most cannot distinguish between various types of signal peptides. We present a deep neural network-based approach that improves SP prediction across all domains of life and distinguishes between three types of prokaryotic SPs.

U2 - 10.1038/s41587-019-0036-z

DO - 10.1038/s41587-019-0036-z

M3 - Journal article

C2 - 30778233

VL - 37

SP - 420

EP - 423

JO - Nature Biotechnology

JF - Nature Biotechnology

SN - 1087-0156

ER -