SignalP 5.0 improves signal peptide predictions using deep neural networks

José Juan Almagro Armenteros; Konstantinos Tsirigos; Casper Kaae Sønderby; Thomas Nordahl Petersen; Ole Winther; Søren Brunak; Gunnar von Heijne; Henrik Nielsen

doi:10.1038/s41587-019-0036-z

SignalP 5.0 improves signal peptide predictions using deep neural networks

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

811 Citationer (Scopus)

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.

Originalsprog	Engelsk
Tidsskrift	Nature Biotechnology
Vol/bind	37
Udgave nummer	4
Sider (fra-til)	420-423
ISSN	1087-0156
DOI	https://doi.org/10.1038/s41587-019-0036-z
Status	Udgivet - 1 apr. 2019

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10.1038/s41587-019-0036-z

Citationsformater

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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.",

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

AU - Armenteros, José Juan Almagro

AU - Tsirigos, Konstantinos

AU - Sønderby, Casper Kaae

AU - Petersen, Thomas Nordahl

AU - Winther, Ole

AU - Brunak, Søren

AU - von Heijne, Gunnar

AU - Nielsen, Henrik

PY - 2019/4/1

Y1 - 2019/4/1

N2 - 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.

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

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SN - 1087-0156

VL - 37

SP - 420

EP - 423

JO - Nature Biotechnology

JF - Nature Biotechnology

IS - 4

ER -

SignalP 5.0 improves signal peptide predictions using deep neural networks

Abstract

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