SignalP 6.0 predicts all five types of signal peptides using protein language models

Signal peptides (SPs) are short amino acid sequences that control protein secretion and translocation in all living organisms. SPs can be predicted from sequence data, but existing algorithms are unable to detect all known types of SPs. We introduce SignalP 6.0, a machine learning model that detects...

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Autores principales: Teufel, Felix, Almagro Armenteros, José Juan, Johansen, Alexander Rosenberg, Gíslason, Magnús Halldór, Pihl, Silas Irby, Tsirigos, Konstantinos D., Winther, Ole, Brunak, Søren, von Heijne, Gunnar, Nielsen, Henrik
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group US 2022
Materias:
Brief Communication
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9287161/
https://www.ncbi.nlm.nih.gov/pubmed/34980915
http://dx.doi.org/10.1038/s41587-021-01156-3
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author Teufel, Felix
Almagro Armenteros, José Juan
Johansen, Alexander Rosenberg
Gíslason, Magnús Halldór
Pihl, Silas Irby
Tsirigos, Konstantinos D.
Winther, Ole
Brunak, Søren
von Heijne, Gunnar
Nielsen, Henrik
author_facet Teufel, Felix
Almagro Armenteros, José Juan
Johansen, Alexander Rosenberg
Gíslason, Magnús Halldór
Pihl, Silas Irby
Tsirigos, Konstantinos D.
Winther, Ole
Brunak, Søren
von Heijne, Gunnar
Nielsen, Henrik
author_sort Teufel, Felix
collection PubMed
description Signal peptides (SPs) are short amino acid sequences that control protein secretion and translocation in all living organisms. SPs can be predicted from sequence data, but existing algorithms are unable to detect all known types of SPs. We introduce SignalP 6.0, a machine learning model that detects all five SP types and is applicable to metagenomic data.
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spelling pubmed-92871612022-07-17 SignalP 6.0 predicts all five types of signal peptides using protein language models Teufel, Felix Almagro Armenteros, José Juan Johansen, Alexander Rosenberg Gíslason, Magnús Halldór Pihl, Silas Irby Tsirigos, Konstantinos D. Winther, Ole Brunak, Søren von Heijne, Gunnar Nielsen, Henrik Nat Biotechnol Brief Communication Signal peptides (SPs) are short amino acid sequences that control protein secretion and translocation in all living organisms. SPs can be predicted from sequence data, but existing algorithms are unable to detect all known types of SPs. We introduce SignalP 6.0, a machine learning model that detects all five SP types and is applicable to metagenomic data. Nature Publishing Group US 2022-01-03 2022 /pmc/articles/PMC9287161/ /pubmed/34980915 http://dx.doi.org/10.1038/s41587-021-01156-3 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Brief Communication
Teufel, Felix
Almagro Armenteros, José Juan
Johansen, Alexander Rosenberg
Gíslason, Magnús Halldór
Pihl, Silas Irby
Tsirigos, Konstantinos D.
Winther, Ole
Brunak, Søren
von Heijne, Gunnar
Nielsen, Henrik
SignalP 6.0 predicts all five types of signal peptides using protein language models
title SignalP 6.0 predicts all five types of signal peptides using protein language models
title_full SignalP 6.0 predicts all five types of signal peptides using protein language models
title_fullStr SignalP 6.0 predicts all five types of signal peptides using protein language models
title_full_unstemmed SignalP 6.0 predicts all five types of signal peptides using protein language models
title_short SignalP 6.0 predicts all five types of signal peptides using protein language models
title_sort signalp 6.0 predicts all five types of signal peptides using protein language models
topic Brief Communication
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9287161/
https://www.ncbi.nlm.nih.gov/pubmed/34980915
http://dx.doi.org/10.1038/s41587-021-01156-3
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