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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Vydáno v:	Nature biotechnology Ročník 40; číslo 7; s. 1023 - 1025
Hlavní autoři:	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
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Nature Publishing Group US 01.07.2022 Nature Publishing Group
Témata:	631/114/2184 631/114/663/2009 631/337/474 631/80/2023/2022 Agriculture Algorithms Amino acids Annotations Bacteria Bioinformatics Biomedical and Life Sciences Biomedical Engineering/Biotechnology Biomedicine Biotechnology Brief Communication Computer science Datasets Gram-negative bacteria Gram-positive bacteria Language Life Sciences Machine learning Metagenomics Peptides Protein transport Proteins Semantics Signal peptides Translocation
ISSN:	1087-0156, 1546-1696, 1546-1696
On-line přístup:	Získat plný text
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Abstract	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. A new version of SignalP predicts all types of signal peptides.
AbstractList	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. A new version of SignalP predicts all types of signal peptides. 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. 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.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. 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. A new version of SignalP predicts all types of signal peptides.
Author	Johansen, Alexander Rosenberg von Heijne, Gunnar Almagro Armenteros, José Juan Winther, Ole Pihl, Silas Irby Brunak, Søren Teufel, Felix Gíslason, Magnús Halldór Tsirigos, Konstantinos D. Nielsen, Henrik
Author_xml	– sequence: 1 givenname: Felix orcidid: 0000-0003-1275-8065 surname: Teufel fullname: Teufel, Felix organization: Section for Bioinformatics, Department of Health Technology, Technical University of Denmark, Department of Biosystems Science and Engineering, ETH Zurich – sequence: 2 givenname: José Juan orcidid: 0000-0003-0111-1362 surname: Almagro Armenteros fullname: Almagro Armenteros, José Juan organization: Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen – sequence: 3 givenname: Alexander Rosenberg surname: Johansen fullname: Johansen, Alexander Rosenberg organization: Department of Computer Science, Stanford University – sequence: 4 givenname: Magnús Halldór surname: Gíslason fullname: Gíslason, Magnús Halldór organization: Center for Genomic Medicine, Rigshospitalet (Copenhagen University Hospital) – sequence: 5 givenname: Silas Irby surname: Pihl fullname: Pihl, Silas Irby organization: Section for Bioinformatics, Department of Health Technology, Technical University of Denmark – sequence: 6 givenname: Konstantinos D. orcidid: 0000-0001-5280-1107 surname: Tsirigos fullname: Tsirigos, Konstantinos D. organization: EMBL-EBI, Wellcome Genome Campus – sequence: 7 givenname: Ole orcidid: 0000-0002-1966-3205 surname: Winther fullname: Winther, Ole organization: Center for Genomic Medicine, Rigshospitalet (Copenhagen University Hospital), Department of Biology, Bioinformatics Centre, University of Copenhagen, Section for Cognitive Systems, Department of Applied Mathematics and Computer Science, Technical University of Denmark – sequence: 8 givenname: Søren orcidid: 0000-0003-0316-5866 surname: Brunak fullname: Brunak, Søren organization: Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen – sequence: 9 givenname: Gunnar orcidid: 0000-0002-4490-8569 surname: von Heijne fullname: von Heijne, Gunnar organization: Department of Biochemistry and Biophysics, Stockholm University, Science for Life Laboratory, Stockholm University – sequence: 10 givenname: Henrik orcidid: 0000-0002-9412-9643 surname: Nielsen fullname: Nielsen, Henrik email: henni@dtu.dk organization: Section for Bioinformatics, Department of Health Technology, Technical University of Denmark
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/34980915$$D View this record in MEDLINE/PubMed https://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-201108$$DView record from Swedish Publication Index (Stockholms universitet)
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Snippet	Signal peptides (SPs) are short amino acid sequences that control protein secretion and translocation in all living organisms. SPs can be predicted from...
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SubjectTerms	631/114/2184 631/114/663/2009 631/337/474 631/80/2023/2022 Agriculture Algorithms Amino acids Annotations Bacteria Bioinformatics Biomedical and Life Sciences Biomedical Engineering/Biotechnology Biomedicine Biotechnology Brief Communication Computer science Datasets Gram-negative bacteria Gram-positive bacteria Language Life Sciences Machine learning Metagenomics Peptides Protein transport Proteins Semantics Signal peptides Translocation
Title	SignalP 6.0 predicts all five types of signal peptides using protein language models
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