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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Veröffentlicht in:	Nature biotechnology Jg. 40; H. 7; S. 1023 - 1025
Hauptverfasser:	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
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Nature Publishing Group US 01.07.2022 Nature Publishing Group
Schlagworte:	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
Online-Zugang:	Volltext
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Zusammenfassung:	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.
Bibliographie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1087-0156 1546-1696 1546-1696
DOI:	10.1038/s41587-021-01156-3