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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Vydané v:Nature biotechnology Ročník 40; číslo 7; s. 1023 - 1025
Hlavní autori: 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:English
Vydavateľské údaje: New York Nature Publishing Group US 01.07.2022
Nature Publishing Group
Predmet:
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
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Popis
Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1087-0156
1546-1696
1546-1696
DOI:10.1038/s41587-021-01156-3