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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Bibliographic Details
Published in:Nature biotechnology Vol. 40; no. 7; pp. 1023 - 1025
Main Authors: 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
Language:English
Published: New York Nature Publishing Group US 01.07.2022
Nature Publishing Group
Subjects:
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 Access:Get full text
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Description
Summary: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.
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ISSN:1087-0156
1546-1696
1546-1696
DOI:10.1038/s41587-021-01156-3