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

Felix Teufel; Jose Juan Almagro Armenteros; Alexander Rosenberg Johansen; Magnús Halldór Gíslason; Silas Irby Pihl; Konstantinos D. Tsirigos; Ole Winther; Søren Brunak; Gunnar von Heijne; Henrik Nielsen; Felix Teufel; Jose Juan Almagro Armenteros; Alexander Rosenberg Johansen; Magnús Halldór Gíslason; Silas Irby Pihl; Konstantinos D. Tsirigos; Ole Winther; Søren Brunak; Gunnar von Heijne; Henrik Nielsen

doi:10.1038/s41587-021-01156-3

Abstract

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.

MeSH Terms

AlgorithmsAmino Acid SequenceLanguageProtein Sorting SignalsProteins

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Publication Info

Year: 2022
Type: article
Volume: 40
Issue: 7
Pages: 1023-1025
Citations: 2267
Access: Closed

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APA Style

                            
                                
                                    Felix Teufel, 
                                
                                    Jose Juan Almagro Armenteros, 
                                
                                    Alexander Rosenberg Johansen
                                
                                et al.
                            
                            (2022). 
                            SignalP 6.0 predicts all five types of signal peptides using protein language models. 
                            Nature Biotechnology
                            , 40
                            (7)
                            , 1023-1025.
                            https://doi.org/10.1038/s41587-021-01156-3
                        

Identifiers

DOI: 10.1038/s41587-021-01156-3
PMID: 34980915
PMCID: PMC9287161

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Data completeness: 81%