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

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.

Keywords

Signal peptideSIGNAL (programming language)Computational biologyChemistryCell biologyBiochemistryBiologyPeptide sequenceComputer scienceGene

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Year: 2022
Type: article
Citations: 2093
Access: Closed

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APA Style

                            
                                    Felix Teufel, 
                                
                                    José Juan Almagro Armenteros, 
                                
                                    Alexander Rosenberg Johansen
                                
                                et al.
                            
                            (2022). 
                            SignalP 6.0 predicts all five types of signal peptides using protein language models. 
                            Repository for Publications and Research Data (ETH Zurich)
                            
                            .
                            https://doi.org/10.3929/ethz-b-000524414

Identifiers

DOI: 10.3929/ethz-b-000524414