SignalP 6.0 predicts all five types of signal peptides using protein language models
FTFelix TeufelJJJosé Juan Almagro ArmenterosARAlexander Rosenberg JohansenMHMagnús Halldór GíslasonSISilas Irby Pihl
ETH Zurich · Technical University of Denmark · +8 more institutions
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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.
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Authors
10- FTFelix TeufelCorresponding
ETH Zurich, Technical University of Denmark
- JJJosé Juan Almagro Armenteros
University of Copenhagen, Novo Nordisk Foundation
- ARAlexander Rosenberg Johansen
Stanford University
- MHMagnús Halldór Gíslason
Copenhagen University Hospital, Rigshospitalet
- SISilas Irby Pihl
Technical University of Denmark
Topics & keywords
Topics
Keywords
- Signal peptide
- Secretion
- Secretory protein
- Peptide sequence
- Protein sequencing
- SIGNAL (programming language)
- Protein Sorting Signals
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