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

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Authors

Felix Teufel · ETH Zurich

José Juan Almagro Armenteros · University of Copenhagen

Alexander Rosenberg Johansen · Stanford University

Magnús Halldór Gíslason · Copenhagen University Hospital

Silas Irby Pihl · Technical University of Denmark

Konstantinos D. Tsirigos · European Bioinformatics Institute

Ole Winther · University of Copenhagen

Søren Brunak

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.

Topics & Keywords

Machine Learning in Bioinformatics vaccines and immunoinformatics approaches Bioinformatics and Genomic Networks

Publication Details

Published in: Nature Biotechnology

Volume 40, Issue 7, pp. 1023-1025

DOI: 10.1038/s41587-021-01156-3

Field-Weighted Citation Impact: 212.46

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

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Abstract

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Publication Details