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. 2022 Jul;40(7):1023-1025.
doi: 10.1038/s41587-021-01156-3. Epub 2022 Jan 3.

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

Felix Teufel  1   2 , José Juan Almagro Armenteros  3 , Alexander Rosenberg Johansen  4 , Magnús Halldór Gíslason  5 , Silas Irby Pihl  1 , Konstantinos D Tsirigos  6 , Ole Winther  5   7   8 , Søren Brunak  3 , Gunnar von Heijne  9   10 , Henrik Nielsen  11
Affiliations

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

Felix Teufel et al. Nat Biotechnol. 2022 Jul.

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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Conflict of interest statement

The downloadable version of SignalP 6.0 has been commercialized (it is licensed for a fee to commercial users). The revenue from these commercial sales is divided between the program developers and the Technical University of Denmark.

Figures

Fig. 1
Fig. 1. Modeling SP structure using protein LMs.
a, Region structures of the five SP types. Twin arginine (RR)-translocated SPs feature a twin-arginine motif, while SPs cleaved by SPase II feature a C-terminal lipobox. Sec/SPIII SPs have no substructure. b, Protein LM training procedure. BERT learns protein features by predicting masked amino acids in sequences from UniRef100. c, t-Distributed stochastic neighbor embedding (t-SNE) projection of protein representations before prediction training. Different SP types form distinct clusters, separated from sequences without SPs. d, SignalP 6.0 architecture. An amino acid sequence is passed through the LM, and the resulting representation serves as input for the CRF, which predicts region probabilities at each position and the SP type. CS, cleavage site.
Fig. 2
Fig. 2. SignalP 6.0 shows strong performance on all types and organism groups.
a, SP detection performance (ARC, Archaea; EUK, Eukarya; NEG, Gram-negative bacteria; POS, Gram-positive bacteria). SignalP 6.0 substantially improves performance on underrepresented types. b, CS prediction performance. SignalP 6.0 has improved precision for all categories. c, Dependence of performance on identity to sequences in the training data. At sequence identities lower than 60%, SignalP 6.0 outperforms SignalP 5.0.
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