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

By Felix Teufel, J. J. Almagro Armenteros, alexander rosenberg johansen, M. H. Gíslason, Silas Irby Pihl, Konstantinos D. Tsirigos, Ole Winther, Søren Brunak, G. von Heijne, Henrik Nielsen. Nature Biotechnology, 2022

2,313 citations recorded by the source corpus.

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. A new version of SignalP predicts all types of signal peptides.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/e240010a00e736106eb2e9489c52edfb43712e1f

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In Molecular Biology

1. Anh Tuan Tran
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3. Dominic M. Eekels
4. Finn Overduin
5. Michael Overduin

Citations in the corpus

• A computational study of the structure and function of human Zrt and Irt-like proteins metal transporters: An elevator-type transport mechanism predicted by AlphaFold2 (2022, neutral): “0; Teufel et al., 2022).”
• Bilingual language model for protein sequence and structure (2024, neutral): “For general-purpose pLMs, this so-called transfer learning succeeds for many aspects of protein prediction, including, for function prediction: gene ontology [12], transport signals [13], binding residues [14], or subcellular location [15], [16], and for protein structure prediction: 2D [17] and 3D ”
• Transmembrane Membrane Readers form a Novel Class of Proteins That Include Peripheral Phosphoinositide Recognition Domains and Viral Spikes (2022, methodology): “Signal sequences were predicted using PrediSi [21] and SignalP [22].”