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SignalP 5.0 improves signal peptide predictions using deep neural networks.

Author information

Affiliations

  • 1. Department of Bio and Health Informatics, Technical University of Denmark, Kgs Lyngby, Denmark.
      Authors
    • Almagro Armenteros JJ 1
    • Tsirigos KD 1, 4
    • Brunak S 1
    • Nielsen H 1
    (4 authors)
  • 2. Bioinformatics Centre, Department of Biology, University of Copenhagen, Copenhagen, Denmark.
      Authors
    • Sønderby CK 2
    • Winther O 2
    (2 authors)
  • 3. National Food Institute, Technical University of Denmark, Kgs Lyngby, Denmark.
      Authors
    • Petersen TN 3
    (1 author)
  • 4. Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden.
      Authors
    • Tsirigos KD 1, 4
    • von Heijne G 4
    (2 authors)

ORCIDs linked to this article

Nature Biotechnology, 18 Feb 2019, 37(4):420-423
https://doi.org/10.1038/s41587-019-0036-z PMID: 30778233 

Abstract 

Signal peptides (SPs) are short amino acid sequences in the amino terminus of many newly synthesized proteins that target proteins into, or across, membranes. Bioinformatic tools can predict SPs from amino acid sequences, but most cannot distinguish between various types of signal peptides. We present a deep neural network-based approach that improves SP prediction across all domains of life and distinguishes between three types of prokaryotic SPs.

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Read article at publisher's site: https://doi.org/10.1038/s41587-019-0036-z

Read article for free, from open access legal sources, via Unpaywall: https://backend.orbit.dtu.dk/ws/files/198503962/postprint.pdfUnpaywall

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Funding 

Funders who supported this work.

Novo Nordisk Foundation Center for Protein Research (1)