SignalP 5.0 improves signal peptide predictions using deep neural networks.

Journal: Nature biotechnology
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.

Authors

  • José Juan Almagro Armenteros
    Department of Bio and Health Informatics, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark.
  • Konstantinos D Tsirigos
    Department of Bio and Health Informatics, Technical University of Denmark, Kgs Lyngby, Denmark.
  • Casper Kaae Sønderby
    The Bioinformatics Centre, Department of Biology, University of Copenhagen, 2200 Copenhagen N, Denmark.
  • Thomas Nordahl Petersen
    National Food Institute, Technical University of Denmark, Kgs Lyngby, Denmark.
  • Ole Winther
    The Bioinformatics Centre, Department of Biology, University of Copenhagen, 2200 Copenhagen N, Denmark.
  • Søren Brunak
    NNF Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark.
  • Gunnar von Heijne
    Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden.
  • Henrik Nielsen
    Department of Bio and Health Informatics, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark.

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