SignalP 5.0 improves signal peptide predictions using deep neural networks

Nat Biotechnol. 2019 Apr;37(4):420-423. doi: 10.1038/s41587-019-0036-z. Epub 2019 Feb 18.


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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Amino Acid Sequence
  • Archaeal Proteins / classification
  • Archaeal Proteins / genetics
  • Archaeal Proteins / metabolism
  • Bacterial Proteins / classification
  • Bacterial Proteins / genetics
  • Bacterial Proteins / metabolism
  • Biotechnology
  • Computational Biology
  • Eukaryota / genetics
  • Eukaryota / metabolism
  • Neural Networks, Computer*
  • Protein Sorting Signals / genetics*
  • Protein Sorting Signals / physiology*
  • Sequence Analysis, Protein
  • Software


  • Archaeal Proteins
  • Bacterial Proteins
  • Protein Sorting Signals