Format

Send to

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

SignalP 5.0 improves signal peptide predictions using deep neural networks.

Author information

1
Department of Bio and Health Informatics, Technical University of Denmark, Kgs Lyngby, Denmark.
2
Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden.
3
Science for Life Laboratory, Stockholm University, Solna, Sweden.
4
Department of Genome Regulation, Max Planck Institute for Molecular Genetics, Berlin, Germany.
5
Bioinformatics Centre, Department of Biology, University of Copenhagen, Copenhagen, Denmark.
6
National Food Institute, Technical University of Denmark, Kgs Lyngby, Denmark.
7
Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs Lyngby, Denmark.
8
Disease Systems Biology Program, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
9
Department of Bio and Health Informatics, Technical University of Denmark, Kgs Lyngby, Denmark. hnielsen@bioinformatics.dtu.dk.

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.

PMID:
30778233
DOI:
10.1038/s41587-019-0036-z
[Indexed for MEDLINE]

Supplemental Content

Full text links

Icon for Nature Publishing Group
Loading ...
Support Center