| Literature DB >> 30778233 |
José Juan Almagro Armenteros1, Konstantinos D Tsirigos1,2,3,4, Casper Kaae Sønderby5, Thomas Nordahl Petersen6, Ole Winther5,7, Søren Brunak1,8, Gunnar von Heijne2,3, Henrik Nielsen9.
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.Entities:
Mesh:
Substances:
Year: 2019 PMID: 30778233 DOI: 10.1038/s41587-019-0036-z
Source DB: PubMed Journal: Nat Biotechnol ISSN: 1087-0156 Impact factor: 54.908