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.
Original language | English |
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Journal | Nature Biotechnology |
Volume | 37 |
Issue number | 4 |
Pages (from-to) | 420-423 |
Number of pages | 4 |
ISSN | 1087-0156 |
DOIs | |
Publication status | Published - Apr 2019 |
Externally published | Yes |
Keywords
- Algorithms
- Amino Acid Sequence
- Archaeal Proteins/classification
- Bacterial Proteins/classification
- Biotechnology
- Computational Biology
- Eukaryota/genetics
- Neural Networks, Computer
- Protein Sorting Signals/genetics
- Sequence Analysis, Protein
- Software