SignalP 5.0 improves signal peptide predictions using deep neural networks
Research output: Contribution to journal › Journal article › Communication
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 status||Published - 18 Feb 2019|