Detecting sequence signals in targeting peptides using deep learning

Jose Juan Almagro Armenteros*, Marco Salvatore, Olof Emanuelsson, Ole Winther, Gunnar Von Heijne, Arne Elofsson, Henrik Nielsen

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

56 Downloads (Pure)

Abstract

In bioinformatics, machine learning methods have been used to predict features embedded in the sequences. In contrast to what is generally assumed, machine learning approaches can also provide new insights into the underlying biology. Here, we demonstrate this by presenting TargetP 2.0, a novel state-of-the-art method to identify N-terminal sorting signals, which direct proteins to the secretory pathway, mitochondria, and chloroplasts or other plastids. By examining the strongest signals from the attention layer in the network, we find that the second residue in the protein, that is, the one following the initial methionine, has a strong influence on the classification. We observe that two-thirds of chloroplast and thylakoid transit peptides have an alanine in position 2, compared with 20% in other plant proteins. We also note that in fungi and single-celled eukaryotes, less than 30% of the targeting peptides have an amino acid that allows the removal of the N-terminal methionine compared with 60% for the proteins without targeting peptide. The importance of this feature for predictions has not been highlighted before.

Original languageEnglish
Article number201900429
JournalLife Science Alliance
Volume2
Issue number5
Number of pages14
ISSN2575-1077
DOIs
Publication statusPublished - 1 Jan 2019

Cite this