RT Journal Article SR Electronic T1 Detecting sequence signals in targeting peptides using deep learning JF Life Science Alliance JO Life Sci. Alliance FD Life Science Alliance LLC SP e201900429 DO 10.26508/lsa.201900429 VO 2 IS 5 A1 Jose Juan Almagro Armenteros A1 Marco Salvatore A1 Olof Emanuelsson A1 Ole Winther A1 Gunnar von Heijne A1 Arne Elofsson A1 Henrik Nielsen YR 2019 UL https://www.life-science-alliance.org/content/2/5/e201900429.abstract AB 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.