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Detecting sequence signals in targeting peptides using deep learning
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).
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Number of Authors: 72019 (English)In: Life science alliance, E-ISSN 2575-1077, Vol. 2, no 5, article id UNSP e201900429Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2019. Vol. 2, no 5, article id UNSP e201900429
National Category
Biochemistry and Molecular Biology
Identifiers
URN: urn:nbn:se:su:diva-176753DOI: 10.26508/lsa.201900429ISI: 000494674100006PubMedID: 31570514OAI: oai:DiVA.org:su-176753DiVA, id: diva2:1377160
Available from: 2019-12-11 Created: 2019-12-11 Last updated: 2019-12-12Bibliographically approved

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Salvatore, MarcoEmanuelsson, OlofWinther, Olevon Heijne, GunnarElofsson, Arne
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