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Detecting sequence signals in targeting peptides using deep learning
Tech Univ Denmark, Dept Hlth Technol, Sect Bioinformat, Lyngby, Denmark..
Sci Life Lab, Solna, Sweden.;Stockholm Univ, Dept Biochem & Biophys, Stockholm, Sweden..
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology.ORCID iD: 0000-0002-8879-9245
Tech Univ Denmark, DTU Compute, Lyngby, Denmark.;Univ Copenhagen, Computat & RNA Biol, Copenhagen, Denmark.;Copenhagen Univ Hosp, Rigshosp, Ctr Genom Med, Copenhagen, Denmark..ORCID iD: 0000-0002-1966-3205
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2019 (English)In: LIFE SCIENCE ALLIANCE, 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
LIFE SCIENCE ALLIANCE LLC , 2019. Vol. 2, no 5, article id UNSP e201900429
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Biological Sciences
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URN: urn:nbn:se:kth:diva-264335DOI: 10.26508/lsa.201900429ISI: 000494674100006PubMedID: 31570514Scopus ID: 2-s2.0-85072779066OAI: oai:DiVA.org:kth-264335DiVA, id: diva2:1373132
Note

QC 20191126

Available from: 2019-11-26 Created: 2019-11-26 Last updated: 2019-11-26Bibliographically approved

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Emanuelsson, Olof

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