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Detecting Novel Sequence Signals in Targeting Peptides Using Deep Learning
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.
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(English)Manuscript (preprint) (Other academic)
National Category
Bioinformatics (Computational Biology)
Research subject
Biochemistry towards Bioinformatics
Identifiers
URN: urn:nbn:se:su:diva-171426OAI: oai:DiVA.org:su-171426DiVA, id: diva2:1341022
Available from: 2019-08-07 Created: 2019-08-07 Last updated: 2019-08-19Bibliographically approved
In thesis
1. Predicting the route: from protein sequence to sorting in eukaryotic cell
Open this publication in new window or tab >>Predicting the route: from protein sequence to sorting in eukaryotic cell
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Proteins need to be localised in the correct compartment of a eukaryotic cell to function correctly. Therefore, a protein needs to be transported to the right location. Specific signals present in the protein sequence direct proteins to different subcellular localisations. The correct transport is essential for the life of the cell, while, possible errors during the transport can cause irreversible damage and interfere with the activities of surrounding proteins. For more than 30 years, the development of methods to identify the localisation of proteins using both experimental and computational approaches has been an important research area. The objective of this thesis is to develop better computational methods for the classification of the subcellular localisation of eukaryotic proteins. I first describe the development of a consensus method, SubCons, which improves the subcellular prediction of human proteins. Next, I present the SubCons web-server as well as an additional benchmark using protein annotation from novel mass-spectrometry studies in two eukaryotic organisms Mus musculus and Drosophila melanogaster. Then, I present the new version of TargetP and how deep learning can improve the identification of N-terminal sorting signals by focusing on relevant biological signatures. Finally, I describe the development of a novel method for sub-nuclear localisation prediction. Here, I show that the performance of a deep convolutional neural network is improved when using an augmented dataset of homologous proteins.

Place, publisher, year, edition, pages
Stockholm: Department of Biochemistry and Biophysics, Stockholm University, 2019. p. 65
Keywords
eukaryotic cell, sorting signals, subcellular localisation, machine learning, biological sequence analysis, bioinformatics
National Category
Bioinformatics (Computational Biology)
Research subject
Biochemistry towards Bioinformatics
Identifiers
urn:nbn:se:su:diva-171434 (URN)978-91-7797-801-5 (ISBN)978-91-7797-802-2 (ISBN)
Public defence
2019-09-27, Magnélisalen, Kemiska övningslaboratoriet, Svante Arrhenius väg 16 B, Stockholm, 10:00 (English)
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Supervisors
Note

At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 3: Manuscript. Paper 4: Manuscript.

Available from: 2019-09-04 Created: 2019-08-08 Last updated: 2019-08-26Bibliographically approved

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Salvatore, Marcovon Heijne, GunnarElofsson, Arne
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