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Detailed prediction of protein sub-nuclear localization
BACKGROUND: Sub-nuclear structures or locations are associated with various nuclear processes. Proteins localized in these substructures are important to understand the interior nuclear mechanisms. Despite advances in high-throughput methods, experimental protein annotations remain limited. Predicti...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6480651/ https://www.ncbi.nlm.nih.gov/pubmed/31014229 http://dx.doi.org/10.1186/s12859-019-2790-9 |
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author | Littmann, Maria Goldberg, Tatyana Seitz, Sebastian Bodén, Mikael Rost, Burkhard |
author_facet | Littmann, Maria Goldberg, Tatyana Seitz, Sebastian Bodén, Mikael Rost, Burkhard |
author_sort | Littmann, Maria |
collection | PubMed |
description | BACKGROUND: Sub-nuclear structures or locations are associated with various nuclear processes. Proteins localized in these substructures are important to understand the interior nuclear mechanisms. Despite advances in high-throughput methods, experimental protein annotations remain limited. Predictions of cellular compartments have become very accurate, largely at the expense of leaving out substructures inside the nucleus making a fine-grained analysis impossible. RESULTS: Here, we present a new method (LocNuclei) that predicts nuclear substructures from sequence alone. LocNuclei used a string-based Profile Kernel with Support Vector Machines (SVMs). It distinguishes sub-nuclear localization in 13 distinct substructures and distinguishes between nuclear proteins confined to the nucleus and those that are also native to other compartments (traveler proteins). High performance was achieved by implicitly leveraging a large biological knowledge-base in creating predictions by homology-based inference through BLAST. Using this approach, the performance reached AUC = 0.70–0.74 and Q13 = 59–65%. Travelling proteins (nucleus and other) were identified at Q2 = 70–74%. A Gene Ontology (GO) analysis of the enrichment of biological processes revealed that the predicted sub-nuclear compartments matched the expected functionality. Analysis of protein-protein interactions (PPI) show that formation of compartments and functionality of proteins in these compartments highly rely on interactions between proteins. This suggested that the LocNuclei predictions carry important information about function. The source code and data sets are available through GitHub: https://github.com/Rostlab/LocNuclei. CONCLUSIONS: LocNuclei predicts subnuclear compartments and traveler proteins accurately. These predictions carry important information about functionality and PPIs. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-019-2790-9) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6480651 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-64806512019-05-01 Detailed prediction of protein sub-nuclear localization Littmann, Maria Goldberg, Tatyana Seitz, Sebastian Bodén, Mikael Rost, Burkhard BMC Bioinformatics Methodology Article BACKGROUND: Sub-nuclear structures or locations are associated with various nuclear processes. Proteins localized in these substructures are important to understand the interior nuclear mechanisms. Despite advances in high-throughput methods, experimental protein annotations remain limited. Predictions of cellular compartments have become very accurate, largely at the expense of leaving out substructures inside the nucleus making a fine-grained analysis impossible. RESULTS: Here, we present a new method (LocNuclei) that predicts nuclear substructures from sequence alone. LocNuclei used a string-based Profile Kernel with Support Vector Machines (SVMs). It distinguishes sub-nuclear localization in 13 distinct substructures and distinguishes between nuclear proteins confined to the nucleus and those that are also native to other compartments (traveler proteins). High performance was achieved by implicitly leveraging a large biological knowledge-base in creating predictions by homology-based inference through BLAST. Using this approach, the performance reached AUC = 0.70–0.74 and Q13 = 59–65%. Travelling proteins (nucleus and other) were identified at Q2 = 70–74%. A Gene Ontology (GO) analysis of the enrichment of biological processes revealed that the predicted sub-nuclear compartments matched the expected functionality. Analysis of protein-protein interactions (PPI) show that formation of compartments and functionality of proteins in these compartments highly rely on interactions between proteins. This suggested that the LocNuclei predictions carry important information about function. The source code and data sets are available through GitHub: https://github.com/Rostlab/LocNuclei. CONCLUSIONS: LocNuclei predicts subnuclear compartments and traveler proteins accurately. These predictions carry important information about functionality and PPIs. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-019-2790-9) contains supplementary material, which is available to authorized users. BioMed Central 2019-04-23 /pmc/articles/PMC6480651/ /pubmed/31014229 http://dx.doi.org/10.1186/s12859-019-2790-9 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Methodology Article Littmann, Maria Goldberg, Tatyana Seitz, Sebastian Bodén, Mikael Rost, Burkhard Detailed prediction of protein sub-nuclear localization |
title | Detailed prediction of protein sub-nuclear localization |
title_full | Detailed prediction of protein sub-nuclear localization |
title_fullStr | Detailed prediction of protein sub-nuclear localization |
title_full_unstemmed | Detailed prediction of protein sub-nuclear localization |
title_short | Detailed prediction of protein sub-nuclear localization |
title_sort | detailed prediction of protein sub-nuclear localization |
topic | Methodology Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6480651/ https://www.ncbi.nlm.nih.gov/pubmed/31014229 http://dx.doi.org/10.1186/s12859-019-2790-9 |
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