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OutCyte: a novel tool for predicting unconventional protein secretion
The prediction of protein localization, such as in the extracellular space, from high-throughput data is essential for functional downstream inference. It is well accepted that some secreted proteins go through the classic endoplasmic reticulum-Golgi pathway with the guidance of a signal peptide. Ho...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6923414/ https://www.ncbi.nlm.nih.gov/pubmed/31857603 http://dx.doi.org/10.1038/s41598-019-55351-z |
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author | Zhao, Linlin Poschmann, Gereon Waldera-Lupa, Daniel Rafiee, Nima Kollmann, Markus Stühler, Kai |
author_facet | Zhao, Linlin Poschmann, Gereon Waldera-Lupa, Daniel Rafiee, Nima Kollmann, Markus Stühler, Kai |
author_sort | Zhao, Linlin |
collection | PubMed |
description | The prediction of protein localization, such as in the extracellular space, from high-throughput data is essential for functional downstream inference. It is well accepted that some secreted proteins go through the classic endoplasmic reticulum-Golgi pathway with the guidance of a signal peptide. However, a large number of proteins have been found to reach the extracellular space by following unconventional secretory pathways. There remains a demand for reliable prediction of unconventional protein secretion (UPS). Here, we present OutCyte, a fast and accurate tool for the prediction of UPS, which for the first time has been built upon experimentally determined UPS proteins. OutCyte mediates the prediction of protein secretion in two steps: first, proteins with N-terminal signals are accurately filtered out; second, proteins without N-terminal signals are classified as UPS or intracellular proteins based on physicochemical features directly generated from their amino acid sequences. We are convinced that OutCyte will play a relevant role in the annotation of experimental data and will therefore contribute to further characterization of the extracellular nature of proteins by considering the commonly neglected UPS proteins. OutCyte has been implemented as a web server at www.outcyte.com. |
format | Online Article Text |
id | pubmed-6923414 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-69234142019-12-20 OutCyte: a novel tool for predicting unconventional protein secretion Zhao, Linlin Poschmann, Gereon Waldera-Lupa, Daniel Rafiee, Nima Kollmann, Markus Stühler, Kai Sci Rep Article The prediction of protein localization, such as in the extracellular space, from high-throughput data is essential for functional downstream inference. It is well accepted that some secreted proteins go through the classic endoplasmic reticulum-Golgi pathway with the guidance of a signal peptide. However, a large number of proteins have been found to reach the extracellular space by following unconventional secretory pathways. There remains a demand for reliable prediction of unconventional protein secretion (UPS). Here, we present OutCyte, a fast and accurate tool for the prediction of UPS, which for the first time has been built upon experimentally determined UPS proteins. OutCyte mediates the prediction of protein secretion in two steps: first, proteins with N-terminal signals are accurately filtered out; second, proteins without N-terminal signals are classified as UPS or intracellular proteins based on physicochemical features directly generated from their amino acid sequences. We are convinced that OutCyte will play a relevant role in the annotation of experimental data and will therefore contribute to further characterization of the extracellular nature of proteins by considering the commonly neglected UPS proteins. OutCyte has been implemented as a web server at www.outcyte.com. Nature Publishing Group UK 2019-12-19 /pmc/articles/PMC6923414/ /pubmed/31857603 http://dx.doi.org/10.1038/s41598-019-55351-z Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Zhao, Linlin Poschmann, Gereon Waldera-Lupa, Daniel Rafiee, Nima Kollmann, Markus Stühler, Kai OutCyte: a novel tool for predicting unconventional protein secretion |
title | OutCyte: a novel tool for predicting unconventional protein secretion |
title_full | OutCyte: a novel tool for predicting unconventional protein secretion |
title_fullStr | OutCyte: a novel tool for predicting unconventional protein secretion |
title_full_unstemmed | OutCyte: a novel tool for predicting unconventional protein secretion |
title_short | OutCyte: a novel tool for predicting unconventional protein secretion |
title_sort | outcyte: a novel tool for predicting unconventional protein secretion |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6923414/ https://www.ncbi.nlm.nih.gov/pubmed/31857603 http://dx.doi.org/10.1038/s41598-019-55351-z |
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