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Context-dependent prediction of protein complexes by SiComPre
Most cellular processes are regulated by groups of proteins interacting together to form protein complexes. Protein compositions vary between different tissues or disease conditions enabling or preventing certain protein−protein interactions and resulting in variations in the complexome. Quantitativ...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6141528/ https://www.ncbi.nlm.nih.gov/pubmed/30245847 http://dx.doi.org/10.1038/s41540-018-0073-0 |
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author | Rizzetto, Simone Moyseos, Petros Baldacci, Bianca Priami, Corrado Csikász-Nagy, Attila |
author_facet | Rizzetto, Simone Moyseos, Petros Baldacci, Bianca Priami, Corrado Csikász-Nagy, Attila |
author_sort | Rizzetto, Simone |
collection | PubMed |
description | Most cellular processes are regulated by groups of proteins interacting together to form protein complexes. Protein compositions vary between different tissues or disease conditions enabling or preventing certain protein−protein interactions and resulting in variations in the complexome. Quantitative and qualitative characterization of context-specific protein complexes will help to better understand context-dependent variations in the physiological behavior of cells. Here, we present SiComPre 1.0, a computational tool that predicts context-specific protein complexes by integrating multi-omics sources. SiComPre outperforms other protein complex prediction tools in qualitative predictions and is unique in giving quantitative predictions on the complexome depending on the specific interactions and protein abundances defined by the user. We provide tutorials and examples on the complexome prediction of common model organisms, various human tissues and how the complexome is affected by drug treatment. |
format | Online Article Text |
id | pubmed-6141528 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-61415282018-09-21 Context-dependent prediction of protein complexes by SiComPre Rizzetto, Simone Moyseos, Petros Baldacci, Bianca Priami, Corrado Csikász-Nagy, Attila NPJ Syst Biol Appl Technology Feature Most cellular processes are regulated by groups of proteins interacting together to form protein complexes. Protein compositions vary between different tissues or disease conditions enabling or preventing certain protein−protein interactions and resulting in variations in the complexome. Quantitative and qualitative characterization of context-specific protein complexes will help to better understand context-dependent variations in the physiological behavior of cells. Here, we present SiComPre 1.0, a computational tool that predicts context-specific protein complexes by integrating multi-omics sources. SiComPre outperforms other protein complex prediction tools in qualitative predictions and is unique in giving quantitative predictions on the complexome depending on the specific interactions and protein abundances defined by the user. We provide tutorials and examples on the complexome prediction of common model organisms, various human tissues and how the complexome is affected by drug treatment. Nature Publishing Group UK 2018-09-17 /pmc/articles/PMC6141528/ /pubmed/30245847 http://dx.doi.org/10.1038/s41540-018-0073-0 Text en © The Author(s) 2018 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 | Technology Feature Rizzetto, Simone Moyseos, Petros Baldacci, Bianca Priami, Corrado Csikász-Nagy, Attila Context-dependent prediction of protein complexes by SiComPre |
title | Context-dependent prediction of protein complexes by SiComPre |
title_full | Context-dependent prediction of protein complexes by SiComPre |
title_fullStr | Context-dependent prediction of protein complexes by SiComPre |
title_full_unstemmed | Context-dependent prediction of protein complexes by SiComPre |
title_short | Context-dependent prediction of protein complexes by SiComPre |
title_sort | context-dependent prediction of protein complexes by sicompre |
topic | Technology Feature |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6141528/ https://www.ncbi.nlm.nih.gov/pubmed/30245847 http://dx.doi.org/10.1038/s41540-018-0073-0 |
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