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Proteome sequence features carry signatures of the environmental niche of prokaryotes
BACKGROUND: Prokaryotic environmental adaptations occur at different levels within cells to ensure the preservation of genome integrity, proper protein folding and function as well as membrane fluidity. Although specific composition and structure of cellular components suitable for the variety of ex...
Autores principales: | , , , , , |
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Formato: | Texto |
Lenguaje: | English |
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BioMed Central
2011
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3045906/ https://www.ncbi.nlm.nih.gov/pubmed/21269423 http://dx.doi.org/10.1186/1471-2148-11-26 |
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author | Smole, Zlatko Nikolic, Nela Supek, Fran Šmuc, Tomislav Sbalzarini, Ivo F Krisko, Anita |
author_facet | Smole, Zlatko Nikolic, Nela Supek, Fran Šmuc, Tomislav Sbalzarini, Ivo F Krisko, Anita |
author_sort | Smole, Zlatko |
collection | PubMed |
description | BACKGROUND: Prokaryotic environmental adaptations occur at different levels within cells to ensure the preservation of genome integrity, proper protein folding and function as well as membrane fluidity. Although specific composition and structure of cellular components suitable for the variety of extreme conditions has already been postulated, a systematic study describing such adaptations has not yet been performed. We therefore explored whether the environmental niche of a prokaryote could be deduced from the sequence of its proteome. Finally, we aimed at finding the precise differences between proteome sequences of prokaryotes from different environments. RESULTS: We analyzed the proteomes of 192 prokaryotes from different habitats. We collected detailed information about the optimal growth conditions of each microorganism. Furthermore, we selected 42 physico-chemical properties of amino acids and computed their values for each proteome. Further, on the same set of features we applied two fundamentally different machine learning methods, Support Vector Machines and Random Forests, to successfully classify between bacteria and archaea, halophiles and non-halophiles, as well as mesophiles, thermophiles and mesothermophiles. Finally, we performed feature selection by using Random Forests. CONCLUSIONS: To our knowledge, this is the first time that three different classification cases (domain of life, halophilicity and thermophilicity) of proteome adaptation are successfully performed with the same set of 42 features. The characteristic features of a specific adaptation constitute a signature that may help understanding the mechanisms of adaptation to extreme environments. |
format | Text |
id | pubmed-3045906 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-30459062011-03-01 Proteome sequence features carry signatures of the environmental niche of prokaryotes Smole, Zlatko Nikolic, Nela Supek, Fran Šmuc, Tomislav Sbalzarini, Ivo F Krisko, Anita BMC Evol Biol Research Article BACKGROUND: Prokaryotic environmental adaptations occur at different levels within cells to ensure the preservation of genome integrity, proper protein folding and function as well as membrane fluidity. Although specific composition and structure of cellular components suitable for the variety of extreme conditions has already been postulated, a systematic study describing such adaptations has not yet been performed. We therefore explored whether the environmental niche of a prokaryote could be deduced from the sequence of its proteome. Finally, we aimed at finding the precise differences between proteome sequences of prokaryotes from different environments. RESULTS: We analyzed the proteomes of 192 prokaryotes from different habitats. We collected detailed information about the optimal growth conditions of each microorganism. Furthermore, we selected 42 physico-chemical properties of amino acids and computed their values for each proteome. Further, on the same set of features we applied two fundamentally different machine learning methods, Support Vector Machines and Random Forests, to successfully classify between bacteria and archaea, halophiles and non-halophiles, as well as mesophiles, thermophiles and mesothermophiles. Finally, we performed feature selection by using Random Forests. CONCLUSIONS: To our knowledge, this is the first time that three different classification cases (domain of life, halophilicity and thermophilicity) of proteome adaptation are successfully performed with the same set of 42 features. The characteristic features of a specific adaptation constitute a signature that may help understanding the mechanisms of adaptation to extreme environments. BioMed Central 2011-01-26 /pmc/articles/PMC3045906/ /pubmed/21269423 http://dx.doi.org/10.1186/1471-2148-11-26 Text en Copyright ©2011 Smole et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Smole, Zlatko Nikolic, Nela Supek, Fran Šmuc, Tomislav Sbalzarini, Ivo F Krisko, Anita Proteome sequence features carry signatures of the environmental niche of prokaryotes |
title | Proteome sequence features carry signatures of the environmental niche of prokaryotes |
title_full | Proteome sequence features carry signatures of the environmental niche of prokaryotes |
title_fullStr | Proteome sequence features carry signatures of the environmental niche of prokaryotes |
title_full_unstemmed | Proteome sequence features carry signatures of the environmental niche of prokaryotes |
title_short | Proteome sequence features carry signatures of the environmental niche of prokaryotes |
title_sort | proteome sequence features carry signatures of the environmental niche of prokaryotes |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3045906/ https://www.ncbi.nlm.nih.gov/pubmed/21269423 http://dx.doi.org/10.1186/1471-2148-11-26 |
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