Proteome sequence features carry signatures of the environmental niche of prokaryotes

BMC Evolutionary Biology

Research Article

Tomislav Šmuc¹  Fran Supek¹  Zlatko Smole²  Ivo F Sbalzarini³  Anita Krisko⁴  Nela Nikolic⁵ 

[1] Division of Electronics, Rudjer Boskovic Institute, Bijenicka 54, 10000, Zagreb, Croatia;Institute for Cell Biology, ETH Zuerich, Schafmattstrase 18, 8093, zuerich, Switzerland;Mediterranean Institute for Life Sciences, Mestrovicevo setaliste bb, 21000, Split, Croatia;Institute of Theoretical Computer Science and Swiss Institute of Bioinformatics, ETH Zurich, Zurich, Switzerland;Mediterranean Institute for Life Sciences, Mestrovicevo setaliste bb, 21000, Split, Croatia;Institut National de la Santé et de la Recherche Médicale U1001, Université Paris Descartes, Faculté de Médecine, Cedex 15, 156 rue de Vaugirard, 75730, Paris, France;Mediterranean Institute for Life Sciences, Mestrovicevo setaliste bb, 21000, Split, Croatia;Institute of Biogeochemistry and Pollutant Dynamics, ETH Zuerich, Unversitätstrasse 16, 8092, Zuerich, Switzerland;

关键词: Support Vector Machine;    Feature Selection;    Random Forest;    Optimal Growth Temperature;    Environmental Niche;   

DOI  :  10.1186/1471-2148-11-26

 received in 2010-07-20, accepted in 2011-01-26,  发布年份 2011

来源: Springer

【 摘 要 】

BackgroundProkaryotic 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.ResultsWe 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.ConclusionsTo 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.

【 授权许可】

Unknown   
© Smole et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. 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.