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Open Access Research article

Proteome sequence features carry signatures of the environmental niche of prokaryotes

Zlatko Smole12, Nela Nikolic23, Fran Supek4, Tomislav Šmuc4, Ivo F Sbalzarini5 and Anita Krisko26*

Author Affiliations

1 Institute for Cell Biology, ETH Zuerich, Schafmattstrase 18, 8093 Zuerich, Switzerland

2 Mediterranean Institute for Life Sciences, Mestrovicevo setaliste bb, 21000 Split, Croatia

3 Institute of Biogeochemistry and Pollutant Dynamics, ETH Zuerich, Unversitätstrasse 16, 8092 Zuerich, Switzerland

4 Division of Electronics, Rudjer Boskovic Institute, Bijenicka 54, 10000 Zagreb, Croatia

5 Institute of Theoretical Computer Science and Swiss Institute of Bioinformatics, ETH Zurich, Zurich, Switzerland

6 Institut National de la Santé et de la Recherche Médicale U1001, Université Paris Descartes, Faculté de Médecine, 156 rue de Vaugirard, 75730 Paris Cedex 15, France

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BMC Evolutionary Biology 2011, 11:26  doi:10.1186/1471-2148-11-26

Published: 26 January 2011

Abstract

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.