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Proteome sequence features carry signatures of the environmental niche of prokaryotes.

Smole Z, Nikolic N, Supek F, Šmuc T, Sbalzarini IF, Krisko A - BMC Evol. Biol. (2011)

Bottom Line: 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.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.

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Affiliation: Institute for Cell Biology, ETH Zuerich, Schafmattstrase 18, 8093 Zuerich, Switzerland.

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.

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Four unique features used for classifications regarding domain of life revealed by the feature selection algorithm of RF. Pairs of box-and-whisker plots are shown for each feature: Leu content, average protein size in a proteome, His content, and 10-Cys content. Box-and-whisker plots represent bacteria and archaea from top to bottom. The feature values are normalized from 0 to 1 from left to right. (+) signs represent outliers.
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Figure 1: Four unique features used for classifications regarding domain of life revealed by the feature selection algorithm of RF. Pairs of box-and-whisker plots are shown for each feature: Leu content, average protein size in a proteome, His content, and 10-Cys content. Box-and-whisker plots represent bacteria and archaea from top to bottom. The feature values are normalized from 0 to 1 from left to right. (+) signs represent outliers.

Mentions: The frequencies of highly polar and charged amino acids were among the most important features for the classification with regard to the domain of life. Among the most important features unique to this classification problem (Figure 1), bacteria appear to have significantly more His residues than archaea. In addition, the slightly increased Leu content in bacteria has been revealed as a highly discriminative feature, as well as a wider range of possible Cys content in bacteria. Finally, archaeal proteomes are characterized by a decreased protein length. In addition, among features shared between all classification problems (Additional file 4) bacteria appear to have significantly more Gln and a decreased amount of Glu residues than archaea. Although found to have slightly more charged proteomes, bacteria seem to have a decreased content of negative charges.


Proteome sequence features carry signatures of the environmental niche of prokaryotes.

Smole Z, Nikolic N, Supek F, Šmuc T, Sbalzarini IF, Krisko A - BMC Evol. Biol. (2011)

Four unique features used for classifications regarding domain of life revealed by the feature selection algorithm of RF. Pairs of box-and-whisker plots are shown for each feature: Leu content, average protein size in a proteome, His content, and 10-Cys content. Box-and-whisker plots represent bacteria and archaea from top to bottom. The feature values are normalized from 0 to 1 from left to right. (+) signs represent outliers.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3045906&req=5

Figure 1: Four unique features used for classifications regarding domain of life revealed by the feature selection algorithm of RF. Pairs of box-and-whisker plots are shown for each feature: Leu content, average protein size in a proteome, His content, and 10-Cys content. Box-and-whisker plots represent bacteria and archaea from top to bottom. The feature values are normalized from 0 to 1 from left to right. (+) signs represent outliers.
Mentions: The frequencies of highly polar and charged amino acids were among the most important features for the classification with regard to the domain of life. Among the most important features unique to this classification problem (Figure 1), bacteria appear to have significantly more His residues than archaea. In addition, the slightly increased Leu content in bacteria has been revealed as a highly discriminative feature, as well as a wider range of possible Cys content in bacteria. Finally, archaeal proteomes are characterized by a decreased protein length. In addition, among features shared between all classification problems (Additional file 4) bacteria appear to have significantly more Gln and a decreased amount of Glu residues than archaea. Although found to have slightly more charged proteomes, bacteria seem to have a decreased content of negative charges.

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute for Cell Biology, ETH Zuerich, Schafmattstrase 18, 8093 Zuerich, Switzerland.

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.

Show MeSH
Related in: MedlinePlus