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Proteome sequence features carry signatures of the environmental niche of prokaryotes

机译:蛋白质组序列特征带有原核生物环境位的特征

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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.
机译:背景原核环境适应发生在细胞内的不同水平,以确保保留基因组完整性,适当的蛋白质折叠和功能以及膜流动性。尽管已经提出了适用于各种极端条件的细胞成分的特定组成和结构,但尚未进行描述此类适应性的系统研究。因此,我们探讨了是否可以从其蛋白质组序列推论出原核生物的环境生态位。最后,我们旨在发现来自不同环境的原核生物的蛋白质组序列之间的精确差异。结果我们分析了来自不同生境的192种原核生物的蛋白质组。我们收集了有关每种微生物最佳生长条件的详细信息。此外,我们选择了42种氨基酸的理化性质,并计算了每种蛋白质组的值。此外,在同一组功能上,我们应用了两种根本不同的机器学习方法,即支持向量机和随机森林,以成功地对细菌和古细菌,嗜盐菌和非嗜盐菌,以及嗜温菌,嗜热菌和嗜温菌进行分类。最后,我们使用随机森林进行了特征选择。结论据我们所知,这是首次成功用相同的42个特征集成功进行了三种不同分类的蛋白质组适应案例(生命域,嗜盐性和嗜热性)。特定适应的特征构成签名,可以帮助理解适应极端环境的机制。

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