Low Contents of Carbon and Nitrogen in Highly Abundant Proteins: Evidence of Selection for the Economy of Atomic Composition
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Proteins that assimilate particular elements were found to avoid using amino acids containing the element, which indicates that the metabolic constraints of amino acids may influence the evolution of proteins. We suspected that low contents of carbon, nitrogen, and sulfur may also be selected for economy in highly abundant proteins that consume large amounts of the resources of cells. By analyzing recently available proteomic data in Escherichia coli, Saccharomyces cerevisiae, and Schizosaccharomyces pombe, we found that at least the carbon and nitrogen contents in amino acid side chains are negatively correlated with protein abundance. An amino acid with a high number of carbon atoms in its side chain generally requires relatively more energy for its synthesis. Thus, it may be selected against in highly abundant proteins either because of economy in building blocks or because of economy in energy. Previous studies showed that highly abundant proteins preferentially use cheap (in terms of energy) amino acids. We found that the carbon content is still negatively correlated with protein abundance after controlling for the energetic cost of the amino acids. However, the negative correlation between protein abundance and energetic cost disappeared after controlling for carbon content. Building blocks seem to be more restricted than energy. It seems that the amino acid sequences of highly abundant proteins have to compromise between optimization for their biological functions and reducing the consumption of limiting resources. By contrast, the amino acid sequences of weakly expressed proteins are more likely to be optimized for their biological functions.
KeywordsAtomic content Energetic cost Amino acid usage Resource availability Protein abundance Protein turnover rate
We thank anonymous reviewers for very valuable comments on the manuscript and Xiaomei Wu, Jie Guo, and Yi-Fei Huang for their help. This study was supported by Program NCET-07-0094 and Beijing Normal University.
- Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G (2000) Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nature Genet 25:25–29PubMedCrossRefGoogle Scholar
- Hong EL, Balakrishnan R, Dong Q, Christie KR, Park J, Binkley G, Costanzo MC, Dwight SS, Engel SR, Fisk DG, Hirschman JE, Hitz BC, Krieger CJ, Livstone MS, Miyasato SR, Nash RS, Oughtred R, Skrzypek MS, Weng S, Wong ED, Zhu KK, Dolinski K, Botstein D, Cherry JM (2008) Gene Ontology annotations at SGD: new data sources and annotation methods. Nucleic Acids Res 36:D577–D581PubMedCrossRefGoogle Scholar
- Kolkman A, Daran-Lapujade P, Fullaondo A, Olsthoorn MMA, Pronk JT, Slijper M, Heck AJR (2006) Proteome analysis of yeast response to various nutrient limitations. Mol Syst Biol 2:2006.0026Google Scholar