Abstract
We have analysed 108 bacterial proteomes in the KEGG database to explore the variation of amino acid composition with respect to protein function. The ratio between the observed amino acid composition and that predicted based on mononucleotide composition was calculated for each functional category. This indicated whether the compositional variation arose from mutation or selection pressure. The results showed that charged amino acids (Lys, Arg and Glu), were found more frequently than expected in proteins involved in genetic information processing (i.e. transcription, translation, etc.) Similarly, in the proteins involved in processing environmental information (e.g. signal transduction), the hydrophobic amino acid Leu was found in excess of values expected from the base composition in the genes.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Akashi H (2001) Gene expression and molecular evolution. Curr Opin Genet Dev 11:660–666
Akashi H, Gojobori T (2002) Metabolic efficency and amino acid composition in the proteomes of Escherichia Coli and Bacillus subtilis. Proc Natl Acad Sci USA 99:3695–3700
Bharanidharan D, Bhargavi GR, Uthanumallian K, Gautham N (2004) Correlations between nucleotide frequencies and amino acid composition in 115 bacterial species. Biochem Biophys Res Commun 315:1097–1103
Dumontier M, Michalickova K, Hogue CWV (2002) Species-specific protein sequence and fold optimizations. BMC Bioinformatics 3:39–54
Farias ST, Bonato MCM (2003) Preferred amino acids and thermostability. Genet Mol Res 2:383–393
Kanehisa M, Goto S (2000) KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28:27–30
Knight CG, Kassen R, Hebestreit H, Rainey PB (2004) Global analysis of predicted proteomes: Functional adaptation of physical properties. Proc Natl Acad Sci USA 101:8390–8395
Knight RD, Freeland SJ, Landweber LF (2001) A simple model based on mutation and selection explains trends in codon and amino-acid usage and GC composition within and across genomes. Genome Biol 2:research0010.1–0010.13
Kreil DP, Ouzounis CA (2001) Identification of thermophilic species by the amino acid compositions deduced from their genomes. Nucleic Acids Res 29:1608–1615
Kyte J, Doolittle RF (1982) A simple method for displaying the hydropathic character of a protein. J Mol Biol 157:105–132
Lobry JR (1997) Influence of genomic G+C content on average amino-acid composition of proteins from 59 bacterial species. Gene 205:309–316
Lobry JR, Chessel D (2003) Internal correspondence analysis of codon and amino-acid usage in thermophilic bacteria. J Appl Genet 44:235–261
Nandi T, Kannan K, Ramachandran S (2003) Species and strain-specific patterns of low-complexity proteins in Escherichia and Mycobacteria. Curr Sci 85:185–187
Peer I, Felder CE, Man O, Silman I, Sussman JL, Beckmann JS (2004) Proteomic signatures: Amino acid and oligopeptide compositions differentiate among phyla. Proteins Struct, Funct Bioinform 54:20–40
Schwartz R, Ting CS, King J (2001) Whole proteome pI values correlate with subcellular localizations of proteins for organisms within the three domains of life. Genome Res 11:703–709
Singer GAC, Hickey DA (2000) Nucleotide bias causes a genomewide bias in the amino acid composition of proteins. Mol Biol Evol 17:1581–1588
Slesarev AI, Mezhevaya KV, Makarova KS, Polushin NN, Shcherbinina OV, Shakhova VV, Belova GI, Aravind L, Natale DA, Rogozin IB, Tatusov RL, Wolf YI, Stetter KO, Malykh AG, Koonin EV, Kozyavkin SA (2002) The complete genome of hyperthermophile Methanopyrus kandleri AV19 and monophyly of archaeal methanogens. Proc Natl Acad Sci USA 99:4644–4649
Sueoka N (1961) Correlation between base composition of deoxyribonucleic acid and amino acid composition and protein, Proc Natl Acad Sci USA 47:1141–1149
Tekaia F, Yeramian E, Dujon B (2002) Amino acid composition of genomes, lifestyles of organisms, and evolutionary trends: A global picture with correspondence analysis. Gene 297:51–60
Acknowledgements
We acknowledge the financial assistance from the following organisations of the Government of India: CSIR under the NMITLI project, UGC under the SAP programme, and DST under the FIST programme. We are grateful to the anonymous referee who has contributed immensely to the interpretation and presentation of these results.
Author information
Authors and Affiliations
Corresponding author
Electronic supplementary material
Rights and permissions
About this article
Cite this article
Bharanidharan, D., Gautham, N. Amino acid variation in cellular processes in 108 bacterial proteomes. Arch Microbiol 184, 168–174 (2005). https://doi.org/10.1007/s00203-005-0034-z
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00203-005-0034-z