Research Papers: Selected Papers from MCCMB2005No Access

TREND OF AMINO ACID COMPOSITION OF PROTEINS OF DIFFERENT TAXA

Institute of Protein Research, Russian Academy of Sciences, Institutskaya str., 4, Pushchino, Moscow Region, 142290, Russia

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ALEXEI V. FINKELSTEIN

Institute of Protein Research, Russian Academy of Sciences, Institutskaya str., 4, Pushchino, Moscow Region, 142290, Russia

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, and

OXANA V. GALZITSKAYA

Institute of Protein Research, Russian Academy of Sciences, Institutskaya str., 4, Pushchino, Moscow Region, 142290, Russia

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https://doi.org/10.1142/S0219720006002016Cited by:78 (Source: Crossref)

Abstract

Archaea, bacteria and eukaryotes represent the main kingdoms of life. Is there any trend for amino acid compositions of proteins found in full genomes of species of different kingdoms? What is the percentage of totally unstructured proteins in various proteomes? We obtained amino acid frequencies for different taxa using 195 known proteomes and all annotated sequences from the Swiss–Prot data base. Investigation of the two data bases (proteomes and Swiss–Prot) shows that the amino acid compositions of proteins differ substantially for different kingdoms of life, and this difference is larger between different proteomes than between different kingdoms of life. Our data demonstrate that there is a surprisingly small selection for the amino acid composition of proteins for higher organisms (eukaryotes) and their viruses in comparison with the "random" frequency following from a uniform usage of codons of the universal genetic code. On the contrary, lower organisms (bacteria and especially archaea) demonstrate an enhanced selection of amino acids. Moreover, according to our estimates, 12%, 3% and 2% of the proteins in eukaryotic, bacterial and archaean proteomes are totally disordered, and long (> 41 residues) disordered segments are found to occur in 16% of arhaean, 20% of eubacterial and 43% of eukaryotic proteins for 19 archaean, 159 bacterial and 17 eukaryotic proteomes, respectively. A correlation between amino acid compositions of proteins of various taxa, show that the highest correlation is observed between eukaryotes and their viruses (the correlation coefficient is 0.98), and bacteria and their viruses (the correlation coefficient is 0.96), while correlation between eukaryotes and archaea is 0.85 only.

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References

S. Karlin, J. Mrazek and A. J. Gentles, Curr. Opin. Struct. Biol. 13, 344 (2003), DOI: 10.1016/S0959-440X(03)00073-3. Crossref, Medline, Google Scholar
Pe'eret al., Proteins 54, 20 (2004), DOI: 10.1002/prot.10559. Crossref, Medline, Google Scholar
I. K. Jordanet al., Nature 433, 633 (2005), DOI: 10.1038/nature03306. Crossref, Medline, Google Scholar
A. Bairoch and R. Apweiler, Nucleic Acids Res. 28, 45 (2000), DOI: 10.1093/nar/28.1.45. Crossref, Medline, Google Scholar
P. E. Wright and H. J. Dyson, J. Mol. Biol. 293, 321 (1999), DOI: 10.1006/jmbi.1999.3110. Crossref, Medline, Google Scholar
H. J. Dyson and P. E. Wright, Adv. Protein Chem. 62, 311 (2002). Crossref, Medline, Google Scholar
J. C. Wootton, Comput. Chem. 18, 269 (1994), DOI: 10.1016/0097-8485(94)85023-2. Crossref, Medline, Google Scholar
A. K. Dunkeret al., Protein disorder and the evolution of molecular recognition: theory, predictions and observations, Pac. Symp. Biocomput.3 (1998) pp. 473–484. Google Scholar
P. Romeroet al., Thousands of proteins likely to have long disordered regions, Pac. Symp. Biocomput. (1998) pp. 437–448. Google Scholar
P. Romero, Z. Obradovic and A. K. Dunker, FEBS Lett. 462, 363 (1999), DOI: 10.1016/S0014-5793(99)01557-4. Crossref, Medline, Google Scholar
O. V. Galzitskaya, A. K. Surin and H. Nakamura, Protein Sci. 9, 580 (2000). Crossref, Medline, Google Scholar
S. Vuceticet al., Proteins 52, 573 (2003), DOI: 10.1002/prot.10437. Crossref, Medline, Google Scholar
Z. Obradovicet al., Proteins 53, 566 (2003), DOI: 10.1002/prot.10532. Crossref, Medline, Google Scholar
P. Radivojacet al., Protein Sci. 13, 71 (2004), DOI: 10.1110/ps.03128904. Crossref, Medline, Google Scholar
V. N. Uversky, J. R. Gillespie and A. L. Fink, Proteins 41, 415 (2000). Crossref, Medline, Google Scholar
V. N. Uversky, Eur. J. Biochem. 269, 2 (2002), DOI: 10.1046/j.0014-2956.2001.02649.x. Crossref, Medline, Google Scholar
S. O. Garbuzynskiy, M. Y. Lobanov and O. V. Galzitskaya, Protein Sci. 13, 2871 (2004), DOI: 10.1110/ps.04881304. Crossref, Medline, Google Scholar
D. J. Hudson , Statistics. Lectures on elementary statistics and probability ( Geneva , 1964 ) . Google Scholar
F. H. C. Crick, Cold Spring Harbor Symp. Quant Biol. 31, 1 (1996). Medline, Google Scholar
F. Takeuchiet al., J. Theor. Biol. 222, 139 (2003), DOI: 10.1016/S0022-5193(03)00021-3. Crossref, Medline, Google Scholar
Z. Dosztanyiet al., J. Mol. Biol. 347, 827 (2005), DOI: 10.1016/j.jmb.2005.01.071. Crossref, Medline, Google Scholar
J. J. Wardet al., J. Mol. Biol. 337, 635 (2004), DOI: 10.1016/j.jmb.2004.02.002. Crossref, Medline, Google Scholar
R. Lindinget al., Structure 11, 1453 (2003), DOI: 10.1016/j.str.2003.10.002. Crossref, Medline, Google Scholar
O. V. Galzitskaya, S. O. Garbuzynskiy and M. Y. Lobanov, Mol. Biol. (Russia) 2, 341 (2006). Google Scholar