Proteome sequence features carry signatures of the environmental niche of prokaryotes.
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.
Institute for Cell Biology, ETH Zuerich, Schafmattstrase 18, 8093 Zuerich, Switzerland.
Zlatko
Z
Smole
Nela
N
Nikolic
Fran
F
Supek
Tomislav
T
Šmuc
Ivo F
IF
Sbalzarini
Anita
A
Krisko
D000220:Adaptation, Biological; D000465:Algorithms; D001105:Archaea / Q000145:classification / Q000378:metabolism*; D001185:Artificial Intelligence; D001419:Bacteria / Q000145:classification / Q000378:metabolism*; D019295:Computational Biology / Q000379:methods; D017753:Ecosystem; D020543:Proteome / Q000378:metabolism*
D020543:Proteome
Proc Natl Acad Sci U S A. 2004 Jun 1;101(22):8257-8
15159533
Environ Microbiol. 2004 Dec;6(12):1287-91
15560825
Mol Biol Evol. 2007 May;24(5):1181-9
17331957
Crit Rev Microbiol. 2007;33(3):183-209
17653987
Int J Biol Macromol. 2007 Oct 1;41(4):447-53
17675150
PLoS Comput Biol. 2008 Oct;4(10):e1000173
18974822
Proteins. 2004 Jan 1;54(1):20-40
14705021
Psychol Methods. 2009 Dec;14(4):323-48
19968396
Gene. 2002 Sep 4;297(1-2):51-60
12384285
BMC Genomics. 2004 Jan 12;5(1):3
14718067
J Mol Biol. 2007 Feb 16;366(2):449-60
17174328
Trends Genet. 2000 Mar;16(3):107-9
10689349
J Mol Biol. 2003 Mar 21;327(2):347-57
12628242
Nucleic Acids Res. 2005 Jun 10;33(10):3390-400
15951512
Structure. 1997 May 15;5(5):635-46
9195883
Proc Natl Acad Sci U S A. 2005 May 3;102(18):6395-400
15851683
J Mol Biol. 2001 Jun 15;309(4):835-43
11399062
Electrophoresis. 1994 Mar-Apr;15(3-4):529-39
8055880
Nat Rev Microbiol. 2006 May;4(5):331-43
16715049
J Mol Biol. 1997 May 30;269(1):154-64
9193007
PLoS Comput Biol. 2007 Jan 12;3(1):e5
17222055
Nucleic Acids Res. 2009 Jan;37(Database issue):D471-8
18849571
Res Microbiol. 1995 Feb;146(2):113-20
7652205
Nat Biotechnol. 2006 Dec;24(12):1565-7
17160063
BMC Genomics. 2006 Dec 05;7:307
17147802
Bioinformatics. 2007 Sep 1;23(17):2231-8
17599925
Nucleic Acids Res. 2001 Apr 1;29(7):1608-15
11266564
Bacteriol Rev. 1974 Sep;38(3):272-90
4607500
Protein Sci. 2006 Jul;15(7):1569-78
16815912
Genome Res. 2001 Oct;11(10):1641-50
11591641
Funct Integr Genomics. 2000 May;1(1):76-88
11793224
Genome Biol. 2004;5(10):117
15461805
Nucleic Acids Res. 1990 Feb 11;18(3):507-11
2155402
Biochemistry. 1981 Nov 10;20(23):6536-43
6796115