Identifying the Mesophilic and Thermophilic Proteins from Their Amino Acid Composition with ν-Support Vector Machines

Abstract

Many researchers had proved that both single amino acid composition and dipeptide composition can influence protein thermostability. We use ν-support vector machines approach to predict hyperthermophilic protein, thermophilic protein and mesophilic protein from single amino acid composition, dipeptide composition and the combination of the two factors. For the prediction accuracies, we conclude that, single amino acid composition is suitable for prediction of mesophilic protein; dipeptide composition is suitable for prediction of hyperthermophilic protein and thermophilic protein; when considering the combination of the two factors, the prediction accuracy of hyperthermophilic protein is 84.1%, thermophilic protein is 83.4%, mesophilic protein is 84.4%, average accuracy is 84.0%. It shows that the protein thermostability can be predicted properly based on the combination of single amino acid composition and dipeptide composition. Obviously, dipeptide composition is correlative significantly to protein thermostability based on the prediction accuracies.

References

Tanaka

Sawano

Ogasahara

Sakaguchi

Bagautdinov

Katoh

Kuroishi

Shinkai

Yokoyama

Yutani

Hyper-thermostability of CutA1 protein, with a denaturation temperature of nearly 150 degrees C. FEBS Letters. 2006, 580(17), 4224–4230.

Suhre

and Claverie

J. M.

, Genomic correlates of hyperthermostability, an update, Journal of Biological Chemistry, 2003, 278(19), 17198–17202.

Cacciapuoti

Porcelli

Bertoldo

Rosa

M.D.

and Zappia

, Purification and characterization of extremely thermophilic and thermostable 5′-methylthioadenosine phosphorylase from the archaeon Sulfolobus solfataricus. Purine nucleoside phosphorylase activity and evidence for intersubunit disulfide bonds, Journal of Biological Chemistry, 1994, 269(40), 24762–24769.

Matsumura

Signor

and Matthews

B.W.

, Substantial increase of protein stability by multiple disulphide bonds, Nature, 1989, 342, 291–293.

Robinson-Rechavi

Alibes

and Godzik

, Contribution of electrostatic interactions, compactness and quaternary structure to protein thermostability: Lessons from structural genomics of Thermotoga maritime, Journal of Molecular Biology, 2006, 356(2), 547–557.

Sadeghi

Naderi-Manesh

Zarrabi

and Ranjbar

, Effective factors in thermostability of thermophilic proteins, Biophysical Chemistry, 2006, 119(3), 256–270.

Saraboji

Gromiha

M.M.

and Ponnuswamy

M.N.

, Importance of main-chain hydrophobic free energy to the stability of thermophilic proteins, International Journal of Biological Macromolecules, 2005, 35(3–4), 211–220.

Serrano

and Fersht

A.R.

, Aromatic-aromatic interactions and protein stability. Investigation by double-mutant cycles, Journal of Molecular Biology, 1991, 218, 465–475.

Vogt

Woell

and Argos

, Protein thermal stability, hydrogen bonds, and ion pairs, Journal of Molecular Biology, 1997, 269(4), 631–643.

10.

Querol

Perez-Pons

J.A.

and Mozo-Villarias

, Analysis of protein conformational characteristics related to thermostability, Protein Engineering, 1996, 9(3), 265–271.

11.

Bae

and Phillips

Jr. , Structures and analysis of highly homologous psychrophilic, mesophilic, and thermophilic adenylate kinases, Journal of Biological Chemistry, 2004, 279(27), 28202–28208.

12.

Fish

Danieli

Ohad

Nechushtai

and Livnah

, Structural basis for the thermostability of ferredoxin from the cyanobacterium Mastigocladus laminosus, Journal of Molecular Biology, 2005, 350(3), 599–608.

13.

Makhatadze

G.I.

Loladze

V.V.

Ermolenko

D.N.

Chen

and Thomas

S. T.

, Contribution of surface salt bridges to protein stability: Guidelines for protein engineering, Journal of Molecular Biology, 2003, 327(5), 1135–1148.

14.

Ding

Y.R.

Cai

Y.J.

Zhang

G.X.

and Xu

W.B.

, The influence of dipeptide composition on protein thermostability, FEBS letters, 2004, 569, 284–288.

15.

Heatwole

Soelaiman

and Shoham

, Crystal structure of a thermophilic alcohol dehydrogenase substrate complex suggests determinants of substrate specificity and thermostability, Proteins: Structure, Function, and Genetics, 1999, 37, 619–627.

16.

Matthews

B.W.

Nicholson

and Becktel

W.J.

, Enhanced protein thermostability from site-directed mutations that decrease the entropy of unfolding, Proceedings of the National Academy of Sciences, 1987, 84, 6663–6667.

17.

Moriyama

Onodera

Ko.

Sakurai

Tanaka

Kirino

Oshima

and Katsubet

, The crystal structures of mutated 3-isopropylmalate dehydrogenase from Thermus thermophilus HB8 and their relationship to the thermostability of the enzyme, The Journal of Biochemistry, 1995, 117, 408–413.

18.

Chen

and Stites

W.E.

, Replacement of staphylococcal nuclease hydrophobic core residues with those from thermophilic homologues indicates packing is improved in some thermostable proteins, Journal of Molecular Biology, 2004, 344(1), 271–280.

19.

Berezovsky

I.N.

and Shakhnovich

E.I.

, Physics and evolution of thermophilic adaptation, Proceedings of the National Academy of Sciences, 2005, 102(36), 12742–12747.

20.

Kumar

Tsai

C.J.

and Nussinov

, Factors enhancing protein thermostability, Protein Engineering, 2000, 13, 179–191.

21.

Vieilie

and Zeikus

G.J.

, Hyperthermophilic enzymes: Sources, uses, and molecular mechanisms for thermostability, Microbiology and Molecular Biology Reviews, 2001, 65, 1–43.

22.

Haney

P.J.

Badger

J.H.

Buldak

G.L.

Reich

C.I.

and Woese

C.R.

, Thermal adaptation analyzed by comparison of protein sequences from mesophilic and extremely thermophilic Methanococcus species, Proceedings of the National Academy of Sciences, 1996, 96, 3578–3583.

23.

Szilagyi

and Zabodszky

, Structural differences between mesophilic, moderately thermophilic and extremely thremophilic protein subunits: Results of a comprehensive survey, Structure, 2000, 8, 493–504.

24.

Gromiha

M.M.

Oobatake

and Sarai

, Important amino acid properties for enhanced thermostability from mesophilic to thermophilic proteins, Biophysical Chemistry, 1999, 82, 51–67.

25.

Russell

R.J.

and Taylor

G.L.

, Engineering thermostability: Lessons from thremophilic proteins, Current Opinion in Biotechnology, 1995, 6, 370–374.

26.

Ding

Y.R.

Cai

Y.J.

W.B.

, Prediction of protein thermostability from amino acid composition with v-support vector machines. Computers and applied chemistry. 2005, 22(6), 459–465.

27.

Tatusov

R.L.

Koonin

E.V.

and Lipman

D.J.

, A genomic perspective on protein families, Science, 1997, 278, 631–637.

28.

Tatusov

R.L.

Galperin

M.Y.

Natale

D.A.

and Koonin

E.V.

, The COG database: A tool for genome-scale analysis of protein functions and evolution, Nucleic. Acids Research, 2000, 28, 33–36.

29.

Tatusov

R.L.

, The COG database: New developments in phylogenetic classification of proteins from complete genomes, Nucleic Acids Research, 2001, 29, 22–28.

30.

Vapnik

, Statistical Learning Theory, Wiley-interscience, New York, 1998.

31.

Sun

Y.F.

Fan

X.D.

and Li

Y.D.

, Identifying splicing sites in eukaryotic RNA: Support vector machine approach, Computers in biology and medicine, 2003, 33, 17–29.

32.

Cai

Y.D.

Liu

X.J.

X.B.

and Chou

K.C.

, Support vector machines for the classification and prediction of β-turn types, Journal of Peptide Science, 2002, 8, 297–301.

33.

Bock

J.R.

and Gough

D.A.

, Predicting protein-protein interactions from primary structure, Bioinformatics, 2001, 17, 455–460.

34.

Hua

S.J.

and Sun

Z.R.

, A novel method of protein secondary structure prediction with high segment overlap measure: Support vector machine approach, Journal of Molecular Biology, 2001, 308, 397–407.

35.

Brown

M. P. S.

Grundy

W.N.

Lin

Cristianini

Sugnet

C. W.

Furey

T.S.

Jr , M.A. and Haussler

, Knowledge-based analysis of microarray gene expression data by using support vector machines, Proceedings of the National Academy of Sciences, 2000, 97, 262–267.

36.

Jaakkola

Diekhans

and Haussler

, Using the Fisher kernel method to detect remote protein homologies. In proceedings of the 7th international Conference on intelligent systems for molecular biology AAAi Press, Menlo Park, CA 1999.

37.

Hua

S.J.

and Sun

Z.R.

, Support vector machine approach for protein subcellular localization prediction, Bioinformatics, 2001, 17, 721–728.

38.

Scholkopf

Smola

A.J.

Williamson

R.C.

and Bartlett

P.L.

New support vector algorithms, Neural Computation, 2000, 12, 1207–1245.

39.

Hsu

C.W.

and Lin

C.J.

, A comparison of methods for multi-class support vector machines, IEEE Transactions on Neural networks, 2002, 13, 415–425.

40.

Andrey

and Rudolf

, Ion pairs and the thermotolerance of proteins from hyperthermophiles: A ‘traffic rule’ for hot roads, Trends in biochemical sciences, 2001, 26, 550–556.

41.

Yano

J.K.

and Poulos

T.L.

, New understandings of thermostable and peizostable enzymes, Current Opinion in Biotechnology, 2003, 14, 360–365.