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Protein Hypersaline Adaptation: Insight from Amino Acids with Machine Learning Algorithms

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Abstract

Traditional bioinformatics methods performed systematic comparison between the halophilic proteins and their non-halophilic homologues, to investigate the features related to hypersaline adaptation. Therefore, proposing some quantitative models to explain the sequence-characteristic relationship of halophilic proteins might shed new light on haloadaptation and help to design new biocatalysts adapt to high salt concentration. Five machine learning algorithm, including three linear and two non-linear methods were used to discriminate halophilic and their non-halophilic counterparts and the prediction accuracy was encouraging. The best prediction reliability for halophilic proteins was achieved by artificial neural network and support vector machine and reached 80 %, for non-halophilic proteins, it was achieved by linear regression and reached 100 %. Besides, the linear models have captured some clues for protein halo-stability. Among them, lower frequency of Ser in halophilic protein has not been report before.

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Fig. 1
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Abbreviations

PCA:

Principal component analysis

LR:

Linear regression

PLSR:

Partial least-square regression

ANN:

Artificial neural networks

SVM:

Support vector machine

LVs:

Latent variables

PCs:

Principal components

References

  1. Arakawa T, Tokunaqa M (2004) Protein Pept Lett 11:125–132

    Article  CAS  Google Scholar 

  2. Binder SR, Hixson C, Glossenger J (2006) Autoimmun Rev 5:234–241

    Article  CAS  Google Scholar 

  3. Britton KL, Baker PJ, Fisher M, Ruzheinikov S, Gilmour DJ, Bonete MJ, Ferrer J, Pire C, Esclapez J, Rice DW (2006) Proc Natl Acad Sci USA 103:4846–4851

    Article  CAS  Google Scholar 

  4. Brown K, Nurizzo D, Besson S, Shepard W, Moura J, Moura I, Tegoni M, Cambillau C (1999) J Mol Biol 289:1017–1028

    Article  CAS  Google Scholar 

  5. Chou KC (2009) Curr Proteomics 6:262–274

    Article  CAS  Google Scholar 

  6. Coquelle N, Talon R, Juers DH, Girard E, Kahn R, Madern D (2010) J Mol Biol 404:493–505

    Article  CAS  Google Scholar 

  7. Duttaa D, Mohanty AK, Choudhury RK, Chand P (1998) Nucl Instrum Meth in Phy Res A 404:445–454

    Article  Google Scholar 

  8. Ebrahimie E, Ebrahimi M, Rahpayma NS, Ebrahimi M (2011) Saline Systems 7:1

    Article  CAS  Google Scholar 

  9. Elcock AH, McCammon JA (1998) J Mol Biol 280:731–748

    Article  CAS  Google Scholar 

  10. Ferrer J, Perez-Pomares F, Bonete MJ (1996) FEMS Microbil Lett 141:59–63

    Article  CAS  Google Scholar 

  11. Fukuchi S, Yoshimune K, Wakayama M, Moriguchi M, Nishikawa K (2003) J Mol Biol 327:347–357

    Article  CAS  Google Scholar 

  12. Geladi P, Kowalski BR (1986) Anal Chim Acta 185:1–17

    Article  CAS  Google Scholar 

  13. Gromiha MM, Yabuki Y (2008) BMC Bioinform 9:135

    Article  Google Scholar 

  14. Hemmateenejad B, Safarpour MA, Miri R, Nesari N (2005) J Chem Inf Model 45:190–199

    Article  CAS  Google Scholar 

  15. Imamoto Y, Kataoka M (2007) Photochem Photobiol 83:40–49

    Article  CAS  Google Scholar 

  16. Inamdar NM, Ehrlich KC, Ehrlich M, Iannello RC, Frank E, Hall K, Trigg L, Holmes G, Witten IH (2004) Bioinformatics 20:2479–2481

    Article  Google Scholar 

  17. Joo WA, Kim CW (2005) J Chromatogr B Analyt Technol Biomed Life Sci 815:237–250

    Article  CAS  Google Scholar 

  18. Karan R, Capes MD, Dassarma S (2012) Aquat Biosyst 8:4

    Article  CAS  Google Scholar 

  19. Kastritis PL, Papandreou NC, Hamodrakas SJ (2007) Int J Biol Macromol 41:447–453

    Article  CAS  Google Scholar 

  20. Kennedy SP, Ng WV, Salzberg SL, Hood L, DasSarma S (2001) Genome Res 11:1641–1650

    Article  CAS  Google Scholar 

  21. Lanyi JK (1974) Bacteriol Rev 38:272–290

    CAS  Google Scholar 

  22. Liew AWC, Yan H, Yang M (2005) Pattern Recog 38:2055–2073

    Article  Google Scholar 

  23. Ma JC, Dougherty DA (1997) Chem Rev 97:1303–1324

    Article  CAS  Google Scholar 

  24. Mevarech M, Frolow F, Gloss LM (2000) Biophys Chem 86:155–164

    Article  CAS  Google Scholar 

  25. Mongodin EF, Nelson KE, Daugherty S, Deboy RT, Wister J, Khouri H, Weidman J, Walsh DA, Papke RT, Sanchez Perez G, Sharma AK, Nesbo CL, MacLeod D, Bapteste E, Doolittle WF, Charlebois RL, Legault B, Rodriguez-Valera F (2005) Proc Natl Acad Sci USA 102:18147–18152

    Article  CAS  Google Scholar 

  26. Paul S, Bag SK, Das S, Harvill ET, Dutta C (2008) Genome Biol 9:R70

    Article  Google Scholar 

  27. Schlessinger A, Rost B (2005) Proteins 61:115–126

    Article  CAS  Google Scholar 

  28. Siglioccolo A, Paiardini A, Piscitelli M, Pascarella S (2011) BMC Structur Biol 11:50

    Article  CAS  Google Scholar 

  29. Tokunaga H, Arakawa T, Tokunaga M (2008) Protein Sci 17:1603–1610

    Article  CAS  Google Scholar 

Download references

Acknowledgments

This work was supported by the Cultivation Project of Huaqiao University for the China National Funds for Distinguished Young Scientists (No. JB-GJ1006) and the Program for New Century Excellent Talents in Universities of Fujian Province (No. 07176C02).

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Authors and Affiliations

  1. Department of Bioengineering and Biotechnology, Huaqiao University, Xiamen, 361021, Fujian, People’s Republic of China

    Guangya Zhang & Huihua Ge

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  1. Guangya Zhang
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  2. Huihua Ge
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Correspondence to Guangya Zhang.

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Zhang, G., Ge, H. Protein Hypersaline Adaptation: Insight from Amino Acids with Machine Learning Algorithms. Protein J 32, 239–245 (2013). https://doi.org/10.1007/s10930-013-9484-3

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