Skip to main content

Advertisement

Log in

Identify Secretory Protein of Malaria Parasite with Modified Quadratic Discriminant Algorithm and Amino Acid Composition

  • Original Research Article
  • Published:
Interdisciplinary Sciences: Computational Life Sciences Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

Malaria parasite secretes various proteins in infected red blood cell for its growth and survival. Thus identification of these secretory proteins is important for developing vaccine or drug against malaria. In this study, the modified method of quadratic discriminant analysis is presented for predicting the secretory proteins. Firstly, 20 amino acids are divided into five types according to the physical and chemical characteristics of amino acids. Then, we used five types of amino acids compositions as inputs of the modified quadratic discriminant algorithm. Finally, the best prediction performance is obtained by using 20 amino acid compositions, the sensitivity of 96 %, the specificity of 92 % with 0.88 of Mathew’s correlation coefficient in fivefold cross-validation test. The results are also compared with those of existing prediction methods. The compared results shown our method are prominent in the prediction of secretory proteins.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1

Similar content being viewed by others

References

  1. Snow RW, Guerra CA, Noor AM, Myint HY, Hay SI (2005) The global distribution of clinical episodes of Plasmodium falciparum malaria. Nature 434:214–217

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Birkholtz LM, Blatch G, Coetzer TL, Hoppe HC, Human E, Morris EJ, Ngcete Z, Oldfield L, Roth R, Shonhai A, Stephens L, Louw AI (2008) Heterologous expression of plasmodial proteins for structural studies and functional annotation. Malar J 7:197. doi:10.1186/1475-2875-7-197

    Article  PubMed  PubMed Central  Google Scholar 

  3. Liu H, Yang J, Liu DQ, Shen HB, Chou KC (2007) Using a new alignment kernel function to identify secretory proteins. Protein Pept Lett 14(2):203–208

    Article  PubMed  Google Scholar 

  4. Verma R, Tiwari A, Kaur S, Varshney GC, Raghava GP (2008) Identification of proteins secreted by malaria parasite into erythrocyte using SVM and PSSM profiles. BMC Bioinf 9:201–212

    Article  Google Scholar 

  5. Zuo YC, Li QZ (2010) Using K-minimum increment of diversity to predict secretory proteins of malaria parasite based on groupings of amino acids. Amino Acids 38:859–867

    Article  CAS  PubMed  Google Scholar 

  6. Lin WZ, Fang JA, Xiao X, Chou KC (2012) Predicting secretory proteins of malaria parasite by incorporating sequence evolution information into pseudo amino acid composition via grey system model. PLoS One 7(11):e49040. doi:10.1371/journal.pone.0049040

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Garg A, Raghava GP (2008) A machine learning based method for the prediction of secretory proteins using amino acid composition, their order and similarity-search. Silico Biol 8(2):129–140

    CAS  Google Scholar 

  8. Hayakawa T, Arisue N, Udono T, Hirai H, Sattabongkot J, Toyama T, Tsuboi T, Horii T, Tanabe K (2009) Identification of Plasmodium malariae, a human malaria parasite, in imported chimpanzees. PLoS One 4:e7412

    Article  PubMed  PubMed Central  Google Scholar 

  9. Huang WL (2012) Ranking gene ontology terms for predicting non-classical secretory proteins in eukaryotes and prokaryotes. J Theor Biol 312:105–113. doi:10.1016/j.jtbi.2012.07.027

    Article  CAS  PubMed  Google Scholar 

  10. Oyelade J, Ewejobi I, Brors B, Eils R, Adebiyi E (2011) Computational identification of signalling pathways in Plasmodium falciparum. Infect Genet Evol 11:755–764

    Article  CAS  PubMed  Google Scholar 

  11. Tedder PM, Bradford JR, Needham CJ, McConkey GA, Bulpitt AJ, Westhead DR (2010) Gene function prediction using semantic similarity clustering and enrichment analysis in the malaria parasite Plasmodium falciparum. Bioinformatics 26:2431–2437

    Article  CAS  PubMed  Google Scholar 

  12. Tonkin CJ, Kalanon M, McFadden GI (2008) Protein targeting to the malaria parasite plastid. Traffic 9:166–175

    CAS  PubMed  Google Scholar 

  13. Yu L, Guo Y, Zhang Z, Li Y, Li M, Li G, Xiong W, Zeng Y (2010) SecretP: a new method for predicting mammalian secreted proteins. Peptides 31(4):574–578. doi:10.1016/j.peptides.2009.12.026

    Article  CAS  PubMed  Google Scholar 

  14. Zhang VM, Chavchich M, Waters NC (2012) Targeting protein kinases in the malaria parasite: update of an antimalarial drug target. Curr Top Med Chem 12:456–472

    Article  CAS  PubMed  Google Scholar 

  15. Ding H, Deng EZ, Yuan LF, Liu L, Lin H, Chen W, Chou KC (2014) iCTX-type: a sequence-based predictor for identifying the types of conotoxins in targeting ion channels. Biomed Res Int 2014:286419. doi:10.1155/2014/286419

    PubMed  PubMed Central  Google Scholar 

  16. Ding H, Feng PM, Chen W, Lin H (2014) Identification of bacteriophage virion proteins by the ANOVA feature selection and analysis. Mol Biosyst 10(8):2229–35. doi:10.1039/c4mb00316k

    Article  CAS  PubMed  Google Scholar 

  17. Ding H, Lin H, Chen W, Li ZQ, Guo FB, Huang J, Rao N (2014) Prediction of protein structural classes based on feature selection technique. Interdiscip Sci 6(3):235–240. doi:10.1007/s12539-013-0205-6

    Article  CAS  PubMed  Google Scholar 

  18. Liu WX, Deng EZ, Chen W, Lin H (2014) Identifying the subfamilies of voltage-gated potassium channels using feature selection technique. Int J Mol Sci 15(7):12940–12951. doi:10.3390/ijms150712940

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Yuan LF, Ding C, Guo SH, Ding H, Chen W, Lin H (2013) Prediction of the types of ion channel-targeted conotoxins based on radial basis function network. Toxicol In Vitro 27(2):852–856. doi:10.1016/j.tiv.2012.12.024

    Article  CAS  PubMed  Google Scholar 

  20. Feng PM, Chen W, Lin H, Chou KC (2013) iHSP-PseRAAAC: identifying the heat shock protein families using pseudo reduced amino acid alphabet composition. Anal Biochem 442(1):118–125. doi:10.1016/j.ab.2013.05.024

    Article  CAS  PubMed  Google Scholar 

  21. Feng PM, Ding H, Chen W, Lin H (2013) Naïve Bayes classifier with feature selection to identify phage virion proteins. Comput Math Methods Med 2013:530696. doi:10.1155/2013/530696

    PubMed  PubMed Central  Google Scholar 

  22. Feng PM, Lin H, Chen W (2013) Identification of antioxidants from sequence information using Naïve Bayes. Comput Math Methods Med 2013:567529. doi:10.1155/2013/567529

    PubMed  PubMed Central  Google Scholar 

  23. Ding H, Guo SH, Deng EZ, Yuan LF, Guo FB, Huang J, Rao NN, Chen W, Lin H (2013) Prediction of Golgi-resident protein types by using feature selection technique. Chemom Intell Lab Syst 124:9–13. doi:10.1016/j.chemolab.2013.03.005

    Article  CAS  Google Scholar 

  24. Lin H, Chen W, Yuan LF, Li ZQ, Ding H (2013) Using over-represented tetrapeptides to predict protein submitochondria locations. Acta Biotheor 61(2):259–268. doi:10.1007/s10441-013-9181-9

    Article  PubMed  Google Scholar 

  25. Lin H, Ding C, Yuan LF, Chen W, Ding H, Li ZQ, Guo FB, Huang J, Rao NN (2013) Predicting subchloroplast locations of proteins based on the general form of Chou’s pseudo amino acid composition: approached from optimal tripeptide composition. Int J Biomath 62(2):1350003

    Article  Google Scholar 

  26. Lin WZ, Fang JA, Xiao X, Chou KC (2013) iLoc-Animal: a multi-label learning classifier for predicting subcellular localization of animal proteins. Mol BioSyst 9:634–644

    Article  CAS  PubMed  Google Scholar 

  27. Feng YE (2014). Prediction of four kinds of simple super-secondary structures in protein by using chemical shifts. Sci World J (Article ID 978503), http://dx.doi.org/10.1155/2014/978503

  28. Feng YE, Luo LF (2008) Use of tetrapeptide signals for protein secondary structure prediction. Amino acids 35:607–614

    Article  CAS  PubMed  Google Scholar 

  29. Chou KC, Shen HB (2010a) Plant-mPLoc: a top-down strategy to augment the power for predicting plant protein subcellular localization. PLoS One 5:e11335

    Article  PubMed  PubMed Central  Google Scholar 

  30. Chen W, Feng PM, Lin H, Chou KC (2013) iRSpot-PseDNC: identify recombination spots with pseudo dinucleotide composition. Nucleic Acids Res 41(6):e68

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Chen W, Lin H, Feng PM, Ding C, Zuo YC, Chou KC (2012) iNuc-PhysChem: a sequence-based predictor for identifying nucleosomes via physicochemical properties. PLoS One 7:e47843

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Chen C, Shen ZB, Zou XY (2012) Dual-layer wavelet SVM for predicting protein structural class via the general form of Chou’s pseudo amino acid composition. Protein Pept Lett 19:422–429

    Article  CAS  PubMed  Google Scholar 

  33. Chou KC, Shen HB (2010b). Cell-PLoc 2. 0: an improved package of web-servers for predicting subcellular localization of proteins in various organisms. Nat Sci 2:1090–1103. doi:10.4236/ns.2010.210136 (openly accessible at http://www.scirp.org/journal/NS/)

  34. Esmaeili M, Mohabatkar H, Mohsenzadeh S (2010) Using the concept of Chou’s pseudo amino acid composition for risk type prediction of human papillomaviruses. J Theor Biol 263:203–209

    Article  CAS  PubMed  Google Scholar 

  35. Guo J, Rao N, Liu G, Yang Y, Wang G (2011) Predicting protein folding rates using the concept of Chou’s pseudo amino acid composition. J Comput Chem 32:1612–1617

    Article  CAS  PubMed  Google Scholar 

  36. Hayat M, Khan A (2012) Discriminating outer membrane proteins with fuzzy K-nearest neighbor algorithms based on the general form of Chou’s PseAAC. Protein Pept Lett 19:411–421

    Article  CAS  PubMed  Google Scholar 

  37. Xiao X, Wang P, Lin WZ, Jia JH, Chou KC (2013) iAMP-2L: a two-level multi-label classifier for identifying antimicrobial peptides and their functional types. Anal Biochem 436:168–177

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgments

The author is grateful to the anonymous reviewers for their valuable suggestions and comments, which have led to the improvement of this paper. The work was supported by National Science foundation of China (No. 31360206) and the project of “prairie excellence” engineering in Inner Mongolia and the Inner Mongolia autonomous region higher school science and technology research projects (No. NJZY067) and Basic Science of Inner Mongolia Agriculture University Research Fund (No. JC2013004).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yong-E Feng.

Appendices

Appendix 1

See Table 3.

Table 3 The classification of amino acids based on hydrophilic–hydrophobic characteristics

Appendix 2

See Table 4.

Table 4 The classification of based on acid–base properties of amino acids

Appendix 3

See Table 5.

Table 5 The classification of amino acids based on polarity

Appendix 4

See Table 6.

Table 6 The classification of amino acids based on the chemical structure of the R-base

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Feng, YE. Identify Secretory Protein of Malaria Parasite with Modified Quadratic Discriminant Algorithm and Amino Acid Composition. Interdiscip Sci Comput Life Sci 8, 156–161 (2016). https://doi.org/10.1007/s12539-015-0112-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12539-015-0112-0

Keywords

Navigation