The Protein Journal

, Volume 29, Issue 6, pp 427–431 | Cite as

Prediction of Lipid-Binding Sites Based on Support Vector Machine and Position Specific Scoring Matrix



Lipid–protein interactions play a vital role in various biological processes, which are involved in cellular functions and can affect the stability, folding and the function of peptides and proteins. In this study, a sequence-based method by using support vector machine and position specific scoring matrix (PSSM) was proposed to predict lipid-binding sites. Considering the influence of surrounding residues of one amino acid, a sliding window was chosen to encode the PSSM profiles. By incorporating the evolutionary information and the local features of residues surrounding one lipid-binding site, the method yielded a high accuracy of 80.86% and the Matthew’s Correlation Coefficient of 0.58 by using fivefold cross validation test. The good result indicates the applicability of the method.


Lipid–protein interactions Lipid-binding sites Position specific scoring matrix Support vector machine 



Support vector machine


Position-specific scoring matrices


Radial basis function


Position specific iterated-basic local alignment search tool


Receiver operating characteristic


Area under curve



The authors gratefully thank Yaping Fang for sharing the sliding window code. The authors would like to express their cordial thanks to the unknown reviewers for providing comments on the manuscript. This work was supported by the National Natural Science Foundation of China (Nos. 20905054, 20972103).


  1. 1.
    Ahmad S, Sarai A (2005) BMC Bioinformatics 6:33CrossRefGoogle Scholar
  2. 2.
    Altschul SF, Madden TL, Schaffer AA, Zhang JH, Zhang Z, Miller W, Lipman DJ (1997) Nucleic Acids Res 25(17):3389–3402CrossRefGoogle Scholar
  3. 3.
    Balla T (2005) J Cell Sci 118(10):2093–2104CrossRefGoogle Scholar
  4. 4.
    Bernlohr DA, Simpson MA, Hertzel AV, Banaszak LJ (1997) Annu Rev Nutr 17:277–303CrossRefGoogle Scholar
  5. 5.
    Cai YD, Feng KY, Li YX, Chou KC (2003) Peptides 24(4):629–630CrossRefGoogle Scholar
  6. 6.
    Cai YD, Liu XJ, Xu XB, Chou KC (2002) J Comput Chem 23(2):267–274CrossRefGoogle Scholar
  7. 7.
    Cheng CW, Su ECY, Hwang JK, Sung TY, Hsu WL (2008) BMC Bioinformatics 9:S6CrossRefGoogle Scholar
  8. 8.
    Chou KC, Cai YD (2002) J Biol Chem 277(48):45765–45769CrossRefGoogle Scholar
  9. 9.
    Christie WW (1989) High-performance liquid chromatography and lipids: a practical guide. The Oily Press Ltd, Ayrshire, 42 ppGoogle Scholar
  10. 10.
    Findlay HE, Booth PJ (2006) J Phys Condens Matter 18(28):S1281–S1291CrossRefGoogle Scholar
  11. 11.
    Fonteh AN, Harrington RJ, Huhmer AF, Biringer RG, Riggins JN, Harrington MG (2006) Dis Markers 22(1–2):39–64Google Scholar
  12. 12.
    Glatz JFC (2007) Mol Cell Biochem 299(1–2):1–3CrossRefGoogle Scholar
  13. 13.
    Gross RW, Jenkins CM, Yang JY, Mancuso DJ, Han XL (2005) Prostaglandins Other Lipid Mediat 77(1–4):52–64CrossRefGoogle Scholar
  14. 14.
    Hunte C, Richers S (2008) Curr Opin Struct Biol 18(4):406–411CrossRefGoogle Scholar
  15. 15.
    Jones DT (1999) J Mol Biol 292(2):195–202CrossRefGoogle Scholar
  16. 16.
    Kaur H, Raghava GPS (2003) Protein Sci 12(3):627–634CrossRefGoogle Scholar
  17. 17.
    Kumar M, Gromiha AM, Raghava GPS (2008) Proteins 71(1):189–194CrossRefGoogle Scholar
  18. 18.
    Kuznetsov IB, Gou ZK, Li R, Hwang SW (2006) Proteins 64(1):19–27CrossRefGoogle Scholar
  19. 19.
    Lelliott CJ, Ljungberg A, Ahnmark A, William-Olsson L, Ekroos K, Elmgren A, Arnerup G, Shoulders CC, Oscarsson J, Linden D (2007) Arterioscler Thromb Vasc Biol 27(12):2707–2713CrossRefGoogle Scholar
  20. 20.
    Lin HH, Han LY, Zhang HL, Zheng CJ, Xie B, Chen YZ (2006) J Lipid Res 47(4):824–831CrossRefGoogle Scholar
  21. 21.
    Matthews BW (1975) Biochim Biophys Acta 405(2):442–451Google Scholar
  22. 22.
    Niggli V (2001) Trends Biochem Sci 26(10):604–611CrossRefGoogle Scholar
  23. 23.
    Pebay-Peyroula E, Rosenbusch JP (2001) Curr Opin Struct Biol 11(4):427–432CrossRefGoogle Scholar
  24. 24.
    Piomelli D (2005) Prostaglandins Other Lipid Mediat 77(1–4):23–34CrossRefGoogle Scholar
  25. 25.
    Scott DL, Diez G, Goldmann WH (2006) Theor Biol Med Model 3:17CrossRefGoogle Scholar
  26. 26.
    Su X, Han XL, Mancuso DJ, Abendschein DR, Gross RW (2005) Biochemistry 44(13):5234–5245CrossRefGoogle Scholar
  27. 27.
    Tempel M, Goldmann WH, Isenberg G, Sackmann E (1995) Biophys J 69(1):228–241CrossRefGoogle Scholar
  28. 28.
    Tomiki Y, Suda S, Tanaka M, Okuzawa A, Matsuda M, Ishibiki Y, Sakamoto K, Kamano T, Tsurumaru M, Watanabe Y (2004) J Exp Clin Cancer Res 23(2):233–240Google Scholar
  29. 29.
    Vapnik V (1998) Statistical learning theory. Wiley, New YorkGoogle Scholar
  30. 30.
    Wang L, Irausquin SJ, Yang JY (2008) Int J Comput Biol Drug Des 1(1):14–25CrossRefGoogle Scholar
  31. 31.
    Zhu C, Hu P, Liang QL, Wang YM, Luo GA (2009) Chin J Anal Chem 37(9):1390–1396CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.College of ChemistrySichuan UniversityChengduChina

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