Molecular Diversity

, Volume 15, Issue 1, pp 149–155 | Cite as

Quat-2L: a web-server for predicting protein quaternary structural attributes

Full Length Paper

Abstract

By hybridizing the functional-domain and sequence-correlated pseudo amino acid composition approaches, a 2-layer predictor called “Quat-2L” was developed for predicting the quaternary structural attribute of a protein according to its sequence information alone. The 1st layer is to identify the query protein as monomer, homo-oligomer, or hetero-oligomer. If the result thus obtained turns out to be homo-oligomer or hetero-oligomer, then the prediction will be automatically continued to further identify it belonging to one of the following six subtypes: (1) dimer, (2) trimer, (3) tetramer, (4) pentamer, (5) hexamer, and (6) octamer. The overall success rate of Quat-2L for the 1st layer identification was 71.14%; while the overall success rates of the 2nd layer for homo-oligomers and hetero-oligomers were 76.91 and 82.52%, respectively. These rates were derived by the jackknife cross-validation tests on the stringent benchmark data set in which none of proteins has ≥60% pairwise sequence identity to any other in the same subset. As a web-server, Quat-2L is freely accessible to the public via http://icpr.jci.jx.cn/bioinfo/Quat-2L, where one can get 2-level results in about 15 s.

Keywords

SMART Function domain composition Pseudo amino acid composition Complexity measure factor Fuzzy K nearest neighbor 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Supplementary material

11030_2010_9227_MOESM1_ESM.doc (131 kb)
ESM 1 (DOC 131 kb)
11030_2010_9227_MOESM2_ESM.pdf (2 mb)
ESM 2 (PDF 2,035 kb)

References

  1. 1.
    Voet D, Voet JG (1995) Biochemistry, 2. Wiley, New York, pp 180–185Google Scholar
  2. 2.
    Xiao X, Wang P, Chou KC (2009) Predicting protein quaternary structural attribute by hybridizing functional domain composition and pseudo amino acid composition. J Appl Crystallogr 42: 169–173. doi:10.1107/S0021889809002751 CrossRefGoogle Scholar
  3. 3.
    Schnell JR, Chou JJ (2008) Structure and mechanism of the M2 proton channel of influenza A virus. Nat 451: 591–595. doi:10.1038/nature06531 CrossRefGoogle Scholar
  4. 4.
    Chou KC (1988) Review: Low-frequency collective motion in biomacromolecules and its biological functions. Biophys Chem 30: 3–48. doi:10.1016/0301-4622(88)85002-6 PubMedCrossRefGoogle Scholar
  5. 5.
    Garian R (2001) Prediction of quaternary structure from primary structure. Bioinformatics 17: 551–556. doi:10.1093/bioinformatics/17.6.551 PubMedCrossRefGoogle Scholar
  6. 6.
    Chou KC, Cai YD (2003) Predicting protein quaternary structure by pseudo amino acid composition. Proteins: Struct Funct Genet 53: 282–289. doi:10.1002/prot.10500 CrossRefGoogle Scholar
  7. 7.
    Zhang SW, Pan Q, Zhang HC, Zhang YL, Wang HY (2003) Classification of protein quaternary structure with support vector machine. Bioinformatics 19: 2390–2396. doi:10.1093/bioinformatics/btg331 PubMedCrossRefGoogle Scholar
  8. 8.
    Zhang SW, Pan Q, Zhang HC, Shao ZC, Shi JY (2006) Prediction protein homo-oligomer types by pseudo amino acid composition: Approached with an improved feature extraction and naive Bayes feature fusion. Amino Acids 30: 461–468. doi:10.1007/s00726-006-0263-8 PubMedCrossRefGoogle Scholar
  9. 9.
    Zhang SW, Chen W, Yang F, Pan Q (2008) Using Chou’s pseudo amino acid composition to predict protein quaternary structure: a sequence-segmented PseAAC approach. Amino Acids 35: 591–598. doi:10.1007/s00726-008-0086-x PubMedCrossRefGoogle Scholar
  10. 10.
    Carugo O (2007) A structural proteomics filter: prediction of the quaternary structural type of hetero-oligomeric proteins on the basis of their sequences. Appl Crystallogr 40: 986–989. doi:10.1107/S0021889807041076 CrossRefGoogle Scholar
  11. 11.
    Chou KC, Cai YD (2002) Using functional domain composition and support vector machines for prediction of protein subcellular location. J Biol Chem 277: 45765–45769. doi:10.1074/jbc.M204161200 PubMedCrossRefGoogle Scholar
  12. 12.
    Chou KC (2001) Prediction of protein cellular attributes using pseudo amino acid composition. Proteins: Struct Funct Genet (Erratum: ibid, 2001, vol 44, 60) 43: 246–255. doi:10.1002/prot.1035 CrossRefGoogle Scholar
  13. 13.
    Chou KC (2009) Pseudo amino acid composition and its applications in bioinformatics, proteomics and system biology. Curr Proteom 6: 262–274. doi:10.2174/157016409789973707 CrossRefGoogle Scholar
  14. 14.
    Chou KC, Shen HB (2009) Review: recent advances in developing web-servers for predicting protein attributes. Nat Sci 2:63–92. http://www.scirp.org/journal/NS/. doi:10.4236/ns.2009.12011
  15. 15.
    Li W, Godzik A (2006) Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22: 1658–1659. doi:10.1093/bioinformatics/btl158 PubMedCrossRefGoogle Scholar
  16. 16.
    Chou KC, Shen HB (2007) Review: recent progresses in protein subcellular location prediction. Anal Biochem 370: 1–16. doi:10.1016/j.ab.2007.07.006 PubMedCrossRefGoogle Scholar
  17. 17.
    Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller W et al (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 25: 3389–3402. doi:gka562 [pii] PubMedCrossRefGoogle Scholar
  18. 18.
    Chou KC (1995) A novel approach to predicting protein structural classes in a (20-1)-D amino acid composition space. Proteins: Struct Funct Genet 21: 319–344. doi:10.1002/prot.340210406 CrossRefGoogle Scholar
  19. 19.
    Tatusov RL, Fedorova ND, Jackson JD, Jacobs AR, Kiryutin B, Koonin EV et al (2003) The COG database: an updated version includes eukaryotes. BMC Bioinform 4: 41. doi:10.1186/1471-2105-4-41 CrossRefGoogle Scholar
  20. 20.
    Finn RD, Mistry J, Schuster-Bockler B, Griffiths-Jones S, Hollich V, Lassmann T et al (2006) Pfam: clans, web tools and services. Nucleic Acids Res 34: D247–D251. doi:10.1093/nar/gkj149 PubMedCrossRefGoogle Scholar
  21. 21.
    Letunic I, Copley RR, Pils B, Pinkert S, Schultz J, Bork P (2006) SMART 5: domains in the context of genomes and networks. Nucleic Acids Res 34: D257–D260. doi:10.1093/nar/gkj079 PubMedCrossRefGoogle Scholar
  22. 22.
    Marchler-Bauer A, Anderson JB, Derbyshire MK, DeWeese-Scott C, Gonzales NR, Gwadz M et al (2007) CDD: a conserved domain database for interactive domain family analysis. Nucleic Acids Res 35: D237–D240. doi:10.1093/nar/gkl951 PubMedCrossRefGoogle Scholar
  23. 23.
    Chou KC, Shen HB (2008) ProtIdent: a web server for identifying proteases and their types by fusing functional domain and sequential evolution information. Biochem Biophys Res Commun 376: 321–325. doi:10.1016/j.bbrc.2008.08.125 PubMedCrossRefGoogle Scholar
  24. 24.
    Chou KC (2005) Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes. Bioinformatics 21: 10–19. doi:10.1093/bioinformatics/bth466 PubMedCrossRefGoogle Scholar
  25. 25.
    Chen C, Chen L, Zou X, Cai P (2009) Prediction of protein secondary structure content by using the concept of Chou’s pseudo amino acid composition and support vector machine. Protein Pept Lett 16: 27–31. doi:10.1016/j.jtbi.2006.06.025 PubMedCrossRefGoogle Scholar
  26. 26.
    Ding YS, Zhang TL (2008) Using Chou’s pseudo amino acid composition to predict subcellular localization of apoptosis proteins: an approach with immune genetic algorithm-based ensemble classifier. Pattern Recognit Lett 29: 1887–1892. doi:10.1016/j.patrec.2008.06.007 CrossRefGoogle Scholar
  27. 27.
    Ding H, Luo L, Lin H (2009) Prediction of cell wall lytic enzymes using Chou’s amphiphilic pseudo amino acid composition. Protein Pept Lett 16: 351–355. doi:10.2174/092986609787848045 PubMedCrossRefGoogle Scholar
  28. 28.
    Georgiou DN, Karakasidis TE, Nieto JJ, Torres A (2009) Use of fuzzy clustering technique and matrices to classify amino acids and its impact to Chou’s pseudo amino acid composition. J Theor Biol 257: 17–26. doi:10.1016/j.jtbi.2008.11.003 PubMedCrossRefGoogle Scholar
  29. 29.
    Jiang X, Wei R, Zhang TL, Gu Q (2008) Using the concept of Chou’s pseudo amino acid composition to predict apoptosis proteins subcellular location: an approach by approximate entropy. Protein Pept Lett 15: 392–396. doi:10.2174/092986608784246443 PubMedCrossRefGoogle Scholar
  30. 30.
    Li FM, Li QZ (2008) Predicting protein subcellular location using Chou’s pseudo amino acid composition and improved hybrid approach. Protein Pept Lett 15: 612–616. doi:10.2174/092986608784966930 PubMedCrossRefGoogle Scholar
  31. 31.
    Lin H (2008) The modified Mahalanobis discriminant for predicting outer membrane proteins by using Chou’s pseudo amino acid composition. J Theor Biol 252: 350–356. doi:10.1016/j.jtbi.2008.02.004 PubMedCrossRefGoogle Scholar
  32. 32.
    Lin H, Ding H, Feng-Biao Guo FB, Zhang AY, Huang J (2008) Predicting subcellular localization of mycobacterial proteins by using Chou’s pseudo amino acid composition. Protein Pept Lett 15: 739–744PubMedCrossRefGoogle Scholar
  33. 33.
    Lin H, Wang H, Ding H, Chen YL, Li QZ (2009) Prediction of subcellular localization of apoptosis protein using Chou’s pseudo amino acid composition. Acta Biotheor 57: 321–330. doi:10.1007/s10441-008-9067-4 PubMedCrossRefGoogle Scholar
  34. 34.
    Qiu JD, Huang JH, Liang RP, Lu XQ (2009) Prediction of G-protein-coupled receptor classes based on the concept of Chou’s pseudo amino acid composition: an approach from discrete wavelet transform. Anal Biochem 390: 68–73. doi:10.1016/j.ab.2009.04.009 PubMedCrossRefGoogle Scholar
  35. 35.
    Zeng YH, Guo YZ, Xiao RQ, Yang L, Yu LZ, Li ML (2009) Using the augmented Chou’s pseudo amino acid composition for predicting protein submitochondria locations based on auto covariance approach. J Theor Biol 259: 366–372. doi:10.1016/j.jtbi.2009.03.028 PubMedCrossRefGoogle Scholar
  36. 36.
    Zhang GY, Fang BS (2008) Predicting the cofactors of oxidoreductases based on amino acid composition distribution and Chou’s amphiphilic pseudo amino acid composition. J Theor Biol 253: 310–315. doi:10.1016/j.jtbi.2008.03.015 PubMedCrossRefGoogle Scholar
  37. 37.
    Zhang GY, Li HC, Fang BS (2008) Predicting lipase types by improved Chou’s pseudo-amino acid composition. Protein Pept Lett 15: 1132–1137. doi:10.2174/092986608786071184 PubMedCrossRefGoogle Scholar
  38. 38.
    Zhou XB, Chen C, Li ZC, Zou XY (2007) Using Chou’s amphiphilic pseudo-amino acid composition and support vector machine for prediction of enzyme subfamily classes. J Theor Biol 248: 546–551. doi:10.1016/j.jtbi.2007.06.001 PubMedCrossRefGoogle Scholar
  39. 39.
    Chou KC, Zhang CT (1994) Predicting protein folding types by distance functions that make allowances for amino acid interactions. J Biol Chem 269: 22014–22020PubMedGoogle Scholar
  40. 40.
    Xiao X, Shao S, Ding Y, Huang Z, Huang Y, Chou KC (2005) Using complexity measure factor to predict protein subcellular location. Amino Acids 28: 57–61. doi:10.1007/s00726-004-0148-7 PubMedCrossRefGoogle Scholar
  41. 41.
    Xiao X, Shao SH, Huang ZD, Chou KC (2006) Using pseudo amino acid composition to predict protein structural classes: approached with complexity measure factor. J Comput Chem 27: 478–482. doi:10.1002/jcc.20354 PubMedCrossRefGoogle Scholar
  42. 42.
    Wolfram S (1984) Cellular automation as models of complexity. Nature 311: 419–424CrossRefGoogle Scholar
  43. 43.
    Cover TM, Hart PE (1967) Nearest neighbour pattern classification. IEEE Trans Inform Theory (IT) 13: 21–27CrossRefGoogle Scholar
  44. 44.
    Chou KC, Shen HB (2006) Hum-PLoc: A novel ensemble classifier for predicting human protein subcellular localization. Biochem Biophys Res Commun 347: 150–157. doi:10.1016/j.bbrc.2006.06.059 PubMedCrossRefGoogle Scholar
  45. 45.
    Chou KC, Shen HB (2007) Large-scale plant protein subcellular location prediction. J Cell Biochem 100: 665–678. doi:10.1002/jcb.21096 PubMedCrossRefGoogle Scholar
  46. 46.
    Chou KC, Shen HB (2007) Euk-mPLoc: a fusion classifier for large-scale eukaryotic protein subcellular location prediction by incorporating multiple sites. J Proteom Res 6: 1728–1734. doi:10.1021/pr060635i CrossRefGoogle Scholar
  47. 47.
    Chou KC, Shen HB (2007) MemType-2L: a Web server for predicting membrane proteins and their types by incorporating evolution information through Pse-PSSM. Biochem Biophys Res Commun 360: 339–345. doi:10.1021/pr060635i PubMedCrossRefGoogle Scholar
  48. 48.
    Keller JM, Gray MR, Givens JA (1985) A fuzzy k-nearest neighbours algorithm. IEEE Trans Syst Man Cybern 15: 580–585Google Scholar
  49. 49.
    Mardia KV, Kent JT, Bibby JM (1979) Multivariate analysis: chapter 11 discriminant analysis; chapter 12 multivariate analysis of variance; chapter 13 cluster analysis. Academic Press, London, pp 322–381Google Scholar
  50. 50.
    Mahalanobis PC (1936) On the generalized distance in statistics. Proc Natl Inst Sci India 2: 49–55Google Scholar
  51. 51.
    Zhou GP (1998) An intriguing controversy over protein structural class prediction. J Protein Chem 17: 729–738. doi:10.1023/A:1020713915365 PubMedCrossRefGoogle Scholar
  52. 52.
    Zhou GP, Assa-Munt N (2001) Some insights into protein structural class prediction. Proteins: Struct Funct Genet 44: 57–59. doi:10.1002/prot.1071 CrossRefGoogle Scholar
  53. 53.
    Zhou GP, Doctor K (2003) Subcellular location prediction of apoptosis proteins. Proteins: Struct Funct Genet 50: 44–48. doi:10.1002/prot.10251 CrossRefGoogle Scholar
  54. 54.
    Wang T, Yang J, Shen HB, Chou KC (2008) Predicting membrane protein types by the LLDA algorithm. Protein Pept Lett 15: 915–921. doi:10.2174/092986608785849308 PubMedCrossRefGoogle Scholar
  55. 55.
    Chen K, Kurgan M, Kurgan L (2008) Sequence based prediction of relative solvent accessibility using two-stage support vector regression with confidence values. J Biomed Sci Eng (JBiSE) 1: 1–9. http://www.srpublishing.org/journal/jbise/. doi:10.4236/jbise.2008.11001
  56. 56.
    Shen HB, Song JN, Chou KC (2009) Prediction of protein folding rates from primary sequence by fusing multiple sequential features. J Biomed Sci Eng (JBiSE) 2:136–143. http://www.srpublishing.org/journal/jbise/. doi:10.4236/jbise.2009.23024 Google Scholar
  57. 57.
    Chou KC, Shen HB (2009) FoldRate: a web-server for predicting protein folding rates from primary sequence. Open Bioinform J 3:31–50. http://www.bentham.org/open/tobioij/ Google Scholar
  58. 58.
    Du P, Cao S, Li Y (2009) SubChlo: predicting protein subchloroplast locations with pseudo-amino acid composition and the evidence-theoretic K-nearest neighbor (ET-KNN) algorithm. J Theor Biol 261: 330–335. doi:10.1016/j.jtbi.2009.08.004 PubMedCrossRefGoogle Scholar
  59. 59.
    Vilar S, Gonzalez-Diaz H, Santana L, Uriarte E (2009) A network-QSAR model for prediction of genetic-component biomarkers in human colorectal cancer. J Theor Biol 261: 449–458. doi:10.1016/j.jtbi.2009.07.031 PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  1. 1.Computer DepartmentJing-De-Zhen Ceramic InstituteJing-De-ZhenChina
  2. 2.Gordon Life Science InstituteSan DiegoUSA

Personalised recommendations