Amino Acids

, Volume 30, Issue 1, pp 49–54 | Cite as

Using cellular automata images and pseudo amino acid composition to predict protein subcellular location

  • X. Xiao
  • S. Shao
  • Y. Ding
  • Z. Huang
  • K.-C. Chou


The avalanche of newly found protein sequences in the post-genomic era has motivated and challenged us to develop an automated method that can rapidly and accurately predict the localization of an uncharacterized protein in cells because the knowledge thus obtained can greatly speed up the process in finding its biological functions. However, it is very difficult to establish such a desired predictor by acquiring the key statistical information buried in a pile of extremely complicated and highly variable sequences. In this paper, based on the concept of the pseudo amino acid composition (Chou, K. C. PROTEINS: Structure, Function, and Genetics, 2001, 43: 246–255), the approach of cellular automata image is introduced to cope with this problem. Many important features, which are originally hidden in the long amino acid sequences, can be clearly displayed through their cellular automata images. One of the remarkable merits by doing so is that many image recognition tools can be straightforwardly applied to the target aimed here. High success rates were observed through the self-consistency, jackknife, and independent dataset tests, respectively.

Keywords: Cellular automata images – Pseudo amino-acid composition – Protein subcellular location – Complexity – Covariant-discriminant algorithm 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Boland, MV, Markey, MK, Murphy, RF 1998Automated recognition of patterns characteristic of subcellular structures in fluorescence microscopy images.Cytometry33366375CrossRefPubMedGoogle Scholar
  2. Cai, YD 2001Is it a paradox or misinterpretation.PROTEINS: Structure, Function, and Genetics43336338CrossRefGoogle Scholar
  3. Cai, YD, Chou, KC 2003Nearest neighbour algorithm for predicting protein subcellular location by combining functional domain composition and pseudo-amino acid composition.Biochem Biophys Res Comm305407411PubMedGoogle Scholar
  4. Cai, YD, Chou, KC 2004aPredicting 22 protein localizations in budding yeast.Biochem Biophys Res Comm323425428CrossRefGoogle Scholar
  5. Cai, YD, Chou, KC 2004bPredicting subcellular localization of proteins in a hybridization space.Bioinformatics2011511156Google Scholar
  6. Cai, YD, Liu, XJ, Xu, XB, Chou, KC 2002aSupport vector machines for prediction of protein subcellular location by incorporating quasi-sequence-order effect.J Cell Biochem84343348CrossRefGoogle Scholar
  7. Cai, YD, Liu, XJ, Xu, XB, Chou, KC 2002bSVM for predicting membrane protein types by incorporating quasi-sequence-order effect. Internet.Electronic Journal of Molecular Design1219226Google Scholar
  8. Cai, YD, Zhou, GP, Chou, KC 2003Support vector machines for predicting membrane protein types by using functional domain composition.Biophys J8432573263PubMedGoogle Scholar
  9. Cai, YD, Zhou, GP, Chou, KC 2005Predicting enzyme family classes by hybridizing gene product composition and pseudo-amino acid composition.J Theor Biol234145149CrossRefPubMedGoogle Scholar
  10. Cedano, J, Aloy, P, P’erez-Pons, JA, Querol, E 1997Relation between amino acid composition and cellular location of proteins.J Mol Biol266594600CrossRefPubMedGoogle Scholar
  11. Chou, KC 1995A novel approach to predicting protein structural classes in a (20–1)-D amino acid composition space.Proteins: Structure, Function & Genetics21319344Google Scholar
  12. Chou, KC 2000aPrediction of protein subcellular locations by incorporating quasi-sequence-order effect.Biochem Biophys Res Commun278477483CrossRefGoogle Scholar
  13. Chou, KC 2000bReview: Prediction of protein structural classes and subcellular locations.Curr Protein Pept Sci1171208CrossRefGoogle Scholar
  14. Chou, KC 2001Prediction of protein cellular attributes using pseudo-amino-acid-composition.PROTEINS: Structure, Function, and Genetics43246255(Erratum: ibid. (2001) 44: 60)CrossRefGoogle Scholar
  15. Chou KC (2002) A new branch of proteomics: prediction of protein cellular attributes. In: Weinrer PW, Lu Q (eds) Gene cloning & expression technologies, Chapter 4. Eaton Publishing, Westborough, MA, pp 57–70Google Scholar
  16. Chou, KC 2005Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes.Bioinformatics211019CrossRefPubMedGoogle Scholar
  17. Chou, KC, Cai, YD 2002Using functional domain composition and support vector machines for prediction of protein subcellular location.J Biol Chem2774576545769PubMedGoogle Scholar
  18. Chou, KC, Cai, YD 2003aPredicting protein quaternary structure by pseudo amino acid composition.PROTEINS: Structure, Function, and Genetics53282289CrossRefGoogle Scholar
  19. Chou, KC, Cai, YD 2003bPrediction and classification of protein subcellular location: sequence-order effect and pseudo amino acid composition.J Cell Biochem9012501260(Addendum, ibid. (2004) 91 5: 1085)CrossRefGoogle Scholar
  20. Chou, KC, Cai, YD 2004aPredicting protein structural class by functional domain composition.Biochem Biophys Res Comm32110071009(Corrigendum: ibid. (2005) 329: 1362)CrossRefGoogle Scholar
  21. Chou, KC, Cai, YD 2004bPredicting subcellular localization of proteins by hybridizing functional domain composition and pseudo-amino acid composition.J Cell Biochem9111971203CrossRefGoogle Scholar
  22. Chou, KC, Cai, YD 2004cPrediction of protein subcellular locations by GO-FunD-PseAA predicor.Biochem Biophys Res Commun32012361239CrossRefGoogle Scholar
  23. Chou, KC, Cai, YD 2005Predicting protein localization in budding yeast.Bioinformatics21944950CrossRefPubMedGoogle Scholar
  24. Chou, KC, Elrod, DW 1999Protein subcellular location prediction.Protein Engineering12107118CrossRefPubMedGoogle Scholar
  25. Chou, JJ, Zhang, CT 1993A joint prediction of the folding types of 1490 human proteins from their genetic codons.J Theor Biol161251262CrossRefPubMedGoogle Scholar
  26. Chou, KC, Zhang, CT 1994Predicting protein folding types by distance functions that make allowances for amino acid interactions.J Biol Chem2692201422020PubMedGoogle Scholar
  27. Chou, KC, Zhang, CT 1995Review: Prediction of protein structural classes.Crit Rev Biochem Mol Biol30275349PubMedGoogle Scholar
  28. Chou, KC, Liu, W, Maggiora, GM, Zhang, CT 1998Prediction and classification of domain structural classes.PROTEINS: Structure, Function, and Genetics3197103CrossRefGoogle Scholar
  29. Gao, Y, Shao, SH, Xiao, X, Ding, YS, Huang, YS, Huang, ZD, Chou, KC 2005Using pseudo amino acid composition to predict protein subcellular location: approached with Lyapunov index, Bessel function, and Chebyshev filter.Amino Acids28373376CrossRefPubMedGoogle Scholar
  30. Gusev, VD, Nemytikova, LA, Chuzhanova, NA 2001A rapid method for detecting interconnections between functionally and/or evolutionary close biological sequences.Mol Biol (Mosk)3510151022Google Scholar
  31. Haddadnia, J, Faez, K, Ahmadi, M 2002A neural based human face recognition system using an efficient feature extraction method with pseudo zernike moment.J Circuits, Systems, and Computers11283304Google Scholar
  32. Murphy, RF, Boland, MV, Velliste, M 2000Towards a systematics for protein subcellular location: quantitative description of protein localization patterns and automated analysis of fluorescence microscope images.Proc Int Conf Intell Syst Mol Biol8251259PubMedGoogle Scholar
  33. Nakai, K 2000Protein sorting signals and prediction of subcellular localization.Adv Protein Chem54277344CrossRefPubMedGoogle Scholar
  34. Pan, YX, Zhang, ZZ, Guo, ZM, Feng, GY, Huang, ZD, He, L 2003Application of pseudo amino acid composition for predicting protein subcellular location: stochastic signal processing approach.J Protein Chem22395402CrossRefPubMedGoogle Scholar
  35. Park, KJ, Kanehisa, M 2003Prediction of protein subcellular locations by support vector machines using compositions of amino acid and amino acid pairs.Bioinformatics1916561663PubMedGoogle Scholar
  36. Portilla, J, Simoncelli, EP 2000A parametric texture model based on joint statistics of complex wavelet coefficients.Int J Comput Vision404971CrossRefGoogle Scholar
  37. Wang, M, Yang, J, Liu, GP, Xu, ZJ, Chou, KC 2004aWeighted-support vector machines for predicting membrane protein types based on pseudo amino acid composition.Protein Eng Des Sel17509516Google Scholar
  38. Wang, M, Yang, J, Xu, ZJ, Chou, KC 2004bSLLE for predicting membrane protein types.J Theor Biol232715Google Scholar
  39. Wang, M, Yao, JS, Huang, ZD, Xu, ZJ, Liu, GP, Zhao, HY, Wang, XY, Yang, J, Zhu, YS, Chou, KC 2005A new nucleotide-composition based fingerprint of SARS-CoV with visualization analysis.Med Chem13947Google Scholar
  40. Wolfram S (2002) A new kind of science. Wolfram Media Inc., Champaign, ILGoogle Scholar
  41. Xiao, X, Shao, S, Dingl, Y, Huang, Z, Huang, Y, Chou, KC 2005aUsing complexity measure factor to predict protein subcellular location.Amino Acids285761Google Scholar
  42. Xiao, X, Shao, S, Ding, Y, Huang, Z, Chen, X, Chou, KC 2005bAn application of gene comparative image for predicting the effect on replication ratio by HBV virus gene missense mutation.J Theor Biol235555565CrossRefGoogle Scholar
  43. Xiao, X, Shao, S, Ding, Y, Huang, Z, Chen, X, Chou, KC 2005cUsing cellular automata to generate Image representation for biological sequences.Amino Acids282935Google Scholar
  44. Zhou, GP 1998An intriguing controversy over protein structural class prediction.J Protein Chem17729738CrossRefPubMedGoogle Scholar
  45. Zhou, GP, Assa-Munt, N 2001Some insights into protein structural class prediction.PROTEINS: Structure, Function, and Genetics445759Google Scholar
  46. Zhou, GP, Doctor, K 2003Subcellular location prediction of apoptosis proteins.PROTEINS: Structure, Function, and Genetics504448CrossRefGoogle Scholar
  47. Zhu, SC, Wu, Y, Mumford, D 1997Minimax entropy principle and its application to texture modeling.Neural comput916271660Google Scholar
  48. Ziv, J, Lempel, A 1976On the complexity of finite sequences.IEEE Trans Inf TheorIT-227581Google Scholar

Copyright information

© Springer-Verlag/Wien 2005

Authors and Affiliations

  • X. Xiao
    • 1
    • 2
  • S. Shao
    • 1
  • Y. Ding
    • 1
  • Z. Huang
    • 1
  • K.-C. Chou
    • 1
    • 3
  1. 1.Bioinformatics Research Center, Donghua UniversityShanghaiChina
  2. 2.Computer DepartmentJing-De-Zhen Ceramic InstituteJing-De-ZhenChina
  3. 3.Gordon Life Science InstituteSan DiegoU.S.A.

Personalised recommendations