Skip to main content
Log in

An eigenvalue-eigenvector approach to predicting protein folding types

  • Published:
Journal of Protein Chemistry Aims and scope Submit manuscript

Abstract

The accuracy of predicting protein folding types can be significantly enhanced by a recently developed algorithm in which the coupling effect among different amino acid components is taken into account [Chou and Zhang (1994)J. Biol. Chem. 269, 22014-22020]. However, in practical calculations using this powerful algorithm, one may sometimes face illconditioned matrices. To overcome such a difficulty, an effective eigenvalue-eigenvector approach is proposed. Furthermore, the new approach has been used to predict a recently constructed set of 76 proteins not included in the training set, and the accuracy of prediction is also much higher than those of other methods.

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.

Institutional subscriptions

Similar content being viewed by others

References

  • Chou, K. C. (1995). A novel approach to predicting protein structural classes in a (20-1)-D amino acid composition space,Proteins: Structure, Function, and Genetics 21, 319–344.

    Article  CAS  Google Scholar 

  • Chou, K. C., and Zhang, C. T. (1993). A new approach to predicting protein folding types,J. Protein Chem. 12, 169–178.

    Article  CAS  PubMed  Google Scholar 

  • Chou, K. C., and Zhang, C. T. (1994). Predicting protein folding types by distance functions that make allowance for amino acid interactions.J. Biol. Chem. 269, 22014–22020.

    Article  CAS  PubMed  Google Scholar 

  • Chou, P. Y. (1980). Amino acid composition of four classes of proteins, inAbstracts of Papers, Part I, Second Chemical Congress of the North American Continent, Las Vegas, Nevada.

  • Chou, P. Y. (1989). Prediction of protein structural classes from amino acid composition, inPrediction of Protein Structure and the Principles of Protein Conformation (Fasman, G. D., ed.), Plenum Press, New York, pp. 549–586.

    Chapter  Google Scholar 

  • Dubchak, I., Holbrook, S. R., and Kim, S. H. (1993). Prediction of protein folding class from amino acid composition.Proteins: Struct. Funct. Genet. 16, 79–91.

    Article  CAS  PubMed  Google Scholar 

  • Kikuchi, T. (1993). Discrimination of folding types of globular proteins based on average distance maps constructed from their sequences,J. Protein Chem. 12, 515–523.

    Article  CAS  PubMed  Google Scholar 

  • Klein, P. (1986). Prediction of protein structural class by discriminant analysis,Biochim. Biophys. Acta 874, 205–215.

    Article  CAS  PubMed  Google Scholar 

  • Klein, P., and Delisi, C. (1986). Prediction of protein structural class from amino acid sequence,Biopolymers 25, 1569–1672.

    Article  Google Scholar 

  • Levitt, M., and Chothia, C. (1976). Structural patterns in globular proteins,Nature 261, 552–557.

    Article  CAS  PubMed  Google Scholar 

  • Mahalanobis, P. C. (1936). On the generalized distance in statistics,Proc. Natl. Inst. Sci. India 2, 49–55.

    Google Scholar 

  • Mao, B., Chou, K. C., and Zhang, C. T. (1994). Protein folding classes: A geometric interpretation of amino acid composition of globular proteins.Protein Eng. 7, 319–330.

    Article  CAS  PubMed  Google Scholar 

  • Metfessel, B. A., Saurugger, P. N., Connelly, D. P., and Rich, S. S. (1993). Cross-validation of protein structural class prediction using statistical clustering and neural networks,Protein Sci. 2, 1171–1182.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Nakashima, H., Nishikawa, K., and Ooi, T. (1986). The folding type of a protein is relevant to the amino acid composition,J. Biochem. 99, 152–162.

    Article  Google Scholar 

  • Pillai, K. C. S. (1985). MahalanobisD 2, inEncyclopedia of Statistical Sciences, Vol. 5, (Kotz, S., and Johnson, N. L., eds.), Wiley, New York, pp. 176–181.

    Google Scholar 

  • Richardson, J. S., and Richardson, D. C. (1989). Principles and patterns of protein conformation, inPrediction of Protein Structure and the Principles of Protein Conformation, (Fasman, G. D., ed.), Plenum Press, New York, pp. 1–98.

    Google Scholar 

  • Zhang, C. T., and Chou, K. C. (1992). An optimization approach to predicting protein structural class from amino acid composition.Protein Sci. 1, 401–408.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Zhang, CT., Chou, KC. An eigenvalue-eigenvector approach to predicting protein folding types. J Protein Chem 14, 309–326 (1995). https://doi.org/10.1007/BF01886788

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF01886788

Key words

Navigation