Skip to main content
Log in

An Intriguing Controversy over Protein Structural Class Prediction

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

Abstract

A recent report by Bahar et al. [(1997), Proteins 29, 172–185] indicates that the coupling effects among different amino acid components as originally formulated by K. C. Chou [(1995), Proteins 21, 319–344] are important for improving the prediction of protein structural classes. These authors have further proposed a compact lattice model to illuminate the physical insight contained in the component-coupled algorithm. However, a completely opposite result was concluded by Eisenhaber et al. [(1996), Proteins 25, 169–179], using a different dataset constructed according to their definition. To address such an intriguing controversy, tests were conducted by various approaches for the datasets from an objective database, the SCOP database [Murzin et al. (1995), J. Mol. Biol. 247, 536–540]. The results obtained by both self-consistency and jackknife tests indicate that the overall rates of correct prediction by the algorithm incorporating the coupling effect among different amino acid components are significantly higher than those by the algorithms without counting such an effect. This is fully consistent with the physical reality that the folding of a protein is the result of a collective interaction among its constituent amino acid residues, and hence the coupling effects of different amino acid components must be incorporated in order to improve the prediction quality. It was found by a revisiting the calculation procedures by Eisenhaber et al. that there was a conceptual mistake in constructing the structural class datasets and a systematic mistake in applying the component-coupled algorithm. These findings are informative for understanding and utilizing the component-coupled algorithm to study the structural classes of 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.

Similar content being viewed by others

REFERENCES

  • Bahar, I., Atilgan, A. R., Jernigan, R. L., and Erman, B. (1997). Understanding the recognition of protein structural classes by amino acid composition, Proteins 29, 172–185.

    Article  CAS  PubMed  Google Scholar 

  • Chandonia, J. M., and Karplus, M. (1995). Neural networks for secondary structure and structural class prediction, Protein Sci. 4, 275–285.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Chou, K. C. (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.

    Article  CAS  PubMed  Google Scholar 

  • Chou, K. C., and Zhang, C. T. (1995). Prediction of protein structural classes, Crit. Rev. Biochem. Mole. Biol. 30, 275–349.

    Article  CAS  Google Scholar 

  • Chou, P. Y. (1980). Amino acid composition of four classes of proteins, In Abstracts 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, In Prediction 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). Predicting protein secondary structure content: A tandem neural network approach, Proteins Struct. Funct. Genet. 16, 79–91.

    Article  CAS  PubMed  Google Scholar 

  • Duda, R. O., and Hart, P. E. (1973). Pattern Classification and Scene Analysis, Wiley, New York, Chapter 2.

    Google Scholar 

  • Eisenhaber, F., Frömmel, C., and Argos, P. (1996). Prediction of secondary structural content of proteins from their amino acid composition alone. II. The paradox with secondary structural class, Proteins Struct. Funct. Genet. 25, 169–179.

    Article  CAS  PubMed  Google Scholar 

  • Farber, G. K., and Petsko, G. A. (1990). The evolution of α/β barrel enzymes, TIBS 15, 228–234.

    CAS  PubMed  Google Scholar 

  • Kabsch, W., Mannherz, H. G., Suck, D., Pai, E. F., and Holms, K. C. (1990). Atomic structure of the actin:DNase 1 complex, Nature 347, 37–44.

    Article  CAS  PubMed  Google Scholar 

  • Kabsch, W., and Sander, C. (1983). Dictionary of protein secondary structure: Pattern recognition of hydrogen-bonded and geometrical features, Biopolymers 22, 2577–2637.

    Article  CAS  PubMed  Google Scholar 

  • Klein, P., and Delisi, C. (1986). Prediction of protein structural class from amino acid sequence, Biopolymers 25, 1659–1672.

    Article  CAS  PubMed  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 

  • Mardia, K. V., Kent, J. T., and Bibby, J. M. (1979) Multivariate Analysis. Academic Press, London, pp. 322 and 381.

    Google Scholar 

  • Metfessel, B. A., Saurugger, P. N., Connelly, D. P., and Rich, S. T. (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 

  • Murzin, A. G., Brenner, S. E., Hubbard, T., and Chothia, C. (1995). SCOP: A structural classification of protein database for the investigation of sequence and structures, J. Mol. Biol. 247, 536–540.

    Article  CAS  PubMed  Google Scholar 

  • Muskal, S. M., and Kim, S.-H. (1992). Predicting protein secondary structure content: A tandem neural network approach, J. Mol. Biol. 225, 713–727.

    Article  CAS  PubMed  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). Mahalanobis D 2, In Encyclopedia of Statistical Sciences (Kotz, S., and Johnson, N. L., eds.), Wiley, New York, Vol. 5, pp. 176–181.

    Google Scholar 

  • Rost, B., and Sander, C. (1994). Combining evolutionary information and neural networks to predict protein secondary structure, Protein Struct. Funct. Genet. 19, 55–72.

    Article  CAS  Google Scholar 

  • Sondek, J., and Shortle, D. (1990). Accomodation of single amino acid insertions by the native state of staphyloccocal nuclease, Proteins Struct. Funct. Genet. 7, 299–305.

    Article  CAS  PubMed  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

Zhou, GP. An Intriguing Controversy over Protein Structural Class Prediction. J Protein Chem 17, 729–738 (1998). https://doi.org/10.1023/A:1020713915365

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1020713915365

Navigation