Abstract
The function of a protein is closely correlated with its subcellular location. With the success of human genome project and the rapid increase in the number of newly found protein sequences entering into data banks, it is highly desirable to develop an automated method for predicting the subcellular location of proteins. The establishment of such a predictor will no doubt expedite the functionality determination of newly found proteins and the process of prioritizing genes and proteins identified by genomics efforts as potential molecular targets for drug design. Based on the concept of pseudo amino acid composition originally proposed by K. C. Chou (Proteins: Struct. Funct. Genet. 43: 246–255, 2001), the digital signal processing approach has been introduced to partially incorporate the sequence order effect. One of the remarkable merits by doing so is that many existing tools in mathematics and engineering can be straightforwardly used in predicting protein subcellular location. The results thus obtained are quite encouraging. It is anticipated that the digital signal processing may serve as a useful vehicle for many other protein science areas as well.
Similar content being viewed by others
REFERENCES
Cai, Y. D. (2001). Is it a paradox or misinterpretation. Proteins: Struct. Funct. Genet. 43: 336–338.
Cai, Y. D., and Chou, K. C. (2000). Using neural networks for prediction of subcellular location of prokaryotic and eukaryotic proteins. Mol. Cell Biol. Res. Commun. 4: 172–173.
Cai, Y. D., Liu, X. J., Xu, X. B., and Chou, K. C. (2000). Support vector machines for prediction of protein subcellular location. Mol. Cell Biol. Res. Commun. 4: 230–233.
Cai, Y. D., Liu, X. J., Xu, X. B., and Chou, K. C. (2002a). Support vector machines for predicting membrane protein types by incorporating quasi-sequence-order effect. Internet Electron. J. Mol. Des. 1: 219–226.
Cai, Y. D., Liu, X. J., Xu, X. B., and Chou, K. C. (2002b). Support vector machines for prediction of protein subcellular location by incorporating quasi-sequence-order effect. J. Cell. Biochem. 84: 343–348.
Candy, J. V. (1988). In: Signal Processing, McGraw-Hill, New York, pp. 21–98.
Cedano, J., Aloy, P., P'erez-pons, J. A., and Querol, E. (1997). Relation between amino acid composition and cellular location of proteins. J. Mol. Biol. 266: 594–600.
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.
Chou, K. C. (2000a). Prediction of protein subcellular locations by incorporating quasi-sequence-order effect. Biochem. Biophys. Res. Commun. 278: 477–483.
Chou, K. C. (2000b). Review: Prediction of protein structural classes and subcellular locations. Curr. Protein Pept. Sci. 1: 171–208.
Chou, K. C. (2001). Prediction of protein cellular attributes using pseudoamino-acid-composition. Proteins: Struct. Funct. Genet. 43: 246–255 (Erratum: Proteins: Struct. Funct. Genet. 44: 60, 2001).
Chou, K. C. (2002). A new branch of proteomics: Prediction of protein cellular attributes. In: Weinrer, P. W., and Lu, Q. (eds.), Gene Cloning and Expression Technologies (Chap. 4), Eaton Publishing, Westborough, MA, pp. 57–70.
Chou, K. C., and Cai, Y. D. (2002). Using functional domain composition and support vector machines for prediction of protein subcellular location. J. Biol. Chem. 277: 45765–45769.
Chou, K. C., and Elrod, D. W. (1998). Using discriminant function for prediction of subcellular location of prokaryotic proteins. Biochem. Biophys. Res. Commun. 252: 63–68.
Chou, K. C., and Elrod, D. W. (1999a). Prediction of membrane protein types and subcellular locations. Proteins: Struct. Funct. Genet. 34: 137–153.
Chou, K. C., and Elrod, D. W. (1999b). Protein subcellular location prediction. Protein Eng. 12: 107–118.
Chou, K. C., and Elrod, D. W. (2002). Bioinformatical analysis of G-protein-coupled receptors. J. Proteome Res. 1: 429–433.
Chou, K. C., and Elrod, D. W. (2003). Prediction of enzyme family classes. J. Proteome Res. 2: 183–190.
Chou, K. C., and Zhang, C. T. (1993). A new approach to predicting protein folding types. J. Protein Chem. 12: 169–178.
Chou, K. C., and Zhang, C. T. (1994). Predicting protein folding types by distance functions that make allowances for amino acid interactions. J. Biol. Chem. 269: 22014–22020.
Chou, K. C., and Zhang, C. T. (1995). Review: Prediction of protein structural classes. Crit. Rev. Biochem. Mol. Biol. 30: 275–349.
Chou, K. C., Liu, W., Maggiora, G. M., and Zhang, C. T. (1998). Prediction and classification of domain structural classes. Proteins: Struct. Funct. Genet. 31: 97–103.
Chou, P. Y. (1980). Amino acid composition of four classes of proteins. Abstracts of Papers, Part I, Second Chemical Congress of the North American Continent, Las Vegas.
Chou, P. Y. (1989). Prediction of protein structural classes from amino acid composition. In: Fasman, G. D. (ed.), Prediction of Protein Structure and the Principles of Protein Conformation, Plenum Press, New York, pp. 549–586.
Elrod, D. W., and Chou, K. C. (2002). A study on the correlation of G-protein-coupled receptor types with amino acid composition. Protein Eng. 15: 713–715.
Jones, N. B. (1982). In: Digital Signal Processing, Peter Peregrinus Ltd., London, UK, pp. 139–161.
Liu, W., and Chou, K. C. (1998). Prediction of protein structural classes by modified Mahalanobis discriminant algorithm. J. Protein Chem. 17: 209–217.
Mahalanobis, P. C. (1936). On the generalized distance in statistics. Proc. Natl. Inst. Sci. India 2: 49–55.
Mardia, K. V., Kent, J. T., and Bibby, J. M. (1979). In: Multivariate Analysis, Academic Press, London, pp. 322, 381.
Nakashima, H., and Nishikawa, K. (1994). Discrimination of intracellular and extracellular proteins using amino acid composition and residue-pair frequencies. J. Mol. Biol. 238: 54–61.
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.
Pillai, K. C. S. (1985). Mahalanobis D2. In: Kotz, S., and Johnson, N. L. (eds.), Encyclopedia of Statistical Sciences (Vol. 5), John Wiley & Sons, New York, pp. 176–181.
Reinhardt, A., and Hubbard, T. (1998). Using neural networks for prediction of the subcellular location of proteins. Nucleic Acids Res. 26: 2230–2236.
Tretter, A. S. (1990). In: Introduction to Discrete-Time Signal Processing, John Wiley & Sons, pp. 276–280.
Zhou, G. P. (1998). An intriguing controversy over protein structural class prediction. J. Protein Chem. 17: 729–738.
Zhou, G. P., and Assa-Munt, N. (2001). Some insights into protein structural class prediction. Proteins: Struct. Funct. Genet. 44: 57–59.
Zhou, G. P., and Doctor, K. (2003). Subcellular location prediction of apoptosis proteins. Proteins: Struct. Funct. Genet. 50: 44–48.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Pan, YX., Zhang, ZZ., Guo, ZM. et al. Application of Pseudo Amino Acid Composition for Predicting Protein Subcellular Location: Stochastic Signal Processing Approach. J Protein Chem 22, 395–402 (2003). https://doi.org/10.1023/A:1025350409648
Published:
Issue Date:
DOI: https://doi.org/10.1023/A:1025350409648