Skip to main content
Protein Science : A Publication of the Protein Society logoLink to Protein Science : A Publication of the Protein Society
. 1992 May;1(5):667–677. doi: 10.1002/pro.5560010512

Protein classification artificial neural system.

C Wu 1, G Whitson 1, J McLarty 1, A Ermongkonchai 1, T C Chang 1
PMCID: PMC2142223  PMID: 1304365

Abstract

A neural network classification method is developed as an alternative approach to the large database search/organization problem. The system, termed Protein Classification Artificial Neural System (ProCANS), has been implemented on a Cray supercomputer for rapid superfamily classification of unknown proteins based on the information content of the neural interconnections. The system employs an n-gram hashing function that is similar to the k-tuple method for sequence encoding. A collection of modular back-propagation networks is used to store the large amount of sequence patterns. The system has been trained and tested with the first 2,148 of the 8,309 entries of the annotated Protein Identification Resource protein sequence database (release 29). The entries included the electron transfer proteins and the six enzyme groups (oxidoreductases, transferases, hydrolases, lyases, isomerases, and ligases), with a total of 620 superfamilies. After a total training time of seven Cray central processing unit (CPU) hours, the system has reached a predictive accuracy of 90%. The classification is fast (i.e., 0.1 Cray CPU second per sequence), as it only involves a forward-feeding through the networks. The classification time on a full-scale system embedded with all known superfamilies is estimated to be within 1 CPU second. Although the training time will grow linearly with the number of entries, the classification time is expected to remain low even if there is a 10-100-fold increase of sequence entries. The neural database, which consists of a set of weight matrices of the networks, together with the ProCANS software, can be ported to other computers and made available to the genome community. The rapid and accurate superfamily classification would be valuable to the organization of protein sequence databases and to the gene recognition in large sequencing projects.

Full Text

The Full Text of this article is available as a PDF (1,015.8 KB).

Selected References

These references are in PubMed. This may not be the complete list of references from this article.

  1. Altschul S. F., Gish W., Miller W., Myers E. W., Lipman D. J. Basic local alignment search tool. J Mol Biol. 1990 Oct 5;215(3):403–410. doi: 10.1016/S0022-2836(05)80360-2. [DOI] [PubMed] [Google Scholar]
  2. Barker W. C., George D. G., Hunt L. T. Protein sequence database. Methods Enzymol. 1990;183:31–49. doi: 10.1016/0076-6879(90)83005-t. [DOI] [PubMed] [Google Scholar]
  3. Claverie J. M., Sauvaget I., Bougueleret L. K-tuple frequency analysis: from intron/exon discrimination to T-cell epitope mapping. Methods Enzymol. 1990;183:237–252. doi: 10.1016/0076-6879(90)83017-4. [DOI] [PubMed] [Google Scholar]
  4. Demeler B., Zhou G. W. Neural network optimization for E. coli promoter prediction. Nucleic Acids Res. 1991 Apr 11;19(7):1593–1599. doi: 10.1093/nar/19.7.1593. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Doolittle R. F. Searching through sequence databases. Methods Enzymol. 1990;183:99–110. doi: 10.1016/0076-6879(90)83008-w. [DOI] [PubMed] [Google Scholar]
  6. Kneller D. G., Cohen F. E., Langridge R. Improvements in protein secondary structure prediction by an enhanced neural network. J Mol Biol. 1990 Jul 5;214(1):171–182. doi: 10.1016/0022-2836(90)90154-E. [DOI] [PubMed] [Google Scholar]
  7. Lipman D. J., Pearson W. R. Rapid and sensitive protein similarity searches. Science. 1985 Mar 22;227(4693):1435–1441. doi: 10.1126/science.2983426. [DOI] [PubMed] [Google Scholar]
  8. Needleman S. B., Wunsch C. D. A general method applicable to the search for similarities in the amino acid sequence of two proteins. J Mol Biol. 1970 Mar;48(3):443–453. doi: 10.1016/0022-2836(70)90057-4. [DOI] [PubMed] [Google Scholar]
  9. O'Neill M. C. Training back-propagation neural networks to define and detect DNA-binding sites. Nucleic Acids Res. 1991 Jan 25;19(2):313–318. doi: 10.1093/nar/19.2.313. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Pabo C. O. New generation databases for molecular biology. Nature. 1987 Jun 11;327(6122):467–467. doi: 10.1038/327467a0. [DOI] [PubMed] [Google Scholar]
  11. Pearson W. R., Lipman D. J. Improved tools for biological sequence comparison. Proc Natl Acad Sci U S A. 1988 Apr;85(8):2444–2448. doi: 10.1073/pnas.85.8.2444. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Stormo G. D., Schneider T. D., Gold L., Ehrenfeucht A. Use of the 'Perceptron' algorithm to distinguish translational initiation sites in E. coli. Nucleic Acids Res. 1982 May 11;10(9):2997–3011. doi: 10.1093/nar/10.9.2997. [DOI] [PMC free article] [PubMed] [Google Scholar]

Articles from Protein Science : A Publication of the Protein Society are provided here courtesy of The Protein Society

RESOURCES