Skip to main content

A Weighted Profile Based Method for Protein-RNA Interacting Residue Prediction

  • Conference paper
Transactions on Computational Systems Biology IV

Part of the book series: Lecture Notes in Computer Science ((TCSB,volume 3939))

Abstract

The prediction of putative RNA-interacting residues in proteins is an important problem in a field of molecular recognition. We suggest a weighted profile based method for predicting RNA-interacting residues, which utilizes the trained neural network. Most neural networks have a learning rule which allows the network to adjust its connection weights in order to correctly classify the training data. We focus on the network weights that are dependent on the training data set and give evidence of which inputs were more influential in the network. A large set of the network weights trained on sequence profiles is analyzed and qualified. We explore the feasibility of utilizing the qualified information to improve the prediction performance for protein-RNA interaction. Our proposed method shows a considerable improvement, which has been applied to the profiles of the PSI-BLAST alignment. Results for predictions using alternative representations of profile are included for comparison.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Allers, J., Shamoo, Y.: Structure-based analysis of protein-RNA interactions using the program ENTANGLE. J. Mol. Biol. 311, 75–86 (2001)

    Article  Google Scholar 

  2. Altschul, S.F., Madden, T.L., Schaffer, A.A., Zhang, J., Zhang, Z., Miller, W., Lipman, D.J.: Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25, 3389–3402 (1997)

    Article  Google Scholar 

  3. Berman, H.M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T.N., Weissig, H., Shindyalov, I.N., Bourne, P.E.: The Protein Data Bank. Nucleic Acids Res. 28, 235–242 (2000)

    Article  Google Scholar 

  4. Chen, Y., Kortemme, T., Robertson, T., Baker, D., Varani, G.: A new hydrogen-bonding potential for the design of protein-RNA interactions predicts specific contacts and discriminates decoys. Nucleic Acids Res. 32, 5147–5172 (2004)

    Article  Google Scholar 

  5. Crooks, G.E., Hon, G., Chandonia, J.-M., Brenner, S.E.: WebLogo: a sequence logo generator. Genome Res. 14, 1188–1190 (2004)

    Article  Google Scholar 

  6. Cuff, J.A., Barton, G.J.: Application of multiple sequence alignment profiles to improve protein secondary structure prediction. Proteins 40, 502–511 (2000)

    Article  Google Scholar 

  7. Draper, D.E.: Themes in RNA-protein recognition. J. Mol. Biol. 293, 255–270 (1999)

    Article  Google Scholar 

  8. Eddy, S.R.: Profile hidden Markov models. Bioinformatics 14, 755–763 (1998)

    Article  Google Scholar 

  9. Han, L., Cai, C., Lo, S., Chung, M., Chen, Y.: Prediction of RNA-binding proteins from primary sequence by a support vector machine approach. RNA 10, 355–368 (2004)

    Article  Google Scholar 

  10. Hassibi, B., Stork, D.G.: Second order derivatives for network pruning: Optimal brain surgeon. Advances in Neural Information Processing Systems 5, 164–172 (1993)

    Google Scholar 

  11. Henikoff, S., Henikoff, J.G.: Protein family classification based on searching a database of blocks. Genomics 19, 97–107 (1994)

    Article  Google Scholar 

  12. Henikoff, S., Henikoff, J.G.: Using substitution probabilities to improve position-specific scoring matrices. CABIOS 12, 135–143 (1996)

    Google Scholar 

  13. Jeong, E., Chung, I., Miyano, S.: A neural network method for identification of RNA-interacting residues in protein. Genome Informatics 15, 105–116 (2004)

    Google Scholar 

  14. Jones, D.T.: Protein secondary structure prediction based on position-specific scoring matrices. J. Mol. Biol. 292, 195–202 (1999)

    Article  Google Scholar 

  15. Jones, S., Daley, D.T.A., Luscombe, N.M., Berman, H.M., Thornton, J.M.: Protein-RNA interactions: a structural analysis. Nucleic Acids Res. 29(4), 943–954 (2001)

    Article  Google Scholar 

  16. Kaur, H., Raghava, G.: A neural network method for prediction of β-turn types in proteins using evolutionary information. Bioinformatics 20, 2751–2758 (2004)

    Article  Google Scholar 

  17. Kim, H., Jeong, E., Lee, S.W., Han, K.: Computational analysis of hydrogen bonds in protein-RNA complexes for interaction patterns. FEBS Lett. 552, 231–239 (2003)

    Article  Google Scholar 

  18. Kloczkowski, A., Ting, K.L., Jernigan, R.L., Garnier, G.: Combining the GOR V algorithm with evolutionary information for protein secondary structure prediction from amino acid sequence. Proteins 49, 154–166 (2000)

    Article  Google Scholar 

  19. Krogh, A., Brown, M., Mian, I.S., Sjölander, K., Haussler, D.: Hidden Markov models in computational biology. J. Mol. Biol. 235, 1501–1531 (1994)

    Article  Google Scholar 

  20. Le Cun, Y., Denker, J.S., Solla, S.A.: Optimal brain damage. Advances in Neural Information Processing Systems 2, 598–605 (1990)

    Google Scholar 

  21. Mitchison, G., Durbin, R.: Tree-based maximal likelihood substitution matrices and hidden Markov models. J. Mol. Evol. 41, 1139–1151 (1995)

    Article  Google Scholar 

  22. Mount, W.: Bioinformatics: sequence and genome analysis. Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NY (2001)

    Google Scholar 

  23. Rost, B., Sander, C.: Combining evolutionary information and neural networks to predict protein secondary structure. Proteins 19, 55–72 (1994)

    Article  Google Scholar 

  24. Rost, B., Sander, C.: Conservation and prediction of solvent accessibility in protein families. Proteins 20, 216–226 (1994)

    Article  Google Scholar 

  25. Sayle, R., Milner-White, E.J.: RasMol: Biomolecular graphics for all. Trends Biochem. Sci. 20, 374–376 (1995)

    Article  Google Scholar 

  26. Schneider, T.D., Stephens, R.M.: Sequence logos: a new way to display consensus sequence. Nucleic Acids Res. 18, 6097–6100 (1990)

    Article  Google Scholar 

  27. Sharp, K.A., Honig, B., Harvey, S.C.: Electrical potential of transfer RNAs: codon-anticodon recognition. Biochemistry 29, 340–346 (1990)

    Article  Google Scholar 

  28. Sjölander, K., Karplus, K., Brown, M., Hughey, R., Krogh, A., Mian, I.S., Haussler, D.: Dirichlet mixtures: a method for improved detection of weak but significant protein sequence homology. Comput Appl Biosci. 12, 327–345 (1996)

    Google Scholar 

  29. Thompson, J.D., Higgins, D.G., Gibson, T.J.: CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res. 22, 4673–4680 (1994)

    Article  Google Scholar 

  30. Zell, A., Mamier, G.: Stuttgart neural network simulator version 4.2 (1997)

    Google Scholar 

  31. Zhou, H.X., Shan, Y.: Prediction of protein interaction sites from sequence profile and residue neighbor list. Proteins 44, 336–343 (2001)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Jeong, E., Miyano, S. (2006). A Weighted Profile Based Method for Protein-RNA Interacting Residue Prediction. In: Priami, C., Cardelli, L., Emmott, S. (eds) Transactions on Computational Systems Biology IV. Lecture Notes in Computer Science(), vol 3939. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11732488_11

Download citation

  • DOI: https://doi.org/10.1007/11732488_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-33245-9

  • Online ISBN: 978-3-540-33248-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics