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Fuzzy k-Nearest Neighbor Method for Protein Secondary Structure Prediction and Its Parallel Implementation

  • Seung-Yeon Kim
  • Jaehyun Sim
  • Julian Lee
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4115)

Abstract

Fuzzy k-nearest neighbor method is a generalization of nearest neighbor method, the simplest algorithm for pattern classification. One of the important areas for application of the pattern classification is the protein secondary structure prediction, an important topic in the field of bioinformatics. In this work, we develop a parallel algorithm for protein secondary structure prediction, based on the fuzzy k-nearest neighbor method, that uses evolutionary profile obtained from PSI-BLAST (Position Specific Iterative Basic Local Sequence Alignment Tool) as the feature vectors.

Keywords

Feature Vector Secondary Structure Prediction Solvent Accessibility Protein Structure Prediction Reference Dataset 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Kryshtafovych, A., Venclovas, C., Fidelis, K., Moult, J.: Progress over the First Decade of CASP Experiments. Proteins 61, 225–236 (2005)CrossRefGoogle Scholar
  2. 2.
    Lee, J., Kim, S.-Y., Joo, K., Kim, I., Lee, J.: Prediction of Protein Tertiary Structure using PROFESY, a Novel Method Based on Fragment Assembly and Conformational Space Annealing. Proteins 56, 704–714 (2004)CrossRefGoogle Scholar
  3. 3.
    Lee, J., Kim, S.-Y., Lee, J.: Protein Structure Prediction Based on Fragment Assembly and Parameter Optimization. Biophys. Chem. 115, 209–214 (2005)CrossRefGoogle Scholar
  4. 4.
    Lee, J., Kim, S.-Y., Lee, J.: Protein Structure Prediction Based on Fragment Assembly and Beta-strand Pairing Energy Function. J. Korean Phys. Soc. 46, 707–712 (2005)Google Scholar
  5. 5.
    Rost, B., Sander, C.: Prediction of Secondary Structure at Better than 70% Accuracy. J. Mol. Biol. 232, 584–599 (1993)CrossRefGoogle Scholar
  6. 6.
    Jones, D.: Protein Secondary Structure Prediction Based on Position-specific Scoring Matrices. J. Mol. Biol. 292, 195–202 (1999)CrossRefGoogle Scholar
  7. 7.
    Ouali, M., King, R.: Cascaded Multiple Classifiers for Secondary Structure Prediction. Protein Science 9, 1162–1176 (1999)CrossRefGoogle Scholar
  8. 8.
    Adamczak, R., Porollo, A., Meller, J.: Combining Prediction of Secondary Structure and Solvent accessibility in proteins. Proteins 59, 467–475 (2005)CrossRefGoogle Scholar
  9. 9.
    Hua, S., Sun, Z.: A Novel Method of Protein Secondary Structure Prediction with High Segment Overlap Measure: Support Vector Machine Approach. J. Mol. Biol. 308, 397–407 (2001)CrossRefGoogle Scholar
  10. 10.
    Kim, K., Park, H.: Protein Secondary Structure Prediction based on improved Support Vector Machines Approach. Protein Eng. 16, 553–560 (2003)CrossRefGoogle Scholar
  11. 11.
    Joo, K., Lee, J., Kim, S.-Y., Kim, I., Lee, S.J., Lee, J.: Profile-based Nearest Neighbor Method for Pattern Recognition. J. Korean Phys. Soc. 44, 599–604 (2004)Google Scholar
  12. 12.
    Joo, K., Kim, I., Lee, J., Kim, S.-Y., Lee, S.J., Lee, J.: Prediction of the Secondary Structure of Proteins Using PREDICT, a Nearest Neighbor Method on Pattern Space. J. Korean Phys. Soc. 45, 1441–1449 (2004)Google Scholar
  13. 13.
    Pollastri, G., McLysaght, A.: Porter: a new, Accurate Server for Protein Secondary Structure Prediction. Bioinformatics 21, 1719–1720 (2004)CrossRefGoogle Scholar
  14. 14.
    Jiang, F.: Prediction of Protein Secondary Structure with a Reliability Score Estimated by Local Sequence Clustering. Protein Eng. 16, 651–657 (2003)CrossRefGoogle Scholar
  15. 15.
    Salamov, A.A., Solovyev, V.V.: Protein Secondary Structure Prediction Using Local Alignments. J. Mol. Biol. 268, 31–35 (1997)CrossRefGoogle Scholar
  16. 16.
    Kim, H., Park, H.: Prediction of Protein Relative Solvent Accessibility with Support Vector Machines and Long-range Interaction 3D Local Descriptor. Proteins 54, 557–562 (2004)CrossRefGoogle Scholar
  17. 17.
    Kabsch, W., Sander, C.: Dictionary of Protein Secondary Structure: Pattern Recognition of Hydrogen-bonded and Geometrical Features. Biopolymers 22, 2577–2637 (1983)CrossRefGoogle Scholar
  18. 18.
    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)CrossRefGoogle Scholar
  19. 19.
    Keller, J.M., Gray, R., Givens, J.A.: A Fuzzy k-nearest Neighbor Algorithm. IEEE Trans. Systems Man Cybernet. 15, 580–585 (1985)Google Scholar
  20. 20.
    Sim, J.H., Kim, S.-Y., Lee, J.: Prediction of Protein Solvent Accessibility Using Fuzzy k-Nearest Neighbor Method. Bioinformatics 21, 2844–2849 (2005)CrossRefGoogle Scholar
  21. 21.
    Brenner, S.E., Koehl, P., Levitt, M.: The ASTRAL Compendium for Protein Structure and Sequence Analysis. Nucleic Acids Res. 28, 254–256 (2000)CrossRefGoogle Scholar
  22. 22.
    Koh, I.Y., Eyrich, V., Marti-Renom, M.A., Przybylski, D., Madhusudhan, M.S., Eswar, N., Grana, O., Pazos, F., Valencia, A., Sali, A., Rost, B.: EVA: Evaluation of Protein Structure Prediction Servers. Nucleic Acids Res. 31, 3311–3315 (2003)CrossRefGoogle Scholar
  23. 23.
    Zemla, A., Venclovas, C., Fidelis, K., Rost, B.: A Modified Definition of Sov, a Segment-Based Measurement for Protein Secondary Structure Prediction Assessment. Proteins 34, 220–223 (1999)CrossRefGoogle Scholar
  24. 24.
    Gorodkin, J.: Comparing two K-category Assignment by a K-category Correlation Coefficient. Comput. Biol. and Chem. 28, 367–374 (2004)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Seung-Yeon Kim
    • 1
  • Jaehyun Sim
    • 2
  • Julian Lee
    • 3
  1. 1.Computer Aided Molecular Design Research CenterSoongsil UniversitySeoulKorea
  2. 2.School of DentistrySeoul National UniversitySeoulKorea
  3. 3.Department of Bioinformatics and Life ScienceSoongsil UniversitySeoulKorea

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