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Fuzzy K-Nearest Neighbor Classifier to Predict Protein Solvent Accessibility

  • Conference paper
Neural Information Processing (ICONIP 2007)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4985))

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Abstract

The prediction of protein solvent accessibility is an intermediate step for predicting the tertiary structure of proteins. Knowledge of solvent accessibility has proved useful for identifying protein function, sequence motifs, and domains. Using a position-specific scoring matrix (PSSM) generated from PSI-BLAST in this paper, we develop the modified fuzzy k-nearest neighbor method to predict the protein relative solvent accessibility. By modifying the membership functions of the fuzzy k-nearest neighbor method by Sim et al. [1], has recently been applied to protein solvent accessibility prediction with excellent results. Our modified fuzzy k-nearest neighbor method is applied on the three-state, E, I, and B, and two-state, E, and B, relative solvent accessibility predictions, and its prediction accuracy compares favorly with those by the fuzzy k-NN and other approaches.

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References

  1. Sim, J., Kim, S.Y., Lee, J.: Prediction of protein solvent accessibility using fuzzy k-nearest neighbor method. Bioinformatics 21, 2844–2849 (2005)

    Article  Google Scholar 

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

    Article  Google Scholar 

  3. Thompson, M.J., Goldstein, R.A.: Predicting solvent accessibility: higher accuracy using Bayesian statistics and optimized residue substitution classes. Proteins 25, 38–47 (1996)

    Article  Google Scholar 

  4. 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 

  5. Frishman, D., Argos, P.: Seventy-five percent accuracy in protein secondary structure prediction. Proteins 27, 329–335 (1997)

    Article  Google Scholar 

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

    Article  Google Scholar 

  7. Przybylski, D., Rost, B.: Alignments grow, secondary structure prediction improves. Proteins 46, 197–205 (2002)

    Article  Google Scholar 

  8. Wohlfahrt, G., et al.: Positioning of anchor groups in protein loop prediction: the importance of solvent accessibility and secondary structure elements. Proteins 47, 370–378 (2002)

    Article  Google Scholar 

  9. Eyal, E., et al.: Importance of solvent accessibility and contact surfaces in modeling side-chain conformations in proteins. J. Comput. Chem. 25, 712–724 (2004)

    Article  Google Scholar 

  10. Russell, S.J., et al.: Stability of cyclic beta-hairpins: asymmetric contributions from side chains of a hydrogen-bonded cross-strand residue pair. J. Am. Chem. Soc. 125, 388–395 (2003)

    Article  Google Scholar 

  11. Totrov, M.: Accurate and efficient generalized born model based on solvent accessibility: derivation and application for LogP octanol/water prediction and flexiblepeptide docking. J. Comput. Chem. 25, 609–619 (2004)

    Article  Google Scholar 

  12. Rost, B., et al.: Protein fold recognition by prediction-based threading. J. Mol. Biol. 270, 471–480 (1997)

    Article  Google Scholar 

  13. Gianese, G., et al.: Improvement in prediction of solvent accessibility by probability profiles. Protein Eng. 16, 987–992 (2003)

    Article  Google Scholar 

  14. Pei, J., Grishin, N.V.: Combining evolutionary and structural information for local protein structure prediction. Proteins 56, 782–794 (2004)

    Article  Google Scholar 

  15. Keller, J.M., et al.: A fuzzy k-nearest neighbor algorithm. IEE Trans. Syst. Man Cybern. 15, 580–585 (1985)

    Google Scholar 

  16. Altschul, S.F., et al.: Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 25, 3389–3402 (1997)

    Article  Google Scholar 

  17. 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)

    Article  Google Scholar 

  18. Nguyen, M.N., Rajapakse, J.C.: Prediction of protein relative solvent accessibility with a two-stage SVM approach. Proteins 59, 30–37 (2005)

    Article  Google Scholar 

  19. Rost, B., Sander, C.: Prediction of protein secondary structure at better than 70% accuracy. J. Mol. Biol. 232, 584–599 (1993)

    Article  Google Scholar 

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Masumi Ishikawa Kenji Doya Hiroyuki Miyamoto Takeshi Yamakawa

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© 2008 Springer-Verlag Berlin Heidelberg

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Chang, JY., Shyu, JJ., Shi, YX. (2008). Fuzzy K-Nearest Neighbor Classifier to Predict Protein Solvent Accessibility. In: Ishikawa, M., Doya, K., Miyamoto, H., Yamakawa, T. (eds) Neural Information Processing. ICONIP 2007. Lecture Notes in Computer Science, vol 4985. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69162-4_87

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  • DOI: https://doi.org/10.1007/978-3-540-69162-4_87

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-69159-4

  • Online ISBN: 978-3-540-69162-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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