Improved Prediction of Protein Secondary Structures Using Adaptively Weighted Profiles

  • Gouchol Pok
  • Keun Ho Ryu
  • Yong J. Chung
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4505)


Prediction of protein secondary structures from amino acid sequences is a useful intermediate step for further elucidation of native, three-dimensional conformation of proteins. Currently, most predictors are based on machine learning approaches with a short fixed-size input window scanning over the amino acid sequence. The center of the window corresponds to the prediction site where the prediction is performed by utilizing the properties of neighboring amino acid residues. By nature, most machine learning approaches consider feature vectors as position-independent in terms of feature components. As such, for the secondary structure prediction problem, most existing approaches do not take into account the distance of amino acid residues from the center residue. We have studied on how the prediction performance can be affected by imposing different weights on the features according to the distance of residues from the center residue, and in this work, we propose an adaptive weighting scheme to improve prediction accuracy.


Support Vector Machine Secondary Structure Prediction Performance Secondary Structure Prediction Protein Secondary Structure 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Altschul, S.F., Gish, W., Miller, W., Myers, E.W., Lipman, D.J.: Basic local alignment search tool. J. Molecular Biology 215, 403–410 (1990)Google Scholar
  2. 2.
    Baldi, P., Brunak, S., Frasconi, P., Soda, G., Pollastri, G.: Exploiting the Past and the Future in Protein Secondary Structure Prediction. Bioinformatics 15(11), 937–946 (1999)CrossRefGoogle Scholar
  3. 3.
    Baldi, P., Brunak, S.: Bioinformatics: The Machine Learning Approach. MIT Press, Cambridge (1998)Google Scholar
  4. 4.
    Berman, H.M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T.N., Weissig, H., Shindyalov, I.N., Bourne, P.E.: The protein data bank. Nucl. Acid. Res. 28, 235–242 (2000)CrossRefGoogle Scholar
  5. 5.
    Casbon, J.: Protein Secondary Structure Prediction with Support Vector Machines. MSc Thesis, University of Sussex (2002)Google Scholar
  6. 6.
    Chou, P.Y., Fasman, G.D.: Prediction of Protein Conformation. Biochemistry 13, 222–245 (1974)CrossRefGoogle Scholar
  7. 7.
    Cuff, J.A., Clamp, M.E., Siddiqui, A.S., Finlay, M., Barton, G.J.: JPred: a consensus secondary structure prediction server. Bioinformatics 14, 892–893 (1998)CrossRefGoogle Scholar
  8. 8.
    Cuff, J.A., Barton, G.J.: Evaluation and improvement of multiple sequence methods for protein secondary structure prediction. Proteins 34, 508–519 (1999)CrossRefGoogle Scholar
  9. 9.
    Frishman, D., Argos, P.: Seventy-five percent accuracy in protein secondary structure prediction. Proteins 27, 329–335 (1997)CrossRefGoogle Scholar
  10. 10.
    Garnier, J., Osguthorpe, D.J., Robson, B.: Analysis of the accuracy and Implications of simple methods for predicting the secondary structure of globular proteins. J. Molecular Biology 120, 97–120 (1978)CrossRefGoogle Scholar
  11. 11.
    Gromiha, M., Selvaraj, S.: Protein Secondary Structure Prediction in Different Structural Classes. Protein Engineering 11(4), 249–251 (1998)CrossRefGoogle Scholar
  12. 12.
    Guo, J., Chen, H., Sun, Z., Lin, Y.: A Novel Method for Protein Secondary Structure Prediction Using Dual-Layer SVM and Profiles. Poteins: Structure, Function, and Bioinformatics 54, 738–743 (2004)CrossRefGoogle Scholar
  13. 13.
    Hua, S., Sun, Z.: A novel method of protein secondary structure prediction with high segment overlap measure: support vector machine approach. J. Molecular Biology 308, 397–407 (2001)CrossRefGoogle Scholar
  14. 14.
    Jones, D.T.: Protein Secondary Structure Prediction Based on Position-specific Scoring Matrices. J. Molecular Biology 292, 195–202 (1999)CrossRefGoogle Scholar
  15. 15.
    Joachims, T.: SVMlight: Support Vector Machine,
  16. 16.
    Kabsch, W., Sander, C.: Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 22(12), 2577–2637 (1983)CrossRefGoogle Scholar
  17. 17.
    Kim, H., Park, H.: Protein secondary structure prediction based on an improved support vector machines approach. Protein Engineering 16(8), 553–560 (2003)CrossRefGoogle Scholar
  18. 18.
    Kneller, D.G., Cohen, F.E., Langridge, R.: Improvements in Protein Secondary Structure Prediction by an Enhanced Neural Network. J. Molecular Biology 214, 171–182 (1990)CrossRefGoogle Scholar
  19. 19.
    Needleman, S.B., Wunsch, C.D.: A General Method Applicable tothe Search for Similarities in the Amino Acid Sequence of Two Proteins. J. Molecular Biology 48, 443–453 (1970)CrossRefGoogle Scholar
  20. 20.
    Nguyen, M.H., Rajapakse, J.C.: Multi-Class Support Vector Machines for Protein Secondary Structure Prediction. Genome Informatics 14, 218–227 (2003)Google Scholar
  21. 21.
    Nordin, M., Sundstrom, M.: Structural Proteomics: Developments in Structure-to-Function Predictions. TRENDS in Biochemistry 20(2), 79–84 (2002)CrossRefGoogle Scholar
  22. 22.
    Pollastri, G., Przybylski, D., Rost, B., Baldi, P.: Improving the Prediction of Protein Secondary Structure in Three and Eight Classes Using Recurrent Neural Networks and Profiles. Proteins 47, 228–235 (2002)CrossRefGoogle Scholar
  23. 23.
    Qian, N., Sejnowski, T.J.: Predicting the secondary structure of globular proteins using neural network models. J. Molecular Biology 202, 865–884 (1988)CrossRefGoogle Scholar
  24. 24.
    Riis, S.K., Krogh, A.: Improving prediction of protein secondary structure using structured neual networks and multiple sequence alignment. J. Comput. Biol. 3, 163–183 (1996)CrossRefGoogle Scholar
  25. 25.
    Rost, B., Sander, C.: Prediction of protein secondary structure at better than 70% accuracy. J. Molecular Biology 232, 584–599 (1993)CrossRefGoogle Scholar
  26. 26.
    Rost, B., Sander, C.: Improved prediction of protein secondary structure by use of sequence profiles and neural networks. Proc. Natl. Acad. Sci. USA 90, 7558–7562 (1993)CrossRefGoogle Scholar
  27. 27.
    Rost, B., Sander, C.: Combining evolutionary information and neural networks to predict protein secondary structure. Proteins 19, 55–72 (1994)CrossRefGoogle Scholar
  28. 28.
    Rost, B.: Better secondary structure prediction through more data. Columbia University,
  29. 29.
    Rost, B.: Rising accuracy of protein secondary structure prediction. In: Chasman, D. (ed.) Protein structure determination, analysis, and modeling for drug discovery, pp. 207–249. Marcel Dekker, New York (2003)Google Scholar
  30. 30.
    Salamov, A.A., Solovyev, V.V.: Prediction of protein secondary structure by combining nearest-neighbor algorithms and multiple sequence alignments. J. Molecular Biology 247, 11–15 (1995)CrossRefGoogle Scholar
  31. 31.
    Smith, T., Waterman, M.: Identification of common molecular subsequences. J. Molecular Biology 147, 195–197 (1981)CrossRefGoogle Scholar
  32. 32.
    Thompson, J., Higgins, D., Gibson, T.: Clustal w: Improving the sensitivity of progressive multiple sequence alignments through sequence weighting, position specific gap penalties and weight matrix choice. Nucleic Acids Res. 22, 4673–4680 (1994)CrossRefGoogle Scholar
  33. 33.
    Vapnik, V.: Statistical learning theory. John Wiley & Sons, New York (1998)zbMATHGoogle Scholar
  34. 34.
    Wang, L.-H., Liu, J.: Predicting Protein Secondary Structure by a Support Vector Machine Based on a New Coding Scheme. Genome Informatics 15(2), 181–190 (2004)Google Scholar
  35. 35.
    Ward, J.J., McGuffin, L.J., Buxton, B.F., Jones, D.T.: Secondary structure prediction with support vector machines. Bioinformatics 19(13), 1650–1655 (2003)CrossRefGoogle Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Gouchol Pok
    • 1
  • Keun Ho Ryu
    • 2
  • Yong J. Chung
    • 3
  1. 1.Yanbian University of Science and Technology, Department of Computer Science, Yanji, Jilin ProvinceChina
  2. 2.Chungbuk National University, Department of Computer Science, Cheongju, ChungbukKorea
  3. 3.Chungbuk National University, Department of Biochemistry, Cheongju, ChungbukKorea

Personalised recommendations