A Hybrid Method for the Protein Structure Prediction Problem

  • Márcio Dorn
  • Ardala Breda
  • Osmar Norberto de Souza
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5167)


This article provides the initial results of our effort to develop a hybrid prediction method, combining the principles of de novo and homology modeling, to help solve the protein three-dimensional (3-D) structure prediction problem. A target protein amino acid sequence is fragmented into many short contiguous fragments. Clustered short templates fragments, obtained from experimental protein structures in the Protein Data Bank (PDB), using the NCBI BLASTp program, were used for building an initial conformation, which was further refined by molecular dynamics simulations. We tested our method with the artificially designed alpha helical hairpin (PDB ID: 1ZDD) starting with its amino acids sequence only. The structure obtained with the proposed method is topologically a helical hairpin, with a C( RMSD of ~ 5.0 Å with respect to the experimental PDB structure for all 34 amino acids residues, and only ~ 2.0 Å when considering amino acids 1 to 22. We discuss further improvements to the method.


Protein 3-D structure ab initio prediction homology modeling molecular dynamics simulations 


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  1. 1.
    Baxevanis, A.D., Ouellette, B.F.F.: Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins, 560 p. Wiley and Sons, Hoboken (2005)Google Scholar
  2. 2.
    Branden, C., Tooze, J.: Introduction to Protein Structure, 410 p. Garlang Publishing Inc., New York (1998)Google Scholar
  3. 3.
    Anfinsen, C.B., Haber, E., Sela, M., White Jr., F.H.: Proceedings of the National Academy of Sciences USA.  47, 1309–1314 (1961)Google Scholar
  4. 4.
    Creighton, T.E.: Protein Folding. Biochemical Journal 270, 1–16 (1990)Google Scholar
  5. 5.
    Bujnicki, J.M.: Protein Structure Prediction by Recombination of Fragments. Chembiochem. 7(1), 19–27 (2006)CrossRefGoogle Scholar
  6. 6.
    Tramontano, A.: Protein Structure Prediction, 228 p. John Wiley and Sons, Weinheim (2006)Google Scholar
  7. 7.
    Osguthorpe, D.J.: Ab initio Protein Folding. Current Opinion in Structural Biology 10, 146–152 (2000)CrossRefGoogle Scholar
  8. 8.
    Moult, J.: A Decade of CASP: Progress, Bottlenecks and Prognosis in Protein Structure Prediction. Current Opinion in Structural Biology 15, 285–289 (2005)CrossRefGoogle Scholar
  9. 9.
    Tramontano, A., Morea, V.: Assessment of homology based predictions in CASP5. Proteins: Structure, Function, and Bioinformatics 53, 352–368 (2003)CrossRefGoogle Scholar
  10. 10.
    Kolinski, A.: Protein Modeling and Structure Prediction with a Reduced Representation. Acta Biochimica Polonica 51, 349–371 (2004)Google Scholar
  11. 11.
    Jones, D.T., Taylort, W.R., Thornton, J.M.: A New Approach to Protein Fold. Nature 358, 86–89 (1992)CrossRefGoogle Scholar
  12. 12.
    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 Research 28(1), 235–242 (2000)CrossRefGoogle Scholar
  13. 13.
    Marti-Renom, M.A., Stuart, A., Fiser, A., Sánchez, R., Melo, F., Sali, A.: Comparative Protein Structure Modeling of genes and genomes. Annual Review of Biophysics and Biomolecular Structure 29, 291–325 (2000)CrossRefGoogle Scholar
  14. 14.
    Ngo, J.T., Marks, J., Karplus, M.: Computational Complexity, protein structure prediction and the Levinthal Paradox. In: Merz Jr., K., Grand, S.L. (eds.) The Protein Folding Problem and Tertiary Structure Prediction, ch. 14, pp. 435–508. Birkhäuser, Boston (1997)Google Scholar
  15. 15.
    Levinthal, C.: Are there pathways for protein folding? Journal de Chimie Physique et de Physico-Chimie Biologique 65, 44–45 (1968)Google Scholar
  16. 16.
    Altschul, S.F., Madden, T.L., Schäffer, 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 Research 25, 3389–3402 (1997)CrossRefGoogle Scholar
  17. 17.
    Chapman, B., Chang, J.: Biopython: Python Tools for Computational Biology. ACM SIGBIO Newsletter 20(2), 15–19 (2002)CrossRefGoogle Scholar
  18. 18.
    Ramachandran, G.N., Sasisekharan, V.: Advances in Protein Chemistry 23, 238–437 (1968)Google Scholar
  19. 19.
    Hovmöller, T.Z., Ohlson, T.: Conformation of Amino Acids in Proteins. Acta Crystallographica D58, 768–776 (2002)Google Scholar
  20. 20.
    Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn., 525 p. Elsevier, San Francisco (2005)zbMATHGoogle Scholar
  21. 21.
    Case, D.A., Cheatham, T.E., Darden, T., Gohlke, H., Luo, R., Merz, K.M., Onufriev, A., Simmerling, C., Wang, B., Woods, R.J.: The AMBER Biomolecular Simulation Programs. Journal of Computational Chemistry 26(16), 1668–1688 (2005)CrossRefGoogle Scholar
  22. 22.
    Cornell, W.D., Cieplak, P., Bayly, C.I., Gould, I.R., Merz Jr., K.M., Ferguson, D.M., Spellmeyer, D.C., Fox, T., Caldwell, J.W., Kollman, P.A.: A Second Generation Force Field for the Simulation of Proteins. Journal of the American Chemical Society 117, 5179–5197 (1995)CrossRefGoogle Scholar
  23. 23.
    Starovasnik, M.A., Braisted, A.C., Wells, J.A.: Structural Mimicry of a Native Protein by a Minimized Binding Domain. Proceedings of the National Academy of Sciences USA 94, 10080–10085 (1997)CrossRefGoogle Scholar
  24. 24.
    Murzin, A.G., Brenner, S.E., Hubbard, T., Chothia, C.: SCOP: a Structural Classification of Proteins Database for the Investigation of Sequences and Structures. Journal of Molecular Biology 247, 536–540 (1995)Google Scholar
  25. 25.
    Bashford, D., Case, D.A.: Generalized Born Models of Macromolecular Solvation Effects. Annual Review Physical Chemistry 51, 129–152 (2000)CrossRefGoogle Scholar
  26. 26.
    Ryckaert, J.P., Ciccotti, G., Berendsen, H.J.C.: Numerical Integration of the Cartesian Equation of Motion of a System with Constraints: Molecular Dynamics of N-alkanes. Journal of Computational Physics 23, 327–341 (1977)CrossRefGoogle Scholar
  27. 27.
    Guex, N., Peitsch, M.C.: SWISS-MODEL and The Swiss-PdbViewer: An Environment for Comparative Protein Modeling. Electrophoresis 18, 2714–2723 (1997)CrossRefGoogle Scholar
  28. 28.
    DeLano, W.L.: The PyMOL Molecular Graphics System. DeLano Scientific, San Carlos (2002)Google Scholar
  29. 29.
    Laskowski, R.A., MacArthur, M.W., Moss, D.S., Thornton, J.M.: PROCHECK: A Program to Check the Stereochemical Quality of Protein Structures. Journal of Applied Crystallography 26, 283–291 (1993)CrossRefGoogle Scholar
  30. 30.
    Kabsch, W., Sander, C.: Dictionary of Protein Secondary Structure: Pattern Recognition of Hydrogen-Bonded and Geometrical Features. Biopolymers 22, 2577–2637 (1983)CrossRefGoogle Scholar
  31. 31.
    Clarke, D.T., Doig, A.J., Stapley, B.J., Jones, G.R.: The alpha-helix Folds on the Millisecond Time Scale. Proceedings of the National Academy of Sciences USA 96, 7232–7237 (1999)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Márcio Dorn
    • 1
  • Ardala Breda
    • 1
  • Osmar Norberto de Souza
    • 1
  1. 1.Laboratório de Bioinformática, Modelagem e Simulação de Biossistemas – LABIO Programa de Pós-Graduação em Ciência da Computação - Faculdade de InformáticaPUCRSPorto AlegreBrasil

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