Protein Structure Prediction pp 219-239

Part of the Methods in Molecular Biology™ book series (MIMB, volume 413) | Cite as

Roadmap Methods for Protein Folding

  • Mark Moll
  • David Schwarz
  • Lydia E. Kavraki

Summary

Protein folding refers to the process whereby a protein assumes its intricate three-dimensional shape. This chapter reviews a class of methods for studying the folding process called roadmap methods. The goal of these methods is not to predict the folded structure of a protein, but rather to analyze the folding kinetics. It is assumed that the folded state is known. Roadmap methods maintain a graph representation of sampled conformations. By analyzing this graph one can predict structure formation order, the probability of folding, and get a coarse view of the energy landscape.

Keywords

protein folding folding kinetics roadmap methods conformation sampling techniques energy landscape 

References

  1. 1.
    M. Gruebele. Protein folding: the free energy surface. Current Opinion in Structural Biology, 12:161–168, 2002.CrossRefPubMedGoogle Scholar
  2. 2.
    T. Head-Gordon and S. Brown. Minimalist models for protein folding and design. Current Opinion in Structural Biology, 13:160–167, 2003.CrossRefPubMedGoogle Scholar
  3. 3.
    X. Zhuang and M. Rief. Single-molecule folding. Current Opinion in Structural Biology, 13:88–97, 2003.CrossRefPubMedGoogle Scholar
  4. 4.
    M. Vendruscolo and E. Paci. Protein folding: bringing theory and experiment closer together. Current Opinion in Structural Biology, 13:82–87, 2003.CrossRefPubMedGoogle Scholar
  5. 5.
    C. M. Dobson. Protein folding and misfolding. Nature, 426:884–890,2003.CrossRefPubMedGoogle Scholar
  6. 6.
    J. N. Onuchic and P. G. Wolynes. Theory of protein folding. Current Opinion in Structural Biology, 14:70–75, 2004.CrossRefPubMedGoogle Scholar
  7. 7.
    C. M. Dobson. Principles of protein folding, misfolding and aggregation. Seminars in Cell & Developmental Biology, 15:3–16, 2004.CrossRefGoogle Scholar
  8. 8.
    M. S. Apaydin. Stochastic roadmap simulation: an efficient representation and algorithm for analyzing molecular motion. PhD thesis, Stanford University, Stanford, CA 94305 USA, Aug 2004.Google Scholar
  9. 9.
    S. L. Thomas, X. Tang, L. Tapia, and N. M. Amato. Simulating protein motions with rigidity analysis. In Proceedings of the ACM International Conference on Research in Computational Molecular Biology (RECOMB), pages 394–409, 2006.Google Scholar
  10. 10.
    S. Thomas, G. Song, and N. M. Amato. Protein folding by motion planning. Physical Biology, 2:S148–S155, 2005.CrossRefPubMedGoogle Scholar
  11. 11.
    N. M. Amato, K. A. Dill, and G. Song. Using motion planning to map protein folding landscapes and analyze folding kinetics of known native structures. Journal of Computational Biology: A Journal of Computational Molecular Cell Biology, 10(3–4):239–255, 2003.Google Scholar
  12. 12.
    G. Song. A motion planning approach to protein folding. PhD thesis, Dept. of Computer Science, Texas A&M University, December 2003.Google Scholar
  13. 13.
    N. M. Amato and G. Song. Using motion planning to study protein folding pathways. Journal of Computational Biology: A Journal of Computational Molecular Cell Biology, 9(2):149–168, 2002.Google Scholar
  14. 14.
    N. Singhal and V. S. Pande. Error analysis and efficient sampling in Markovian state models for molecular dynamics. The Journal of Chemical Physics, 123(20):204909,2005.CrossRefPubMedGoogle Scholar
  15. 15.
    N. Singhal, C. D. Snow, and V. S. Pande. Using path sampling to build better Markovian state models: predicting the folding rate and mechanism of a tryptophan zipper beta hairpin. The Journal of Chemical Physics, 121(1):415–425,2004.CrossRefPubMedGoogle Scholar
  16. 16.
    T.-H. Chiang, M. S. Apaydin, D. L. Brutlag, D. Hsu, and J.-C. Latombe. Predicting experimental quantities in protein folding kinetics using stochastic roadmap simulation. In Proceedings of the ACM International Conference on Research in Computational Molecular Biology (RECOMB), pages 410–424, 2006.Google Scholar
  17. 17.
    M. S. Apaydin, D. L. Brutlag, C. Guestrin, D. Hsu, and J.-C. Latombe. Stochastic conformational roadmaps for computing ensemble properties of molecular motion. In J. D. Boissonnat, J. Burdick, K. Goldberg, and S. Hutchinson, editors, Algorithmic Foundations of Robotics V, pages 131–147. Springer,2004.Google Scholar
  18. 18.
    M. S. Apaydin, D. L. Brutlag, C. Guestrin, D. Hsu, J.-C. Latombe, and C. Varma. Stochastic roadmap simulation: an efficient representation and algorithm for analyzing molecular motion. Journal of Computational Biology: A Journal of Computational Molecular Cell Biology, 10(3–4):257–281, 2003.Google Scholar
  19. 19.
    M. S. Apaydin, C. E. Guestrin, C. Varma, D. L. Brutlag, and J.-C. Latombe. Stochastic roadmap simulation for the study of ligand- protein interactions. Bioinformatics, 18 Suppl 2:18–26, 2002.Google Scholar
  20. 20.
    M. Karplus and J. Kuriyan. Molecular dynamics and protein function. Proceedings of the National Academy of Sciences of the United States of America, 102:6679–6685, 2005.CrossRefPubMedGoogle Scholar
  21. 21.
    D. R. Ripoll, J. A. Vila, and H. A. Scheraga. Folding of the villin headpiece subdomain from random structures. Analysis of the charge distribution as a function of pH. Journal of Molecular Biology, 339(4):915–925, 2004.CrossRefPubMedGoogle Scholar
  22. 22.
    W. F. van Gunsteren and H. J. C. Berendsen. Computer simulation of molecular dynamics: methodology, applications and perspectives in chemistry. Angewandte Chemie International Edition in English, 29(9):992–1023, 1990.CrossRefGoogle Scholar
  23. 23.
    T. Huber, A. E. Torda, and W. F. van Gunsteren. Local elevation: a method for improving the searching properties of molecular dynamics simulation. Journal of Computer-Aided Molecular Design, 8(6):695–708, 1994.CrossRefPubMedGoogle Scholar
  24. 24.
    B. G. Schulze, H. Grubmueller, and J. D. Evanseck. Functional significance of hierarchical tiers in carbonmonoxy myoglobin: conformational substates and transitions studied by conformational flooding simulations. Journal of the American Chemical Society, 122(36):8700–8711, 2000.CrossRefGoogle Scholar
  25. 25.
    Y. Zhang, D. Kihara, and J. Skolnick. Local energy landscape flattening: parallel hyperbolic Monte Carlo sampling of protein folding. Proteins, 48(2): 192–201, 2002.CrossRefPubMedGoogle Scholar
  26. 26.
    K. Lindorff-Larsen, R. B. Best, M. A. DePristo, C. M. Dobson, and M. Vendruscolo. Simultaneous determination of protein structure and dynamics. Nature, 433(7022):128–132, 2005.CrossRefPubMedGoogle Scholar
  27. 27.
    L. E. Kavraki, P. Švestka, J.-C. Latombe, and M. H. Overmars. Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE Transactions on Robotics and Automation: A Publication of the IEEE Robotics and Automation Society, 12(4):566–580, 1996.Google Scholar
  28. 28.
    A. D. MacKerell, Jr. Empirical force fields for biological macromolecules: overview and issues. Journal of Computational Chemistry, 25(13):1584–1604, 2004.CrossRefPubMedGoogle Scholar
  29. 29.
    M. Zhang and L. E. Kavraki. A new method for fast and accurate computation of molecular conformations. Journal of Chemical Information and Computer Sciences, 42:64–70, 2002.PubMedGoogle Scholar
  30. 30.
    S. Sun, P. D. Thomas, and K. A. Dill. A simple protein folding algorithm using a binary code and secondary structure constraints. Protein Engineering, 8(8):769–778, 1995.CrossRefPubMedGoogle Scholar
  31. 31.
    H. Choset, K. M. Lynch, S. Hutchinson, G. Kantor, W. Burgard, L. E. Kavraki, and S. Thrun. Principles of Robot Motion: Theory, Algorithms, and Implementations. MIT Press, 2005.Google Scholar
  32. 32.
    J.-C. Latombe. Robot Motion Planning, chapter 7, pages 295–353. Kluwer, Dordrecht; Boston, 1991.Google Scholar
  33. 33.
    S. M. LaValle and J. J. Kuffner. Randomized kinodynamic planning. The International Journal of Robotics Research, 20(5):378–400, 2001.CrossRefGoogle Scholar
  34. 34.
    D. Hsu, J.-C. Latombe, and R. Motwani. Path planning in expansive configuration spaces. International Journal of Computational Geometry and Applications, 9(4–5):495–512, 1999.Google Scholar
  35. 35.
    A. Ladd and L. E. Kavraki. Fast exploration for robots with dynamics. In Workshop on the Algorithmic Foundations of Robotics, 2004.Google Scholar
  36. 36.
    M. Moll, M. D. Schwarz, A. Heath, and L. E. Kavraki. On flexible docking using expansive search. Technical Report 04-443, Rice University, Houston, TX, 2004.Google Scholar
  37. 37.
    J. Cortés, T. Siméon, V. R. de Angulo, D. Guieysse, M. Remauld-Siméon, and V. Tran. A path planning approach for computing large-amplitude motions of flexible molecules. Bioinformatics, 21 Suppl. 1:i116–i125,2005.CrossRefGoogle Scholar
  38. 38.
    T. J. Brunette and O. Brock. Improving protein structure prediction with model-based search. Bioinformatics, 21 Suppl. 1:i66–i74, 2005.CrossRefGoogle Scholar
  39. 39.
    T. J. Brunette and O. Brock. Model-based search to determine minima in molecular energy landscapes. Technical Report 04-48, Dept. of Computer Science, University of Massachusetts, Amherst, MA, 2005.Google Scholar
  40. 40.
    J. Cortés, T. Siméon, M. Remaud-Siméon, and V. Tran. Geometric algorithms for the conformational analysis of long protein loops. Journal of Computational Chemistry, 25(7):956–967, 2004.CrossRefPubMedGoogle Scholar
  41. 41.
    A. P. Singh, J.-C. Latombe, and D. L. Brutlag. A motion planning approach to flexible ligand binding. In Proceedings of Seventh International Conference on Intelligent Systems for Molecular Biology (ISMB), pages 252–261, 1999.Google Scholar
  42. 42.
    G. N. Ramachandran and V. Sasisekharan. Conformation of polypeptides and proteins. Advances in Protein Chemistry, 23:283–438, 1968.CrossRefPubMedGoogle Scholar
  43. 43.
    T. H. Cormen, C. E. Leiserson, R. R. Rivest, and C. Stein. Introduction to Algorithms. McGraw-Hill, second edition, 1990.Google Scholar
  44. 44.
    R. Du, V. Pande, A. Y. Grosberg, T. Tanaka, and E. Shakhnovich. On the transition coordinate for protein folding. The Journal of Chemical Physics, 108:334–350, 1998.CrossRefGoogle Scholar
  45. 45.
    H. M. Berman, J. Westbrook, Z. Feng, G. Gilliland, T. N. Bhat, H. Weissig, I. N. Shindyalov, and P. E. Bourne. The protein data bank. Nucleic Acids Research, 28:235–242, 2000.CrossRefPubMedGoogle Scholar
  46. 46.
    S. O. Garbuzynskiy, A. V. Finkelstein, and O. V. Galzitskaya. Outlining folding nuclei in globular proteins. Journal of Molecular Biology, 336:509–525, 2004.CrossRefPubMedGoogle Scholar
  47. 47.
    A. Fersht. Structure and Mechanism in Protein Science: A Guide to Enzyme Catalysis and Protein Folding. W.H. Freeman & Company, 1999.Google Scholar
  48. 48.
    M. Shirts and V. S. Pande. Screen savers of the world unite Science, 290:1903–1904, 2000.CrossRefPubMedGoogle Scholar

Copyright information

© Humana Press Inc 2008

Authors and Affiliations

  • Mark Moll
  • David Schwarz
  • Lydia E. Kavraki

There are no affiliations available

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