Advertisement

Abstract

Many large protein complexes undergo extensive conformational changes as part of their functionality. Tracing these changes is important for understanding the way these proteins function. It is not always possible to obtain a high resolution structure for very large complexes. Electron cryo-microscopy (Cryo-EM) enables the representation of large macromolecular structures at a medium resolution level (4–8Å). Traditional conformational search methods cannot be applied to medium resolution data where structural information may be partial or missing. Additionally, simulating large scale conformational changes in proteins require a massive amount of computational efforts. We apply a search method from robotics to structural information obtained from medium resolution Cryo-EM maps, modeled to approximate backbone trace level. The pathways obtained by this method can be useful in understanding protein motions, providing reliable results for the medium resolution data. To provide a baseline validation for our method, we tested it on Adenylate Kinase and Cyanovirin. To test the data on actual cryo-EM determined structures, we simulated the conformational opening of the GroEL single ring complex. We show that we can produce low energy conformational pathways which correspond to known structural data. The method presented here is a promising step towards exploring the conformational motion of even larger complexes.

Keywords

Secondary Structure Element Adenylate Kinase Normal Mode Analysis Goal Structure High Resolution 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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Perutz, M.F.: Mechanisms of cooperativity and allosteric regulation in proteins. Quart. Rev. Biophys. 22, 139–236 (1989)CrossRefGoogle Scholar
  2. 2.
    Schmid, M.F., Sherman, M.B., Matsudaira, P., Chiu, W.: Structure of the acrosomal bundle. Nature 431, 104–107 (2004)CrossRefGoogle Scholar
  3. 3.
    Jiang, W., Li, Z., Zhang, Z., Baker, M., Prevelige Jr., P.E., Chiu, W.: Coat protein fold and maturation transition of bacteriophage P22 seen at subnanometer resolutions. Nature Structural Biology 10(2), 131–135 (2003)CrossRefGoogle Scholar
  4. 4.
    Schroeder, G., Brunger, A.T., Levitt, M.: Combining efficient conformational sampling with a deformable elastic network model facilitates structure refinement at low resolution. Structure 15, 1630–1641 (2007)CrossRefGoogle Scholar
  5. 5.
    Lasker, K., Dror, O., Shatsky, M., Nussinov, R., Wolfson, H.J.: EMatch: discovery of high resolution structural homologues of protein domains in intermediate resolution cryo-EM maps. IEEE/ACM Trans. Comput. Biol. Bioinform. 4(1), 28–39 (2007)CrossRefGoogle Scholar
  6. 6.
    Case, D.A., Cheatham, T., Darden, T., Gohlke, H., Luo, R., Merz Jr., K.M., Onufriev, A., Simmerling, C., Wang, B., Woods, R.: The Amber biomolecular simulation programs. J. Computat. Chem. 26, 1668–1688 (2005)CrossRefGoogle Scholar
  7. 7.
    Kirkpatrick, S., Gelatt Jr., C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220, 671–680 (1983)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Head-Gordon, T., Brown, S.: Minimalist models for protein folding and design. Curr. Opin. Struct. Biol. 13(2), 160–167 (2003)CrossRefGoogle Scholar
  9. 9.
    Whitford, P.C., Miyashita, O., Levy, Y., Onucic, J.N.: Conformational transitions of adenylate kinase: Switching by cracking. Journal of Molecular Biology 366(5), 1661–1671 (2007)CrossRefGoogle Scholar
  10. 10.
    Schuyler, A., Jernigan, R., Qasba, P., Ramakrishnan, B., Chirikjian, G.: Iterative cluster-nma: A tool for generating conformational transitions in proteins. Proteins 74, 760–776 (2009)CrossRefGoogle Scholar
  11. 11.
    Zheng, W., Brooks, B.: Identification of dynamical correlations within the myosin motor domain by the normal mode analysis of an elastic network model. J. Mol. Biol. 346(3), 745–759 (2005)CrossRefGoogle Scholar
  12. 12.
    Temiz, N., Meirovitch, E., Bahar, I.: Escherichia coli adenylate kinase dynamics: comparison of elastic network model modes with mode-coupling (15)n-nmr relaxation data. Proteins 57, 468–480 (2004)CrossRefGoogle Scholar
  13. 13.
    Gohlke, H., Thorpe, M.: A natural coarse graining for simulating large biomolecular motion. Biophysical Journal 9, 2115–2120 (2006)CrossRefGoogle Scholar
  14. 14.
    Weiss, D., Levitt, M.: Can morphing methods predict intermediate structures? J. Mol. Biol. 385, 665–674 (2009)CrossRefGoogle Scholar
  15. 15.
    Choset, H., Lynch, K.M., Hutchinson, S., Kantor, G., Burgard, W., Kavraki, L.E., Thrun, S.: Principles of Robot Motion: Theory, Algorithms, and Implementations. MIT Press (2005)Google Scholar
  16. 16.
    Kavraki, L.E., Švestka, P., Latombe, J.-C., Overmars, M.H.: Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE Transactions on Robotics and Automation 12, 566–580 (1996)CrossRefGoogle Scholar
  17. 17.
    Shehu, A., Kavraki, L., Clementi, C.: On the characterization of protein native state ensembles. Biophysical Journal 92(5), 1503–1511 (2007)CrossRefGoogle Scholar
  18. 18.
    Shehu, A., Kavraki, L., Clementi, C.: Multiscale characterization of protein conformational ensembles. Proteins: Structure, Function and Bioinformatics 76(4), 837–851 (2009)CrossRefGoogle Scholar
  19. 19.
    Haspel, N., Geisbrech, B., Lambris, J., Kavraki, L.E.: Multi-scale characterization of the energy landscape of proteins with application to the c3d/efb-c complex. Proteins: Structure, Function and Bioinformatics 78(4), 1004–1014 (2010)CrossRefGoogle Scholar
  20. 20.
    Cortés, J., Siméon, T., de Angulo, V.R., Guieysse, D., Remauld-Siméon, M., Tran, V.: A path planning approach for computing large-amplitude motions of flexible molecules. Bioinformatics 21(suppl. 1), i116–i125 (2005)Google Scholar
  21. 21.
    Shehu, A., Clementi, C., Kavraki, L.E.: Sampling conformation space to model equilibrium fluctuations in proteins. Algorithmica 48, 303–327 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  22. 22.
    Thomas, S., Tang, X., Tapia, L., Amato, N.M.: Simulating protein motions with rigidity analysis. J. Comp. Biol. 14(6), 839–855 (2007)CrossRefzbMATHGoogle Scholar
  23. 23.
    Chiang, T.H., Apaydin, M.S., Brutlag, D.L., Hsu, D., Latombe, J.-C.: Using stochastic roadmap simulation to predict experimental quantities in protein folding kinetics. J. Comp. Biol. 14(5), 578–593 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  24. 24.
    Yao, P., Dhanik, A., Marz, N., Propper, R., Kou, C., Liu, G., van den Bedem, H., Latombe, J.-C., Halperin-Landsberg, I., Altman, R.B.: Efficient algorithms to explore conformation spaces of flexible protein loops. IEEE/ACM Trans. Comput. Biol. Bioinform. 5(4), 534–545 (2008)CrossRefGoogle Scholar
  25. 25.
    Tang, X., Thomas, S., Tapia, L., Amato, N.M.: Tools for simulating and analyzing rna folding kinetics. In: Proc. Int. Conf. Comput. Molecular Biology (RECOMB), San Francisco, CA, USA, pp. 268–282 (April 2007)Google Scholar
  26. 26.
    Shehu, A., Clementi, C., Kavraki, L.E.: Modeling protein conformational ensembles: From missing loops to equilibrium fluctuations. Proteins: Structure, Function and Bioinformatics 65, 164–179 (2006)CrossRefGoogle Scholar
  27. 27.
    Raveh, B., Enosh, A., Furman-Schueler, O., Halperin, D.: Rapid sampling of molecular motions with prior information constraints. Plos Comp. Biol. (2009) (in press)Google Scholar
  28. 28.
    Haspel, N., Moll, M., Baker, M., Chiu, W., Kavraki, L.E.: Tracing conformational changes in proteins. BMC Structural Biology (2010) (in press)Google Scholar
  29. 29.
    Baker, M.L., Ju, T., Chiu, W.: Identification of secondary structure elements in intermediate resolution density maps. Structure 15, 7–19 (2007)CrossRefGoogle Scholar
  30. 30.
    Abeysinghe, S.S., Ju, T., Baker, M., Chiu, W.: Shape modeling and matching in identifying protein structure from low-resolution images. In: ACM Symposium on Solid and Physical Modeling, pp. 223–232 (2007)Google Scholar
  31. 31.
    Ludtke, S.J., Baldwin, P.R., Chiu, W.: EMAN: semiautomated software for high-resolution single-particle reconstructions. J. Struct. Biol. 128, 82–97 (1999)CrossRefGoogle Scholar
  32. 32.
    Ju, T., Baker, M.L., Chiu, W.: Computing a family of skeletons of volumetric models for shape description. Computer Aided Design 39(5), 352–360 (2007)CrossRefGoogle Scholar
  33. 33.
    Zhang, J., Baker, M., Schroeder, G., Douglas, N., Reissman, S., Jakana, J., Dougherty, M., Fu, C., Levitt, M., Ludtke, S., Frydman, J., Chiu, W.: Mechanism of folding chamber closure in a group ii chaperonin. Nature 463, 379–383 (2010)CrossRefGoogle Scholar
  34. 34.
    Ballester, P.J., Richards, W.G.: Ultrafast shape recognition to search compound databases for similar molecular shapes. J. Comput. Chem. 28(10), 1711–1723 (2007)CrossRefGoogle Scholar
  35. 35.
    Brown, S., Fawzi, N., Head-Gordon, T.: Coarse grained sequences for protein folding and design. Proc. Nat. Acad. USA 100, 10,712–10,717 (2003)Google Scholar
  36. 36.
    Doruker, P., Jernigan, R., Bahar, I.: Dynamics of large proteins through hierarchical levels of coarse-grained structures. J. Comput. Chem. 23(1), 119–127 (2002)CrossRefGoogle Scholar
  37. 37.
    Ladd, A.M.: Motion planning for physical simulation. Ph.D. dissertation, Dept. of Computer Science, Rice University, Houston, TX (Dec. 2006)Google Scholar
  38. 38.
    Şucan, I.A., Kruse, J.F., Yim, M., Kavraki, L.E.: Reconfiguration for modular robots using kinodynamic motion planning. In: ASME Dynamic Systems and Control Conference. Michigan, Ann Arbor (2008)Google Scholar
  39. 39.
    Tsianos, K., Kavraki, L.E.: Replanning: A powerful planning strategy for hard kinodynamic problems. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, Nice, France, pp. 1667–1672 (September 2008)Google Scholar
  40. 40.
    Botos, I., O’Keefe, B., Shenoy, S., Cartner, L., Ratner, D., et al.: Structures of the complexes of a potent anti-hiv protein cyanovirin-n and high mannose oligosaccharides. J. Biol. Chem. 277, 34336–34342 (2002)CrossRefGoogle Scholar
  41. 41.
    Zeilstra-Ryalls, J., Fayet, O., Georgopolous, C.: The universally conserved GroE (Hsp60) chaperonins. Annu. Rev. Microbiol. 45, 301–325 (1991)CrossRefGoogle Scholar
  42. 42.
    Heath, A.P., Kavraki, L.E., Clementi, C.: From coarse-grain to all-atom: Toward multiscale analysis of protein landscapes. Proteins: Structure, Function and Bioinformatics 68(3), 646–661 (2007)CrossRefGoogle Scholar

Copyright information

© ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering 2012

Authors and Affiliations

  • Nurit Haspel
    • 1
  1. 1.Department of Computer ScienceUniversity of Massachusets BostonBostonUSA

Personalised recommendations