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BetaMDGP: Protein Structure Determination Algorithm Based on the Beta-complex

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Part of the book series: Lecture Notes in Computer Science ((TCOMPUTATSCIE,volume 8360))

Abstract

The molecular distance geometry problem (MDGP) is a fundamental problem in determining molecular structures from the NMR data. We present a heuristic algorithm, the BetaMDGP, which outperforms existing algorithms for solving the MDGP. The BetaMDGP algorithm is based on the beta-complex, which is a geometric construct extracted from the quasi-triangulation derived from the Voronoi diagram of atoms. Starting with an initial tetrahedron defined by the centers of four closely located atoms, the BetaMDGP determines a molecular structure by adding one shell of atoms around the currently determined substructure using the beta-complex. The proposed algorithm has been entirely implemented and tested with atomic arrangements stored in an NMR format created from PDB files. Experimental results are also provided to show the powerful capability of the proposed algorithm.

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References

  1. Donald, B.R.: Algorithms in Structural Molecular Biology. The MIT Press (2011)

    Google Scholar 

  2. Cavanagh, J., Fairbrother, W.J., Palmer III, A.G., Rance, M., Skelton, N.J.: Protein NMR spectroscopy: principles and practice. Academic Press (2006)

    Google Scholar 

  3. Jan, D.: Principals of protein X-ray crystallography. Springer (2006)

    Google Scholar 

  4. Blumenthal, L.M.: Theory and Applications of Distance Geometry. Oxford Clarendon Press (1953)

    Google Scholar 

  5. Crippen, G., Havel, T.: Distance Geometry and Molecular Conformation. John Wiley & Sons, New York (1988)

    MATH  Google Scholar 

  6. Liberti, L., Lavor, C., Maculan, N., Mucherino, A.: Euclidean distance geometry and applications. SIAM Review 56 (article in press, 2014)

    Google Scholar 

  7. Havel, T.F.: Distance Geometry, vol. 4. John Wiley & Sons (1995)

    Google Scholar 

  8. Saxe, J.: Embeddability of weighted graphs in k-space is strongly np-hard. In: Proceedings of 17th Allerton Conference in Communications Control and Computing, pp. 480–489 (1979)

    Google Scholar 

  9. Moré, J.J., Wu, Z.: Global continuation for distance geometry problems. SIAM Journal of Optimization 7, 814–836 (1997)

    Article  MATH  Google Scholar 

  10. Moré, J.J., Wu, Z.: Distance geometry optimization for protein strucutures. Journal of Global Optimization 15(3), 219–234 (1999)

    Article  MATH  MathSciNet  Google Scholar 

  11. An, L.T.H.: Solving large scale molecular distance geometry problems by a smoothing technique via the gaussian transform and d.c. programming. Journal of Global Optimization 27, 375–397 (2003)

    Article  MATH  Google Scholar 

  12. An, L.T.H., Tao, P.D.: Large-scale molecular optimization from distance matrices by a d.c. optimization approach. SIAM Journal of Optimization 14(1), 77–114 (2003)

    Article  MATH  Google Scholar 

  13. Liberti, L., Lavor, C., Mucherino, A., Maculan, N.: Molecular distance geometry methods: from continuous to discrete. International Transactions in Operational Research 18, 33–51 (2010)

    Article  MathSciNet  Google Scholar 

  14. Wüthrich, K.: NMR in Structural Biology. World Scientific, New York (1995)

    Google Scholar 

  15. Havel, T.: An evaluation of computational strategies for use in the determination of protein structure from distance constraints obtained by nuclear magnetic resonance. Progress in Biophysics and Molecular Biology 56(1), 43–78 (1991)

    Article  Google Scholar 

  16. Hendrickson, B.: The Molecular Problem: Determining Conformation from Pairwise Distances. PhD thesis, Cornell University (1991)

    Google Scholar 

  17. Hendrickson, B.: The molecule problem: Exploiting structure in global optimization. SIAM Journal of Optimization 5, 835–857 (1995)

    Article  MATH  MathSciNet  Google Scholar 

  18. Dong, Q., Wu, Z.: A geometric build-up algorithm for solving the molecular distance geometry problem with sparse distance data. Journal of Global Optimization 26(3), 321–333 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  19. Wu, D., Wu, Z.: An updated geometric build-up algorithm for solving the molecular distance geometry problems with sparse distance data. Journal of Global Optimization 37(4), 661–673 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  20. Sit, A., Wu, Z., Yuan, Y.: A geometric buildup algorithm for the solution of the distance geometry problem using least-squares approximation. Bulletin of Mathematical Biology 71(8), 1914–1933 (2009)

    Article  MATH  MathSciNet  Google Scholar 

  21. Sit, A., Wu, Z.: Solving a generalized distance geometry problem for protein structure determination. Bulletin of Mathematical Biology, 1–28 (2011)

    Google Scholar 

  22. Brünger, A.T., Adams, P.D., Clore, G.M., DeLano, W.L., Gros, P., Grosse-Kunstleve, R., Jiang, J.S., Kuszewski, J., Nilges, M., Pannu, N.S., Read, R.J., Rice, L.M., Simonson, T., Warren, G.L.: Crystallography & nmr system: A new software suite for macromolecular structure determination. Acta Crystallographica Section D-Biological Crystallography D54, 905–921 (1998)

    Google Scholar 

  23. Schwieters, C.D., Kuszewski, J.J., Tjandra, N., Clore, G.M.: The xplor-nih nmr molecular structure determination package. Journal of Magnetic Resonance 160, 65–73 (2003)

    Article  Google Scholar 

  24. Lavor, C., Liberti, L., Maculan, N., Mucherino, A.: The discretizable molecular distance geometry problem. Computational Optimization and Applications 52, 115–146 (2012)

    Article  MATH  MathSciNet  Google Scholar 

  25. Liberti, L., Lavor, C., Maculan, N.: A branch-and-prune algorithm for the molecular distance geometry problem. International Transactions in Operational Research 15, 1–17 (2008)

    Article  MATH  MathSciNet  Google Scholar 

  26. Kim, D.S., Cho, Y., Sugihara, K., Ryu, J., Kim, D.: Three-dimensional beta-shapes and beta-complexes via quasi-triangulation. Computer-Aided Design 42(10), 911–929 (2010)

    Article  Google Scholar 

  27. Kim, D.S., Kim, J.K., Cho, Y., Kim, C.M.: Querying simplexes in quasi-triangulation. Computer-Aided Design 44(2), 85–98 (2012)

    Article  Google Scholar 

  28. Kim, D.S., Seo, J., Kim, D., Ryu, J., Cho, C.H.: Three-dimensional beta shapes. Computer-Aided Design 38(11), 1179–1191 (2006)

    Article  Google Scholar 

  29. Cho, Y., Kim, J.K., Ryu, J., Won, C.I., Kim, C.M., Kim, D., Kim, D.S.: BetaMol: a molecular modeling, analysis and visualization software based on the beta-complex and the quasi-triangulation. Journal of Advanced Mechanical Design, Systems, and Manufacturing 6(3), 389–403 (2012)

    Article  Google Scholar 

  30. Cho, Y., Kim, D., Kim, D.S.: Topology representation for the Voronoi diagram of 3D spheres. International Journal of CAD/CAM 5(1), 59–68 (2005), http://www.ijcc.org

    Google Scholar 

  31. Kim, D.S., Cho, Y., Kim, D.: Euclidean Voronoi diagram of 3D balls and its computation via tracing edges. Computer-Aided Design 37(13), 1412–1424 (2005)

    Article  MATH  Google Scholar 

  32. Okabe, A., Boots, B., Sugihara, K., Chiu, S.N.: Spatial Tessellations: Concepts and Applications of Voronoi Diagrams, 2nd edn. John Wiley & Sons, Chichester (1999)

    Google Scholar 

  33. Munkres, J.R.: Elements of Algebraic Topology. Perseus Press (1984)

    Google Scholar 

  34. Boissonnat, J.D., Yvinec, M.: Algorithmic Geometry. Cambridge University Press, Cambridge (1998)

    Book  MATH  Google Scholar 

  35. Kim, D.S., Kim, D., Cho, Y., Sugihara, K.: Quasi-triangulation and interworld data structure in three dimensions. Computer-Aided Design 38(7), 808–819 (2006)

    Article  Google Scholar 

  36. Kim, D.S., Cho, Y., Sugihara, K.: Quasi-worlds and quasi-operators on quasi-triangulations. Computer-Aided Design 42(10), 874–888 (2010)

    Article  MATH  Google Scholar 

  37. Kim, D.S., Cho, Y., Ryu, J., Kim, J.K., Kim, D.: Anomalies in quasi-triangulations and beta-complexes of spherical atoms in molecules. Computer-Aided Design 45(1), 35–52 (2013)

    Article  MathSciNet  Google Scholar 

  38. Kim, J.K., Kim, D.S.: Betasuperposer: Superposition of protein surfaces using beta-shapes. Journal of Biomolecular Structure & Dynamics 30(6), 684–700 (2012)

    Article  Google Scholar 

  39. Dunbrack Jr., R.L.: Rotamer libraries in the 21st century. Current Opinion in Structural Biology 12(4), 431–440 (2002)

    Article  Google Scholar 

  40. Dunbrack Jr., R.L., Karplus, M.: Backbone-dependent rotamer library for proteins. Journal of Molecular Biology 230(2), 543–574 (1993)

    Article  Google Scholar 

  41. Dunbrack Jr., R.L., Karplus, M.: Conformational analysis of the backbone-dependent rotamer preferences of protein sidechains. Journal of Molecular Biology 1(5), 334–340 (1994)

    Google Scholar 

  42. Kono, H.: Rotamer libraries for molecular modeling and design of proteins. In: Park, S.J., Cochran, J.R. (eds.) Protein Engineering and Design (2009)

    Google Scholar 

  43. Chazelle, B., Kingsford, C., Singh, M.: The inapproximability of side-chain positioning. Technical report, Princeton University (2004)

    Google Scholar 

  44. Fung, H., Rao, S., Floudas, C., Prokopyev, O., Pardalos, P., Rendl, F.: Computational comparison studies of quadratic assignment like formulations for the In silico sequence selection problem in De Novo protein design. Journal of Combinatorial Optimization 10(1), 41–60 (2005)

    Article  MATH  MathSciNet  Google Scholar 

  45. Pierce, N.A., Winfree, E.: Protein design is NP-hard. Protein Engineering 15(10), 779–782 (2002)

    Article  Google Scholar 

  46. Althaus, E., Kohlbacher, O., Lenhof, H.P., Müller, P.: A combinatorial approach to protein docking with flexible side-chains. In: RECOMB 2000 Proceedings of the Fourth Annual International Conference on Computational Molecular Biology, pp. 15–24 (2000)

    Google Scholar 

  47. Althaus, E., Kohlbacher, O., Lenhof, H.P., Müller, P.: A combinatorial approach to protein docking with flexible side chains. Journal of Computational Biology 9(4), 597–612 (2002)

    Article  Google Scholar 

  48. Lee, C., Subbiah, S.: Prediction of protein side-chain conformation by packing optimization. Journal of Molecular Biology 217(2), 373–388 (1991)

    Article  Google Scholar 

  49. Tuffery, P., Etchebest, C., Hazout, S., Lavery, R.: A new approach to the rapid determination of protein side chain conformations. Journal of Biomolecular Structure & Dynamics 8(6), 1267–1289 (1991)

    Article  Google Scholar 

  50. Leach, A.R.: Molecular Modelling: Principles and Applications. Prentice Hall (2001)

    Google Scholar 

  51. Ryu, J., Kim, D.S.: Protein structure optimization by side-chain positioning via beta-complex. Journal of Global Optimization (2012), doi: 10.1007/s10898-012-9886-3

    Google Scholar 

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Seo, J., Kim, JK., Ryu, J., Lavor, C., Mucherino, A., Kim, DS. (2014). BetaMDGP: Protein Structure Determination Algorithm Based on the Beta-complex. In: Gavrilova, M.L., Tan, C.J.K. (eds) Transactions on Computational Science XXII. Lecture Notes in Computer Science, vol 8360. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54212-1_7

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  • DOI: https://doi.org/10.1007/978-3-642-54212-1_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-54211-4

  • Online ISBN: 978-3-642-54212-1

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