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A Grid-Based Hybrid Hierarchical Genetic Algorithm for Protein Structure Prediction

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Parallel and Distributed Computational Intelligence

Part of the book series: Studies in Computational Intelligence ((SCI,volume 269))

Abstract

A hybrid hierarchical conformational sampling evolutionary algorithm is presented in this chapter, relying on different parallelization models. After first reviewing general conformational sampling aspects, e.g. existing approaches, complexity matters, force field functions, a focus is considered for the protein structure prediction problem. Furthermore, having as basis the highly multimodal nature of the energy landscape structure, a hybrid evolutionary approach is defined, enclosing conjugate gradient and adaptive simulated annealing enforced components. An insular model is employed, the conformational sampling process being conducted on a collaborative basis. Nonetheless, although low energy conformations were obtained, no close to native conformations were attained. Consequently, a higher complexity hierarchical paradigm has been constructed, with incentive following results.

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References

  1. Alba, E., Talbi, E.G., Luque, G., Melab, N.: Metaheuristics and parallelism. In: Parallel Metaheuristics: A New Class of Algorithms. Wiley Series on Parallel and Distributed Computing, vol. 4, pp. 79–104. Wiley, Chichester (2005)

    Google Scholar 

  2. Alder, B., Wainwright, T.: Phase transition for a hard sphere system. Journal of Chemical Physics 27, 1208–1209 (1957)

    Article  Google Scholar 

  3. Alder, B., Wainwright, T.: Studies in molecular dynamics. i. general method. Journal of Chemical Physics 31, 459–466 (1959)

    Article  MathSciNet  Google Scholar 

  4. Bernstein, F., Koetzle, T., Williams, G., Meyer Jr., E.F., Brice, M., Rodgers, J., Kennard, O., Shimanouchi, T., Tasumi, M.: The protein data bank: a computer-based archival file for macromolecular structures. Journal of Molecular Biology 112, 535–542 (1977)

    Article  Google Scholar 

  5. Buck, I.: Gpu computing: Programming a massively parallel processor. In: Proceedings of the International Symposium on Code Generation and Optimization (CGO 2007), p. 17. IEEE Computer Society, Washington (2007)

    Chapter  Google Scholar 

  6. Butenhof, D.: Programming with POSIX Threads. Professional Computing Series. Addison-Wesley Longman Publishing Co., Boston (1997)

    Google Scholar 

  7. Cahon, S., Melab, N., Talbi, E.G.: Paradiseo: A framework for the reusable design of parallel and distributed metaheuristics. Journal of Heuristics 10(3), 357–380 (2004)

    Article  Google Scholar 

  8. Cahon, S., Melab, N., Talbi, E.G.: An enabling framework for parallel optimization on the computational grid. In: Proceedings of International Symposium on Cluster Computing and the Grid (CCGrid 2005), vol. 2, pp. 702–709. IEEE Computer Society, Washington (2005)

    Chapter  Google Scholar 

  9. Calland, P.Y.: On the structural complexity of a protein. Protein Engineering 16(2), 79–86 (2003), http://peds.oxfordjournals.org/cgi/content/abstract/16/2/79

    Article  Google Scholar 

  10. Cantu-Paz, E.: Efficient and Accurate Parallel Genetic Algorithms. Kluwer Academic Publishers, Norwell (2000)

    MATH  Google Scholar 

  11. Cappello, F., Caron, E., Dayde, M., Desprez, F., Jegou, Y., Primet, P., Jeannot, E., Lanteri, S., Leduc, J., Melab, N., Mornet, G., Namyst, R., Quetier, B., Richard, O.: Grid’5000: A Large Scale and Highly Reconfigurable Grid Experimental Testbed. In: Proceedings of IEEE/ACM International Workshop on Grid Computing (GRID 2005), pp. 99–106. IEEE Computer Society, Washington (2005), http://dx.doi.org/10.1109/GRID.2005.1542730

    Chapter  Google Scholar 

  12. Cozzone, A.: Proteins: Fundamental chemical properties. Encyclopedia of Life Sciences, 1–10 (2002), http://doi.wiley.com/10.1038/npg.els.0001330

  13. Crescenzi, P., Goldman, D., Papadimitriou, C., Piccolboni, A., Yannakakis, M.: On the complexity of protein folding. Journal of computational biology 5(3), 423–465 (1998)

    Article  Google Scholar 

  14. Dauber-Osguthorpe, P., Roberts, V., Osguthorpe, D., Wolff, J., Genest, M., Hagler, A.: Structure and energetics of ligand binding to proteins: Escherichia coli dihydrofolate reductase-trimethoprim, a drug-receptor system. Proteins: Structure, Function, and Genetics 4(1), 31–47 (1988), http://dx.doi.org/10.1002/prot.340040106

    Article  Google Scholar 

  15. Dill, K.: Theory for the folding and stability of globular proteins. Biochemistry 24(6), 1501–1509 (1985), http://view.ncbi.nlm.nih.gov/pubmed/3986190

    Article  Google Scholar 

  16. Dorsett, H., White, A.: Overview of molecular modelling and ab initio molecular orbital methods suitable for use with energetic materials. Tech. Rep. DSTO-GD-0253, Department of Defense, Weapons Systems Division, Aeronautical and Maritime Research Laboratory, Salisbury, South Australia (2000)

    Google Scholar 

  17. Fletcher, R., Powell, M.: A rapidly convergent descent method for minimization. Computer Journal 6, 163–168 (1963)

    MATH  MathSciNet  Google Scholar 

  18. Fletcher, R., Reeves, C.: Function minimization by conjugate gradients. Computer Journal 7, 149–154 (1964)

    Article  MATH  MathSciNet  Google Scholar 

  19. Foster, I., Kesselman, C.: The Grid: Blueprint for a new computing infrastructure. Morgan Kaufmann Publishers, Los Altos (2003)

    Google Scholar 

  20. Foster, I., Kesselman, C., Tuecke, S.: The Anatomy of the Grid: Enabling Scalable Virtual Organizations. International Journal of High Performance Computing Applications 15(3), 200–222 (2001), http://dx.doi.org/10.1177/109434200101500302

    Article  Google Scholar 

  21. Garwin, L., Lincoln, T.: A Century of Nature: Twenty-One Discoveries that Changed Science and the World. University of Chicago Press, Chicago (2003)

    Google Scholar 

  22. Graham, R., Shipman, G., Barrett, B., Castain, R., Bosilca, G., Lumsdaine, A.: Open mpi: A high-performance, heterogeneous mpi. In: Proceedings of CLUSTER. IEEE, Los Alamitos (2006), http://dblp.uni-trier.de/db/conf/cluster/cluster2006.html#GrahamSBCBL06

    Google Scholar 

  23. Gropp, W.: Mpich2: A new start for mpi implementations. In: Kranzlmüller, D., Kacsuk, P., Dongarra, J., Volkert, J. (eds.) PVM/MPI 2002. LNCS, vol. 2474, p. 7. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  24. Gropp, W., Lederman, S., Lumsdaine, A., Lusk, E., Nitzberg, B., Saphir, W., Snir, M.: MPI: The Complete Reference, vol. 2. MIT Press, Cambridge (1998)

    Google Scholar 

  25. Hestenes, M., Stiefel, E.: Methods of conjugate gradients for solving linear systems. Journal of Research of the National Bureau of Standards 49(6), 409–436 (1952)

    MATH  MathSciNet  Google Scholar 

  26. Horvath, D., Brillet, L., Roy, S., Conilleau, S., Tantar, A.A., Boisson, J.C., Melab, N., Talbi, E.G.: Local vs. global search strategies in evolutionary grid-based conformational sampling & docking. In: Proceedings of IEEE Congres on Evolutionary Computation, CEC 2009 (2009)

    Google Scholar 

  27. Ingber, L.: Adaptive simulated annealing (asa), global optimization c-code. Tech. rep., Caltech Alumni Association (1993)

    Google Scholar 

  28. Ingber, L.: Simulated annealing: Practice versus theory. Journal of Mathematical Computation Modelling 18(11), 29–57 (1993)

    Article  MATH  MathSciNet  Google Scholar 

  29. Ingber, L.: Adaptive simulated annealing (asa): Lessons learned. Control and Cybernetics 25, 33–54 (1996)

    MATH  Google Scholar 

  30. Ingber, L.: Adaptive simulated annealing (asa) and path-integral (pathint) algorithms: Generic tools for complex systems. Tech. rep., Chicago, IL (2001)

    Google Scholar 

  31. Ingber, L., Rosen, B.: Genetic algorithms and very fast simulated reannealing: A comparison. Mathematical Computer Modeling 16(11), 87–100 (1992)

    Article  MATH  MathSciNet  Google Scholar 

  32. Jones, T., Forrest, S.: Fitness distance correlation as a measure of problem difficulty for genetic algorithms. In: Proceedings of International Conference on Genetic Algorithms, pp. 184–192. Morgan Kaufmann, San Francisco (1995)

    Google Scholar 

  33. Keijzer, M., Guervós, J., Romero, G., Schoenauer, M.: Evolving objects: A general purpose evolutionary computation library. In: Proceedings of European Conference on Artificial Evolution (EA 2002), pp. 231–244. Springer, London (2002)

    Google Scholar 

  34. Kirkpatrick, S., Gelatt, C., Vecchi, M.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983), citeseer.ist.psu.edu/kirkpatrick83optimization.html

    Article  MathSciNet  Google Scholar 

  35. Krauter, K., Buyya, R., Maheswaran, M.: A taxonomy and survey of grid resource management systems for distributed computing. Software Practice and Experience 32(2), 135–164 (2002), citeseer.ist.psu.edu/krauter01taxonomy.html

    Article  MATH  Google Scholar 

  36. Lander, E.S., Linton, L.M., Birren, B., et al.: Initial sequencing and analysis of the human genome. Nature 409(6822), 860–921 (2001)

    Article  Google Scholar 

  37. Little, P.: Dna sequencing: the silent revolution. In: A Century of Nature: Twenty-One Discoveries that Changed Science and the World, ch. 16. University of Chicago Press, Chicago (2003)

    Google Scholar 

  38. McCammon, J.A., Gelin, B.R., Karplus, M.: Dynamics of folded proteins. Nature 267(5612), 585–590 (1977)

    Article  Google Scholar 

  39. Metropolis, N., Rosenbluth, A., Rosenbluth, M., Teller, A., Teller, E.: Equation of state calculations by fast computing machines. The Journal of Chemical Physics 21(6), 1087–1092 (1953), http://link.aip.org/link/?JCP/21/1087/1

    Article  Google Scholar 

  40. Neumaier, A.: Molecular modeling of proteins and mathematical prediction of protein structure. SIAM Review 39(3), 407–460 (1997), citeseer.ist.psu.edu/neumaier97molecular.html

    Article  MATH  MathSciNet  Google Scholar 

  41. Ngo, J.T., Marks, J.: Computational complexity of a problem in molecular structure prediction. Protein Engineering 5(4), 313–321 (1992), http://peds.oxfordjournals.org/cgi/content/abstract/5/4/313

    Article  Google Scholar 

  42. Pande, V., Baker, I., Chapman, J., Elmer, S., Khaliq, S., Larson, S., Rhee, Y., Shirts, M., Snow, C., Sorin, E., Zagrovic, B.: Atomistic protein folding simulations on the submillisecond timescale using worldwide distributed computing. Biopolymers 68(1), 91–109 (2003)

    Article  Google Scholar 

  43. Pant, A., Jafri, H.: Communicating efficiently on cluster based grids with mpich-vmi. In: Proceedings of CLUSTER, pp. 23–33. IEEE Computer Society, Los Alamitos (2004), http://dblp.uni-trier.de/db/conf/cluster/cluster2004.html#PantJ04

    Google Scholar 

  44. Polak, E., Ribière, G.: Note sur la convergence des méthodes de directions conjuguées. Revue française d’informatique et de recherche opérationnelle 16, 35–43 (1969)

    Google Scholar 

  45. Ponder, J., Case, D.: Force fields for protein simulations. Advances in Protein Chemistry 66, 27–85 (2003)

    Article  Google Scholar 

  46. Rabow, A., Scheraga, H.: Improved genetic algorithm for the protein folding problem by use of a Cartesian combination operator. Protein Science 5(9), 1800–1815 (1996), http://www.proteinscience.org/cgi/content/abstract/5/9/1800

    Article  Google Scholar 

  47. Schulten, K., Phillips, J., Kale, L., Bhatele, A.: Biomolecular modeling in the era of petascale computing. In: Bader, D. (ed.) Petascale Computing: Algorithms and Applications, pp. 165–181. Chapman & Hall/CRC Press, Boca Raton (2008)

    Google Scholar 

  48. Shewchuk, J.: An introduction to the conjugate gradient method without the agonizing pain. Tech. rep., Carnegie Mellon University, Pittsburgh, PA, USA (1994), http://portal.acm.org/citation.cfm?id=865018

  49. Shirts, M., Pande, V.: COMPUTING: Screen Savers of the World Unite! Science 290(5498), 1903–1904 (2000)

    Article  Google Scholar 

  50. Stewart, C., Müller, M., Lingwall, M.: Progress towards petascale applications in biology: Status in 2006. In: Proceedings of Euro-Par Workshops, pp. 289–303 (2006)

    Google Scholar 

  51. Talbi, E.G.: A taxonomy of hybrid metaheuristics. Journal of Heuristics 8(5), 541–564 (2002)

    Article  Google Scholar 

  52. Tantar, A.A., Melab, N., Demarey, C., Talbi, E.G.: Building a virtual globus grid in a reconfigurable environment - a case study: Grid5000. Tech. Rep. inria-00168130, INRIA, France (2007), http://hal.inria.fr/inria-00168130/en

  53. Tantar, A.A., Melab, N., Talbi, E.G.: A grid-based genetic algorithm combined with an adaptive simulated annealing for protein structure prediction. Soft Computing 12(12), 1185–1198 (2008)

    Article  MATH  Google Scholar 

  54. Tantar, A.-A., Melab, N., Talbi, E.-G.: An analysis of dynamic mutation operators for conformational sampling. In: Biologically-inspired Optimisation Methods: Parallel Algorithms, Systems and Applications. Studies in Computational Intelligence. Springer, Heidelberg (2009)

    Google Scholar 

  55. Tantar, E., Tantar, A.A., Melab, N., Talbi, E.G.: Analysis of local search algorithms for conformational sampling. In: Advances in Parallel Computing, Parallel Programming and Applications on Grids, P2P and Networked-based Systems. IOS Press, Amsterdam (2009)

    Google Scholar 

  56. Venter, J., Venter, J., Adams, M., et al.: The sequence of the human genome. Science 291(5507), 1304–1351 (2001), http://www.sciencemag.org/cgi/content/abstract/291/5507/1304

    Article  Google Scholar 

  57. Van de Waterbeemd, H., Carter, R., Grassy, G., Kubinyi, H., Martin, Y., Tute, M., Willett, P.: Glossary of terms used in computational drug design. Pure and Applied Chemistry 69(5), 1137–1152 (1997)

    Article  Google Scholar 

  58. White, A., Zerilli, F., Jones, H.: Ab initio calculation of intermolecular potential parameters for gaseous decomposition products of energetic materials. Tech. Rep. DSTO-TR-1016, Department of Defense, Energetic Materials Research and Technology Department, Naval Surface Warfare Center, DSTO-TR-1016, Melbourne Victoria 3001, Australia (2000)

    Google Scholar 

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Authors and Affiliations

  1. INRIA Lille - Nord Europe, Project-Team DOLPHIN, Room 211 bis, Building A, 40, Avenue Halley, Parc Scientifique de la Haute-Borne, 59655, Villeneuve d’Ascq Cedex, France

    Alexandru-Adrian Tantar, Nouredine Melab & El-Ghazali Talbi

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  1. Alexandru-Adrian Tantar
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  2. Nouredine Melab
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  3. El-Ghazali Talbi
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  1. University of Extremadura, C/ Sta Teresa de Jornet, 38, Merida, Spain

    Francisco Fernández de Vega

  2. Yahoo! Inc., 701 First Avenue, 94087, Sunnyvale, CA, USA

    Erick Cantú-Paz

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Tantar, AA., Melab, N., Talbi, EG. (2010). A Grid-Based Hybrid Hierarchical Genetic Algorithm for Protein Structure Prediction. In: de Vega, F.F., Cantú-Paz, E. (eds) Parallel and Distributed Computational Intelligence. Studies in Computational Intelligence, vol 269. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10675-0_13

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  • DOI: https://doi.org/10.1007/978-3-642-10675-0_13

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