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Natural Computing

, Volume 6, Issue 1, pp 55–72 | Cite as

Determination of protein structure and dynamics combining immune algorithms and pattern search methods

  • A. M. Anile
  • V. Cutello
  • G. Narzisi
  • G. Nicosia
  • S. Spinella
Original Paper

Abstract

Natural proteins quickly fold into a complicated three-dimensional structure. Evolutionary algorithms have been used to predict the native structure with the lowest energy conformation of the primary sequence of a given protein. Successful structure prediction requires a free energy function sufficiently close to the true potential for the native state, as well as a method for exploring the conformational space. Protein structure prediction is a challenging problem because current potential functions have limited accuracy and the conformational space is vast. In this work, we show an innovative approach to the protein folding (PF) problem based on an hybrid Immune Algorithm (IMMALG) and a quasi-Newton method starting from a population of promising protein conformations created by the global optimizer DIRECT. The new method has been tested on Met-Enkephelin peptide, which is a paradigmatic example of multiple–minima problem, 1POLY, 1ROP and the three helix protein 1BDC. DIRECT produces an initial population of promising candidate solutions within a potentially optimal rectangle for the funnel landscape of the PF problem. Hence, IMMALG starts from a population of promising protein conformations created by the global optimizer DIRECT. The experimental results show that such a multistage approach is a competitive and effective search method in the conformational search space of real proteins, in terms of solution quality and computational cost comparing the results of the current state-of-art algorithms.

Keywords

Clonal Selection Algorithms DIRECT Immune Algorithms pattern search methods protein folding protein structure prediction structural bioinformatics 

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References

  1. Baldi P, Pollastri G (2003) The principled design of large-scale recursive neural network architectures-DAG-RNNs and the protein structure prediction problem. Journal of Machine Learning Research 4:575–603CrossRefGoogle Scholar
  2. Bartholomew-Biggs MC, Parkhurst SC, Wilson SP (2002) Using DIRECT to solve an aircraft routing problem. Computational Optimization and Applications and International Journal 21(3):311–323MATHCrossRefMathSciNetGoogle Scholar
  3. Bindewald E, Hesser J, Männer R (1998) Implementing genetic algorithms with sterical constrains for protein structure prediction. In: Proceedings of International Conference on Parallel Problem Solving from Nature (PPSN V), pp. 959–967, Amsterdam, The NetherlandsGoogle Scholar
  4. Bowie JU, Eisemberg D (1994) An evolutionary approach to folding small alpha-helical proteins that uses sequence information and an empirical guiding fitness function. Proceedings of the National Academy of Sciences of the United States of America 91: 4436–4440CrossRefGoogle Scholar
  5. Brooks BR, Bruccoleri RE, Olafson BD, States DJ, Swaminathan S, Karplus M (1983) CHARMM: A program for macromolecular energy, minimization, and dynamics calculations. Journal of Computational Chemistry 4:187–217CrossRefGoogle Scholar
  6. Burnet FM (1959) The Clonal Selection Theory of Acquired Immunity. Cambridge, UK: Cambridge University PressGoogle Scholar
  7. Byrd RH, Nocedal J, Schnabel RB (1994) Representation of quasi Newton matrices and their use in limited memory methods. Mathematical Programming 63(4):129–156CrossRefMathSciNetGoogle Scholar
  8. Carter RG, Gablonsky JM, Patrick A, Kelley CT (2001) Algorithms for noisy problems in gas transmission pipeline optimization. Optimization and Engineering 2:139–157MATHCrossRefMathSciNetGoogle Scholar
  9. Cutello V, Nicosia G, Pavone M (2004) Exploring the capability of Immune Algorithms: A characterization of hypermutation operators. In Proceedings of the Third International Conference on Artificial Immune Systems (ICARIS’04), vol. 3239, pp.␣263–276, Catania, ItalyGoogle Scholar
  10. Dennis JE, Schnabel RB (1983) Numerical Methods for Unconstrained Optimization and Nonlinear Equations. Prentice-Hall, Englewood Cliffs, NJMATHGoogle Scholar
  11. Dunbrack RL Jr, Cohen FE (1997) Bayesian statistical analysis of protein sidechain rotamer preferences. Protein Science 6:1661–1681CrossRefGoogle Scholar
  12. Eisenberg D, Marcotte E, Xenarios I, Yeates TO (2000) Protein function in the post-genomic era. Nature 405(6788):823–826CrossRefGoogle Scholar
  13. Finkel DE (2003) DIRECT Optimization Algorithm User Guide. Technical Report, CRSC N.C. State University (ftp://ftp.ncsu.edu/pub/ncsu/crsc/pdf/crsc-tr03-11.pdf)Google Scholar
  14. Foloppe N, MacKerell AD Jr (2000) All-atom empirical force field for nucleic acids: I. Parameter optimization based on small molecule and condensed phase macromolecular target data. Journal of Computational Chemistry 21:86–104CrossRefGoogle Scholar
  15. Gablonsky JM, Kelley CT (2001) A locally-biased form of the DIRECT Algorithm. Journal of Global Optimization 21:27–37MATHCrossRefMathSciNetGoogle Scholar
  16. Hansmann UH, Okamoto Y (1997) Numerical comparisons of three recently proposed algorithms in the protein folding problem. Journal of Computational Chemistry 18: 920–933CrossRefGoogle Scholar
  17. Hong Zhu, Bogy DavidB. (2002). DIRECT Algorithm and its application to slider Air-Bearing surface optimization. IEEE Transactions on Magnetics 38(5): 2168–2170CrossRefGoogle Scholar
  18. Huang CC, Couch GS, Pettersen EF, Ferrin TE (1996) Chimera: An extensible molecular modeling application constructed using standard components. Pacific Symposium on Biocomputing 1, 724Google Scholar
  19. Huang SE, Samudrala R, Ponder JW (1999) Ab initio folding prediction of small helical proteins using distance geometry and knowledge-based scoring functions. Journal of Molecular Biology 290: 267–281CrossRefGoogle Scholar
  20. Jones DR, Perttunen CD, Stuckman BE (1993). Lipschitzian optimization without the lipschitz constant. Journal of Optimization Theory and Application 79: 157–181MATHCrossRefMathSciNetGoogle Scholar
  21. Kaiser CE Jr, Lamont GB, Merkle LD, Gates GH Jr, Patcher R (1997) Polypeptide structure prediction: Real-valued versus binary hybrid genetic algorithms. In: Proceedings of the ACM Symposium on Applied Computing (SAC), pp. 279–286, San Jose, CAGoogle Scholar
  22. Levitt M (1983) Protein folding by restrained energy minimization and molecular dynamics. Journal of Molecular Biology 170:723–764CrossRefGoogle Scholar
  23. Li Z, Scheraga HA (1998) Structure and free energy of complex thermodynamics systems. Journal of Molecular Structure 179:333–352Google Scholar
  24. MacKerell AD Jr, Brooks B, Brooks C L III, Nilsson L, Roux B, Won Y, Karplus M (1998) CHARMM: The energy function and its parameterization with an overview of the program. In: Schleyer PvR et al. (eds) The Encyclopedia of Computational Chemistry, vol. 1, pp. 271–277. John Wiley & Sons, ChichesterGoogle Scholar
  25. McLachlan AD (1982) Rapid comparison of protein structures. Acta cytologica A38: 871–873Google Scholar
  26. Nicosia G (2004) Immune Algorithms for Optimization and Protein Structure Prediction. Department of Mathematics and Computer Science. University of Catania, ItalyGoogle Scholar
  27. Nicosia G, Cutello V, Bentley P, Timmis J (2004) Proceedings of the Third International Conference on Artificial Immune Systems. Springer-Verlag, Berlin, GermanyGoogle Scholar
  28. Purisima EO, Scheraga HA (1987) An approach to the multiple-minima problem in protein folding by relaxing dimensionality. Test on enkephalin. Journal of Molecular Biology 196: 697–709CrossRefGoogle Scholar
  29. Simons KT, Kooperberg C, Huang E, Baker D (1997) Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and bayesian scoring function. Journal of Molecular Biology 306:1191–1199CrossRefGoogle Scholar
  30. Tramontano A (2006) Protein Structure Prediction: Concepts and Applications. Wiley-VCH, WeinheimGoogle Scholar

Copyright information

© Springer Science+Business Media, Inc. 2007

Authors and Affiliations

  • A. M. Anile
    • 1
  • V. Cutello
    • 1
  • G. Narzisi
    • 1
  • G. Nicosia
    • 1
  • S. Spinella
    • 2
  1. 1.Department of Mathematics and Computer ScienceUniversity of CataniaCataniaItaly
  2. 2.Department of LinguisticsUniversity of CalabriaArcavata di Rende (CS)Italy

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