Skip to main content
SpringerLink
Log in

Significance of Hybrid Evolutionary Computation for Ab Initio Protein Folding Prediction

  • Chapter
Book cover Hybrid Evolutionary Algorithms

Part of the book series: Studies in Computational Intelligence ((SCI,volume 75))

Protein folding prediction (PFP), especially the ab initio approach, is one of the most challenging problems facing the bioinformatics research community due to it being extremely complex to solve and computationally very intensive. Hybrid evolutionary computing techniques have assumed considerable importance in attempting to overcome these challenges and so this chapter explores some of these PFP issues. By using the well-known Hydrophobic–Hydrophilic (HP) model, the performance of a number of contemporary nondeterministic search techniques are examined. Particular emphasis is given to the new Hybrid Genetic Algorithm (HGA) approach, which is shown to provide a number of performance benefits for PFP applications.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Allen F, et al. (2001) Blue Gene: A vision for protein science using a petaflop supercom-puter, IBM System Journal, 40(2).

    Google Scholar 

  2. Almasi G, et al. (2005) Early Experience with Scientific Applications on the Blue Gene/L Supercomputer, LNCS, Parallel Processing: 11th International Euro-Par Conference, Lisbon, Portugal, 560-570.

    Google Scholar 

  3. Anekonda T S (2002) Artificial Neural Networks and Hidden Markov Models for Pre-dicting the Protein Structures: The Secondary Structure Prediction in Caspases, Compu-tational Molecular Biology.

    Google Scholar 

  4. Anfinsen C (2006) Biography, http://nobelprize.org/chemistry/laureates /1972/anfinsen-bio.html, March.

  5. Backofen R, Will S (2005) A Constraint-Based Approach to Fast and Exact Structure Pre-diction in Three-Dimensional Protein Models, Kluwer Academic Publishers, Dordecht.

    Google Scholar 

  6. Bastolla U, Frauenkron H, Gerstner E, Grassberger P, Nadler W (1998) Testing a new Monte Carlo algorithm for protein folding, National Center for Biotechnology Informa-tion, 32(1): 52-66.

    Google Scholar 

  7. Berger B, Leighton T (1998) Protein folding in the hydrophobic-hydrophilic (HP) model is NP-complete, Journal of Computational Biology, 5(1): 27-40.

    Article  Google Scholar 

  8. Berg M M, Tymoczko J L, Stryer L (2002) Biochemistry, 5th edition, Freeman W H and Company, San Francisco, CA.

    Google Scholar 

  9. Bornberg-Bauer (1997) Chain Growth Algorithms for HP-Type Lattice Proteins, RECOMB, Santa Fe, NM, USA.

    Google Scholar 

  10. Brown D, et al. (2005) Bioinformatics Group, School of Computer Science, University of Waterloo Canada, http://monod.uwaterloo.ca/, April.

  11. Carr R, Hart W E, Newman A (2004) Bounding A Protein’s Free Energy In Lattice Models Via Linear Programming, RECOMB.

    Google Scholar 

  12. Chen M, Lin K Y (2002) Universal amplitude ratios for three-dimensional self-avoiding walks, Journal of Physics, 35: 1501-1508

    MATH  Google Scholar 

  13. Crescenzi P, et al. (1998) On the Complexity of Protein Folding (extended abstract), ACM, Proceedings of the Second Aannual International Conference on Computational Molecular Biology, 597-603.

    Google Scholar 

  14. Davis L (1991) Handbook of Genetic Algorithm, VNR, New York.

    Google Scholar 

  15. 15. Levinthal C (1969) How to fold graciously. In Mössbauer Spectroscopy in Biological Systems, Proceedings of a Meeting Held at Allerton House, Monticello, Illinois, Editors DeBrunner J T P, Munck E, University of Illinois Press, pp. 22-24.

    Google Scholar 

  16. Dill K A (1985) Theory for the folding and stability of globular proteins, Biochemistry, 24(6): 1501-1509.

    Article  Google Scholar 

  17. Docking (2005) www.cmpharm.ucsf.edu/ and www.scripps.edu/mb/olson/doc/autodock/, February.

  18. Duan Y, Kollman P A (2001) Computational protein folding: From lattice to all-atom, IBM Systems Journal, 40(2), 2001.

    Google Scholar 

  19. Ercolessi F (1997) A molecular Dynamics Primer, Spring College in Computational Physics, ICTP, Trieste.

    Google Scholar 

  20. Executive Summary (2005) Feasibility of an Artificial Neural Network Approach to Solving the Protein Folding Problem, http://www.ecf.utoronto.ca/ writing/esc300/pdf/draft5.pdf, January.

  21. Flebig K M, Dill K A (1993) Protein core assembly processes, The Journal of Chemical Physics, 98(4): 3475-3487.

    Google Scholar 

  22. Fogel D B (2000) Evolutionary Computation Towards a New Philosophy of Machine Intelligence, 2nd edition, IEEE Press.

    Google Scholar 

  23. Germain R S, et al. (2005) Blue Matter on Blue Gene/L: Massively Parallel Computation for Bio-molecular Simulation, ACM.

    Google Scholar 

  24. Goldberg D E (1989) Genetic Algorithm Search, Optimization, and Machine Learning, Addison-Wesley Publishing Company, Reading, MA.

    Google Scholar 

  25. Greenwood G W, Shin J (2003) On the Evolutionary Search for Solutions to the Pro-tein Folding problem, chapter 6 in Evolutionary Computation in Bioinformatics, Editors Fogel G B, Corne D W, Elsevier Science (USA), ISBN: 1-55860-797-8.

    Google Scholar 

  26. Guex N, Peitsch M C (2006): http://swissmodel.expasy.org/course/course-index.htm, March.

  27. Guttmann A J (2005) Self-avoiding walks in constrained and random geometries: Series studies. In Statistics of Linear Polymers in Disordered Media, Editor Chakrabarti B K, Elsevier, 59-101.

    Google Scholar 

  28. Hart E W, Istrail S (1995) Fast Protein Folding in the Hydrophobic-hydrophilic Model Within Three-Eights of Optimal, ACM.

    Google Scholar 

  29. Haupt R L, Haupt S E (2004) Practical Genetic Algorithms, 2nd edition, ISBN 0-471-45565-2.

    Google Scholar 

  30. Head-Gordon T, Wooley J C (2001) Computational challenges in structural and functional genomics, IBM Systems Journal, 40(2).

    Google Scholar 

  31. Head-Gordon T, Brown S (2003) Minimalist models for protein folding and design, Current Opinion in Structural Biology, 12: 160-167.

    Article  Google Scholar 

  32. Holland J H (1992) Adaptation in Natural And Artificial Systems, The MIT Press, Cambridge, Massachusetts London, England.

    Google Scholar 

  33. Hoque M T, Chetty M, Dooley L S (2004) Partially Computed Fitness Function Based Genetic Algorithm for Hydrophobic-Hydrophilic Model. HIS: 291-296, ISBN 0-7695-2291-2.

    Google Scholar 

  34. Hoque M T, Chetty M, Dooley L S (2005) A New Guided Genetic Algorithm for 2D Hydrophobic-Hydrophilic Model to Predict Protein Folding, IEEE Congress on Evolutionary Computation (CEC), 259-266, Edinburgh.

    Google Scholar 

  35. Hoque M T, Chetty M, Dooley L S (2006) A Guided Genetic Algorithm for Protein Folding Prediction Using 3D Hydrophobic-Hydrophilic Model, IEEE WCCI, 8103-8110.

    Google Scholar 

  36. Howard-Spink S (2006) The Power of Proteins, www.research.ibm.com/thinkresearch/pages/2001/20011105_protein.shtml, February.

  37. Jiang T, et al. (2003) Protein folding simulation of the hydrophobic-hydrophilic model by computing tabu search with genetic algorithms, Journal of Chemical Physics, 119(8).

    Google Scholar 

  38. Jones D T, Miller R T, Thornton J M (1995) Successful protein fold recognition by optimal sequence threading validated by rigorous blind testing. Proteins, 23: 387-397.

    Article  Google Scholar 

  39. König R, Dandekar T (1999) Refined genetic algorithm simulation to model proteins, Journal of Molecular Modeling, 5(12): 317-324.

    Article  Google Scholar 

  40. Kuwajima K, Arai M (1999) Old and New Views of Protein Folding, Elesevier.

    Google Scholar 

  41. Lesh N, Mitzenmacher M, Whitesides S (2003) A Complete and Effective Move Set for Simplified Protein Folding, RECOMB, Berlin.

    Google Scholar 

  42. Liang F, Wong W H (2001) Evolutionary Monte Carlo for protein folding simulations, The Journal of Chemical Physics, 115(7): 3374-3380.

    Article  Google Scholar 

  43. Markowetz F, Edler L, Vingron M (2003) Support vector machines for protein fold class prediction, Biometrical Journal, 45(3): 377-389.

    Article  MathSciNet  Google Scholar 

  44. Meller J, Elber R (2001) Linear programming optimization and a double statistical filter for protein threading protocols, PROTEINS: Structure, Function, and Genetics, 45: 241-261.

    Article  Google Scholar 

  45. Merkle L D, Gaulke R L, Lamont G B (1996) Hybrid Genetic Algorithm for Polypeptide Energy Minimization, ACM.

    Google Scholar 

  46. Michalewicz Z (1992) Genetic Algorithms + Data Structures = Evolution Programs, Springer-Verlag, New York.

    MATH  Google Scholar 

  47. Newman A (2002) A New Algorithm for Protein Folding in the HP Model, Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete Algorithms.

    Google Scholar 

  48. Pande V S, et al. (2003) Atomistic protein folding simulation on the submillisecond time scale using worldwide distributed computing, Biopolymers, 68: 91-109.

    Article  Google Scholar 

  49. Panik M J (1996) Linear Programming: Mathematics, Theory and Algorithm, ISBN 0-7923-3782-4.

    Google Scholar 

  50. Petit-Zeman S (2006) Treating protein folding diseases, www.nature.com/horizon/proteinfolding/background/treating.html, March.

  51. Pietzsch J (2006) The importance of protein folding, www.nature.com/horizon/proteinfolding/background/importance.html, March.

  52. Pietzsch J (2006) Protein folding technology, www.nature.com/horizon/proteinfolding/background/technology.html, March.

  53. Pietzsch J (2006) Protein folding diseases, www.nature.com/horizon/proteinfolding/background/disease.html, March.

  54. Rune B L, Christian N S, Pedersen (2005) Protein Folding in the 2D HP model, http://www.brics.dk/RS/99/16/BRICS-RS-99-16.pdf, BRICS, January.

  55. Raval A, Ghahramani Z, Wild D L (2002) A Bayesian network model for protein fold and remote homologue recognition, Bioinformatics, 18(6):788-801.

    Article  Google Scholar 

  56. Setubal J, Meidanis J (1997) Introduction to Computational Molecular Biology, ISBN 0-534-95262-3, An International Thomson Publishing Company.

    Google Scholar 

  57. Schiemann R, Bachmann M, Janke W (2005) Exact enumeration of three-dimensional lattice proteins, Computer Physics Communications 166: 8-16.

    Article  MathSciNet  Google Scholar 

  58. Schlick T (2002) Molecular Modeling and Simulation, Springer.

    Google Scholar 

  59. Schulze-Kremer S(2006) Genetic Algorithms and Protein Folding, http://www.techfak.uni-bielefeld.de/bcd/Curric/ProtEn/proten.html, March

  60. Shmygelska A, Hoos H H (2005) An ant colony optimization algorithm for the 2D and 3D hydrophobic polar protein folding problem, BMC Bioinformatics, 6(30).

    Google Scholar 

  61. Siew N, Fischer D (2001) Convergent evolution of protein structure prediction and com-puter chess tournaments: CASP, Kasparov, and CAFASP, IBM Systems Journal, 40 (2).

    Google Scholar 

  62. Skolnick J, Kolinski A (2001) Computational Studies of Protein Folding, Bioengineering and Biophysics, IEEE.

    Google Scholar 

  63. Stote R, et al(2006) Theory of Molecular Dynamics Simulations http://www.ch.embnet.org/MD_tutorial/, March.

  64. Thirumalai D, Klimov D K, Dima R I (2001) Insights into specific problems in protein folding using simple concepts, Editor Friesner A, Computational Methods for Protein Folding: Advances in Chemical Physics, vol. 120. ISBNs:0-471-22442-1.

    Google Scholar 

  65. Takahashi O, Kita H, Kobayashi S (1999) Protein Folding by A Hierarchical Genetic Algorithm, 4th Int. Symp. AROB.

    Google Scholar 

  66. Toma L, Toma S (1996) Contact interactions methods: A new algorithm for protein folding simulations, Protein Science, 5(1): 147-153.

    MathSciNet  Google Scholar 

  67. Toma L, Toma S (1999) Folding simulation of protein models on the structure-based cubo-octahedral lattice with the contact interactions algorithm, Protein Science, 8(1): 196-202.

    Google Scholar 

  68. Unger R, Moult J (1993) On the Applicability of Genetic Algorithms to Protein Folding. Proceeding of the Twenty-Sixth Hawaii International Conference on System Sciences, 1: 715-725.

    Article  Google Scholar 

  69. Unger R, Moult J (1993) Genetic algorithms for protein folding simulations, Journal of Molecular Biology, 231:75-81.

    Article  Google Scholar 

  70. Unger R, Moult J (1993) Genetic Algorithm for 3D Protein Folding Simulations, 5th International Conference on Genetic Algorithms, 581-588.

    Google Scholar 

  71. Vose M D (1999) The Simple Genetic Algorithm, The MIT Press, Cambridge, Massachusetts London, England.

    MATH  Google Scholar 

  72. Whitley D (2001) An overview of evolutionary algorithms, Journal of Information and Software Technology, 43: 817-831.

    Article  Google Scholar 

  73. Wikipedia (2006) Genetic Algorithm, http://en.wikipedia.org/wiki/Genetic_algorithm, March.

  74. Wikipedia (2006) Nuclear magnetic resonance, http://en.wikipedia.org/wiki/Nuclear_magnetic_resonance, March.

  75. Xia Y, Huang E S, Levitt M, Samudrala R (2000) Ab Initio Construction of Protein Tertiary Structures using a Hierarchical Approach, JMB.

    Google Scholar 

  76. Yue K, Dill K A (1995) Forces of tertiary structural organization in globular proteins, Proceedings of the National Acadamy of Sciences of the USA, 92: 146-150.

    Article  Google Scholar 

  77. Yue K, Dill K A (1993) Sequence-structure relationships in proteins and copolymers, Physical Review E, 48(3): 2267-2278.

    Article  Google Scholar 

  78. Zhang X (1994) A hybrid algorithm for determining protein structure, IEEE Expert, 9(4): 66-74.

    Article  Google Scholar 

  79. Grassbegrer P (1997) Pruned-enriched Rosenbluth method: Simulation of θ polymers of chain length up to 1,000,000. Physical Review E, in press.

    Google Scholar 

  80. Rosenbluth M N, Rpsenbluth A W (1955) Monte Carlo calculation of the average exten-sion of molecular chains. The Journal of Chemical Physics 23: 256.

    Google Scholar 

Download references

Authors
  1. Md. T. Hoque
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. Chetty
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. L. S. Dooley
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Norwegian University of Science and Technology, O.S. Bragstads plass 2E, N-7491, Trondheim, Norway

    Ajith Abraham

  2. Babes-Bolyai University, Kogalniceanu 1, 400084, Cluj-Napoca, Romania

    Crina Grosan

  3. Osaka Prefecture University, 1-1 Gakuen-cho, 599-8531, Naka-ku, Sakai Osaka, Japan

    Hisao Ishibuchi

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Hoque, M.T., Chetty, M., Dooley, L.S. (2007). Significance of Hybrid Evolutionary Computation for Ab Initio Protein Folding Prediction. In: Abraham, A., Grosan, C., Ishibuchi, H. (eds) Hybrid Evolutionary Algorithms. Studies in Computational Intelligence, vol 75. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73297-6_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-73297-6_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73296-9

  • Online ISBN: 978-3-540-73297-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation