Skip to main content
SpringerLink
Log in

Genetic Algorithm inAb Initio Protein Structure Prediction Using Low Resolution Model: A Review

  • Chapter
Biomedical Data and Applications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 224))

Abstract

Proteins are sequences of amino acids bound into a linear chain that adopt a specific folded three-dimensional (3D) shape. This specific folded shape enables proteins to perform specific tasks. The protein structure prediction (PSP) by ab initio or de novo approach is promising amongst various available computational methods and can help to unravel the important relationship between sequence and its corresponding structure. This article presents the ab initio protein structure prediction as a conformational search problem in low resolution model using genetic algorithm. As a review, the essence of twin removal, intelligence in coding, the development and application of domain specific heuristics garnered from the properties of the resulting model and the protein core formation concept discussed are all highly relevant in attempting to secure the best solution.

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

    Google Scholar 

  2. Unger, R., Moult, J.: Genetic Algorithms for Protein Folding Simulations. Journal of Molecular Biology 231, 75–81 (1993)

    Article  Google Scholar 

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

    Google Scholar 

  4. Bonneau, R., Baker, D.: Ab Initio Protein Structure Prediction: Progress and Prospects. Annu. Rev. Biophys. Biomol. Struct. 30, 173–189 (2001)

    Article  Google Scholar 

  5. Chivian, D., Robertson, T., Bonneau, R., Baker, D.: Ab Initio Methods. In: Bourne, P.E., Weissig, H. (eds.) Structural Bioinformatics. Wiley-Liss, Inc, Chichester (2003)

    Google Scholar 

  6. Samudrala, R., Xia, Y., Levitt, M.: A Combined Approach for ab initio Construction of Low Resolution Protein Tertiary Structures from Sequence Pacific Symposium on Biocomputing (PSB), vol. 4, pp. 505–516 (1999)

    Google Scholar 

  7. Corne, D.W., Fogel, G.B.: An Introduction to Bioinformatics for Computer Scientists. In: Fogel, G.B., Corne, D.W. (eds.) Evolutionary Computation in Bioinformatics, pp. 3–18 (2004)

    Google Scholar 

  8. Berg, J.M., Tymoczko, J.L., Stryer, L., Clarke, N.D. (eds.): Biochemistry. W. H. Freeman and Company, New York (2002)

    Google Scholar 

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

    Google Scholar 

  10. Kuwajima, K., Arai, M. (eds.): Old and New Views of Protein Folding. Elesevier, Amsterdam (1999)

    Google Scholar 

  11. Pietzsch, J.: Protein folding technology (July 2007), http://www.nature.com/horizon/proteinfolding/background/technology.html

  12. Hoque, M.T., Chetty, M., Dooley, L.S.: Significance of Hybrid Evolutionary Computation for Ab Inito Protein Folding Prediction. In: Grosan, C., Abraham, A., Ishibuchi, H. (eds.) Hybrid Evolutionary Algorithms, Springer, Berlin (2006)

    Google Scholar 

  13. Lamont, G.B., Merkie, L.D.: Toward effective polypeptide chain prediction with parallel fast messy genetic algorithms. In: Fogel, G., Corne, D. (eds.) Evolutionary Computation in Bioinformatics, pp. 137–161 (2004)

    Google Scholar 

  14. Guex, N., Peitsch, M.C.: Principles of Protein Structure: Comparative Protein Modelling and Visualisation (April 2007), http://swissmodel.expasy.org/course/course-index.htm

  15. Jones, D.T., Miller, R.T., Thornton, J.M.: Successful protein fold recognition by optimal sequence threading validated by rigorous blind testing. Proteins: Structure, Function, and Genetics 23, 387–397 (1995)

    Article  Google Scholar 

  16. Sánchez, R., Šali, A.: Large-scale protein structure modeling of the Saccharomyces cerevisiae genome. In: PNAS 95, pp. 13597–13602 (1998)

    Google Scholar 

  17. Jones, D.T.: GenTHREADER: An efficient and reliable protein fold recognition method for genomic sequences. Journal of Molecular Biology 287, 797–815 (1999)

    Article  Google Scholar 

  18. Wikipedia: De novo protein structure prediction (July 2007), http://en.wikipedia.org/wiki/De_novo_protein_structure_prediction

  19. Xia, Y., Huang, E.S., Levitt, M., Samudrala, R.: Ab Initio Construction of Protein Tertiary Structures using a Hierarchical Approach. J. Mol. Biol. 300, 171–185 (2000)

    Article  Google Scholar 

  20. Anfinsen, C.B.: Studies on the Principles that Govern the Folding of Protein Chains (1972), http://nobelprize.org/nobel_prizes/chemistry/laureates/

  21. Levinthal, C.: Are there pathways for protein folding? Journal of Chemical Physics 64, 44–45 (1968)

    Google Scholar 

  22. Backofen, R., Will, S.: A Constraint-Based Approach to Fast and Exact Structure Prediction in Three-Dimensional Protein Models. Constraints Journal 11 (2006)

    Google Scholar 

  23. Schueler-Furman, O., Wang, C., Bradley, P., Misura, K., Baker, D.: Progress in Modeling of Protein Structures and Interactions. Science 310, 638–642 (2005)

    Article  Google Scholar 

  24. Hirst, J.D., Vieth, M., Skolnick, J., Brook, C.L.: Predicting leucine zipper structures from sequence. Protein Engineering 9, 657–662 (1996)

    Article  Google Scholar 

  25. Roterman, I.K., Lambert, M.H., Gibson, K.D., Scheraga, H.: A comparison of the CHARMM, AMBER and ECEPP potentials for peptides. II. Phi-psi maps for N-acetyl alanine N’-methyl amide: comparisons, contrasts and simple experimental tests. J. Biomol. Struct. Dynamics 7, 421–453 (1989)

    Google Scholar 

  26. Cornell, W.D., Cieplak, P., Bayly, C.I., Gould, I.R., Merz Jr., K.M., Ferguson, D.M., Spellmeyer, D.C., Fox, T., Caldwell, J.W., Kollman, P.A.: A second generation force field for the simulation of proteins and nucleic acids. J. Am. Chem. Soc. 117, 5179–5197 (1995)

    Article  Google Scholar 

  27. Nemethy, G., Gibson, K.D., Palmer, K.A., Yoon, C.N., Paterlini, G., Zagari, A., Rumsey, S., Scheraga, H.A.: Improved geometrical parameters and non-bonded interactions for use in the ECEPP/3 algorithm, with application to proline-containing peptides. Journal of physical chemistry 96, 6472–6484 (1992)

    Article  Google Scholar 

  28. Heureux, P.L., et al.: Knowledge-Based Prediction of Protein Tertiary Structure. Computational Methods for Protein Folding: Advances in Chemical Physics 120 (2002)

    Google Scholar 

  29. Ercolessi, F.: A molecular dynamics primer. In: ICTP, Spring College in Computational Physics (1997)

    Google Scholar 

  30. Schlick, T.: Molecular Modeling and Simulation. Springer, Heidelberg (2002)

    MATH  Google Scholar 

  31. Stote, R.: Theory of Molecular Dynamics Simulations (March 2006), http://www.ch.embnet.org/MDtutorial/

  32. Dill, K.A.: Theory for the Folding and Stability of Globular Proteins. Biochemistry 24, 1501–1509 (1985)

    Article  Google Scholar 

  33. Dill, K.A., Bromberg, S., Yue, K., Fiebig, K.M., Yee, D.P., Thomas, P.D., Chan, H.S.: Principles of protein folding – A perspective from simple exact models. Protein Science 4, 561–602 (1995)

    Google Scholar 

  34. Backofen, R., Will, S., Clote, P.: Algorithmic approach to quantifying the hydrophobic force contribution in protein folding. Pacific Symp. On Biocomputing 5, 92–103 (2000)

    Google Scholar 

  35. Schöppe, G., Heermann, D.W.: Alternative off-lattice model with continuous backbone mass for polymers. Physical Review E59, 636–641 (1999)

    Google Scholar 

  36. Chen, M., Huang, W.: Heuristic Algorithm for off-lattice protein folding problem. Journal of Zhejiang Univ Science B 7, 7–12 (2006)

    Article  Google Scholar 

  37. Skolnick, J., Kolinski, A.: A Unified Approach to the prediction of Protein Structure and Function. Computational Methods for Protein Folding: Advances in Chemical Physics 120 (2002)

    Google Scholar 

  38. Kolinski, A., Gront, D., Kmiecik, S., Kurcinski, M., Latek, D.: Modeling Protein Structure, Dynamics and Thermodynamics with Reduced Representation of Conformational Space. John von Neumann Institute for Computing (NIC) Series 34, 21–28 (2006)

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  41. Germain, R.S., Fitch, B., Rayshubskiy, A., Eleftheriou, M., Pitman, M.C., Suits, F., Giampapa, M., Ward, T.J.C.: Blue Matter on Blue Gene/L: Massively Parallel Computation for Bio-molecular Simulation. ACM, New York (2005)

    Google Scholar 

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

    Google Scholar 

  43. Chivian, D., Kim, D.E., Malmström, L., Schonburn, J., Rohl, C.A., Baker, D.: Prediction of CASP6 Structures Using Automated Robetta Protocols. PROTEINS: Structure, Function, and Genetics 7, 157–166 (2005)

    Google Scholar 

  44. Hung, L.-H., Samudrala, R.: PROTINFO: secondary and tertiary protein structure prediction. Nucleic Acids Research 31, 3296–3299 (2003)

    Article  Google Scholar 

  45. Hung, L.H., Ngan, S.C., Liu, T., Samudrala, R.: PROTINFO: new algorithms for enhanced protein structure predictions. Nucleic Acids Research 33, 77–80 (2005)

    Article  Google Scholar 

  46. Zhang, Y., Arakaki, A.K., Skolnick, J.: TASSER: An Automated Method for the Prediction of Protein Tertiary Structures in CASP6. PROTEINS: Structure, Function, and Bioinformatics 7, 91–98 (2005)

    Article  Google Scholar 

  47. Baker, D.: A surprising simplicity to protein folding. Nature 405, 39–42 (2000)

    Article  Google Scholar 

  48. Baker, D.: Prediction and design of macromolecular structures and interactions. Phil. Trans. R. Soc. B 361, 459–463 (2006)

    Article  Google Scholar 

  49. Zhang, Y.: Protein Structure Prediction by I-TASSER at CASP7 (2006)

    Google Scholar 

  50. Crescenzi, P., Goldman, D., Papadimitriou, C., Piccolboni, A., Yannakakis, M.: On the complexity of protein folding (extended abstract). In: The second annual international conference on Computational molecular biology, pp. 597–603. ACM, New York (1998)

    Google Scholar 

  51. Berger, B., Leighton, T.: Protein Folding in the Hydrophobic-Hydrophilic (HP) Model is NP-Complete. Journal of Computational Biology 5, 27–40 (1998)

    Article  Google Scholar 

  52. Chen, M., Lin, K.Y.: Universal amplitude ratios for three-dimensional self-avoiding walks. Journal of Physics A: Mathematical and General 35, 1501–1508 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  53. Schiemann, R., Bachmann, M., Janke, W.: Exact Enumeration of Three – Dimensional Lattice Proteins. In: Computer Physics Communications, p. 166. Elsevier Science, Amsterdam (2005)

    Google Scholar 

  54. MacDonald, D., Joseph, S., Hunter, D.L., Moseley, L.L., Jan, N., Guttmann, A.J.: Self-avoiding walks on the simple cubic lattice. Journal of Physics A: Mathematical and General 33, 5973–5983 (2000)

    Article  MATH  MathSciNet  Google Scholar 

  55. Guttmann, A.J.: Self-avoiding walks in constrained and random geometries. Elsevier, Amsterdam (2005)

    Google Scholar 

  56. Bastolla, U., Frauenkron, H., Gerstner, E., Grassberger, P., Nadler, W.: Testing a new Monte Carlo Algorithm for Protein Folding. National Center for Biotechnology Information 32, 52–66 (1998)

    Google Scholar 

  57. Liang, F., Wong, W.H.: Evolutionary Monte Carlo for protein folding simulations. J. Chem. Phys. 115 (2001)

    Google Scholar 

  58. Jiang, T., Cui, Q., Shi, G., Ma, S.: Protein folding simulation of the hydrophobic-hydrophilic model by computing tabu search with genetic algorithms. In: ISMB, Brisbane, Australia (2003)

    Google Scholar 

  59. Unger, R., Moult, J.: Genetic Algorithm for 3D Protein Folding Simulations. In: 5th International Conference on Genetic Algorithms, pp. 581–588 (1993)

    Google Scholar 

  60. König, R., Dandekar, T.: Refined Genetic Algorithm Simulation to Model Proteins. Journal of Molecular Modeling 5 (1999)

    Google Scholar 

  61. Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution (1992)

    Google Scholar 

  62. Holland, J.H.: Adaptation in Natural And Artificial Systems. MIT Press, Cambridge (2001)

    Google Scholar 

  63. Schulze-Kremer, S.: Genetic Algorithms and Protein Folding (1996)

    Google Scholar 

  64. Whitley, D.: An Overview of Evolutionary Algorithms. Journal of Information and Software Technology 43, 817–831 (2001)

    Article  Google Scholar 

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

    Google Scholar 

  66. Vose, M.D.: The Simple Genetic Algorithm. MIT Press, Cambridge (1999)

    MATH  Google Scholar 

  67. Fogel, D.B.: Evolutionary Computation Towards a new philosophy of Machine Intelligence. IEEE Press, Los Alamitos (2000)

    Google Scholar 

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

    Google Scholar 

  69. Yao, X.: Evolutionary Computation Theory and Application. World Scientific, Singapore (1999)

    Google Scholar 

  70. Wikipedia: Genetic Algorithm (July 2007), http://en.wikipedia.org/wiki/Genetic_algorithm

  71. Hoque, M.T., Chetty, M., Dooley, L.S.: Generalized Schemata Theorem Incorporating Twin Removal for Protein Structure Prediction. In: Rajapakse, J.C., Schmidt, B., Volkert, L.G. (eds.) PRIB 2007. LNCS (LNBI), vol. 4774, pp. 84–97. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  72. Haupt, R.L., Haupt, S.E.: Practical Genetic Algorithms (2004)

    Google Scholar 

  73. Ronald, S.: Duplicate Genotypes in a Genetic algorithm. In: IEEE World Congress on Computational Intelligence, pp. 793–798 (1998)

    Google Scholar 

  74. Hart, W.E., Istrail, S.: HP Benchmarks (August 2005), http://www.cs.sandia.gov/tech_reports/compbio/tortilla-hp-benchmarks.html

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

    Google Scholar 

  76. Hoque, M.T., Chetty, M., Dooley, L.S.: Non-Isomorphic Coding in Lattice Model and its Impact for Protein Folding Prediction Using Genetic Algorithm. In: IEEE Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). IEEE, Toronto (2006)

    Google Scholar 

  77. Patton, A.L., Punch, W.F., Goodman, E.D.: A Standard GA approach to Native Protein Conformation Prediction. In: 6th International Conference on Genetic Algorithms, pp. 574–581 (1995)

    Google Scholar 

  78. Krasnogor, N., Hart, W.E., Smith, J., Pelta, D.A.: Protein Structure Prediction With Evolutionary Algorithms. In: Genetic and Evolutionary Computation Conference, GECCO 1999 (1999)

    Google Scholar 

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

    Google Scholar 

  80. Hoque, M.T., Chetty, M., Dooley, L.: A Guided Genetic Algorithm for Protein Folding Prediction Using 3D Hydrophobic-Hydrophilic Model. In: Special session in WCCI / IEEE Congress on Evolutionary Computation, CEC (2006)

    Google Scholar 

  81. Hoque, M.T., Chetty, M., Dooley, L.S.: A Hybrid Genetic Algorithm for 2D FCC Hydrophobic-Hydrophilic Lattice Model to Predict Protein Folding. In: Sattar, A., Kang, B.-h. (eds.) AI 2006. LNCS(LNAI), vol. 4304, pp. 867–876. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  82. Hoque, M.T., Chetty, M., Sattar, A.: Protein Folding Prediction in 3D FCC HP Lattice Model Using Genetic Algorithm Bioinformatics special session. In: IEEE Congress on Evolutionary Computation (CEC), Singapore (2007)

    Google Scholar 

  83. Yue, K., Dill, K.A.: Sequence-structure relationships in proteins and copolymers. Phys. Rev. E 48, 2267–2278 (1993)

    Google Scholar 

  84. Bonneau, R., Strauss, C., Baker, D.: Improving the Performance of Rosetta Using Multiple Sequence Alignment Information and Global Measures of Hydrophobic Core. PROTEINS: Structure, Function, and Genetics 43, 1–11 (2001)

    Article  Google Scholar 

  85. Toma, L., Toma, S.: Contact interactions methods: A new Algorithm for Protein Folding Simulations. Protein Science 5, 147–153 (1996)

    Article  Google Scholar 

  86. Backofen, R., Will, S.: A Constraint-Based Approach to Fast and Exact Structure Prediction in Three-Dimensional Protein Models. Kluwer Academic Publishers, Dordrecht (2005)

    Google Scholar 

  87. Raghunathan, G., Jernigan, R.L.: Ideal architecture of residue packing and its observation in protein structures. Protein Sci. 10, 2072–2083 (1997)

    Article  Google Scholar 

  88. Wikipedia: Cuboctahedron (February 2007), http://en.wikipedia.org/wiki/Cuboctahedron

  89. Backofen, R., Will, S., Bornberg-Bauer, E.: Application of constraint programming techniques for structure prediction of lattice proteins with extended alphabets. Bioinformatics 15, 234–242 (1999)

    Article  Google Scholar 

  90. Guo, Y.Z., Feng, E.M., Wang, Y.: Exploration of two-dimensional hydrophobic-polar lattice model by combining local search with elastic net algorithm. J. Chem. Phys. 125 (2006)

    Google Scholar 

  91. Crippen, G.M.: Prediction of Protein Folding from Amino Acid Sequence over Discrete Conformation Spaces. Biochemistry 30, 4232–4237 (1991)

    Article  Google Scholar 

  92. Hoque, M.T., Chetty, M., Sattar, A.: Extended HP model for Protein Structure Prediction. Journal of Computational Biology 16, 1–19 (2007)

    MathSciNet  Google Scholar 

  93. Jordan, J.K., Kondrashov, F.A., Adzhubei, I.A., Wolf, Y.I., Koonin, E.V., Kondrashov, A.S., Sunyaev, S.: A universal trend of amino acid gain and loss in protein evolution. Letter to Nature 433 (2005)

    Google Scholar 

  94. PDB, Protein Data Base (April 2007), http://www.rcsb.org/pdb/

Download references

Author information

Authors and Affiliations

  1. IIIS, Griffith University, Nathan, QLD-4111, Australia

    Md. Tamjidul Hoque & Abdul Sattar

  2. GSIT, Monash University, Churchill, VIC, 3842, Australia

    Madhu Chetty

Authors
  1. Md. Tamjidul Hoque
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Madhu Chetty
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Abdul Sattar
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. WA Centre for Comparative Genomics, Murdoch University, 6150, Murdoch, Western Australia, Australia

    Amandeep S. Sidhu

  2. Digital Ecosystems and Business Intelligence Institute, Curtin University of Technology, 6102, Park Bentley, WA, Australia

    Tharam S. Dillon

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Hoque, M.T., Chetty, M., Sattar, A. (2009). Genetic Algorithm inAb Initio Protein Structure Prediction Using Low Resolution Model: A Review. In: Sidhu, A.S., Dillon, T.S. (eds) Biomedical Data and Applications. Studies in Computational Intelligence, vol 224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02193-0_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-02193-0_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02192-3

  • Online ISBN: 978-3-642-02193-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation