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The Genetic Algorithm and Protein Tertiary Structure Prediction

  • Scott M. Le Grand
  • Kenneth M. MerzJr.

Abstract

In 1959, Anfinsen demonstrated that the primary structure (the sequence of amino acids) of a protein can uniquely determine its tertiary structure (three dimensional conformation). This implied that there must be a consistent set of rules for deriving a protein’s tertiary structure from its primary structure. The search for these rules is known as the protein folding problem. Despite many creative attempts, these rules have not been determined (Fasman, 1989). Currently, the primary structures of approximately 40,000 proteins are known. However, only a small percentage of those proteins have known, tertiary structures. A solution to the protein folding problem will make 40,000 more tertiary structures available for immediate study by translating the DNA sequence information in the sequence databases into three-dimensional protein structures. This translation will be indispensable for the analysis of results from the Human Genome Project, de novo protein design, and many other areas of biotechnological research. Finally, an in-depth study of the rules of protein folding should provide vital clues to the protein folding process. The search for these rules is therefore an important objective for theoretical molecular biology.

Keywords

Genetic Algorithm Dihedral Angle Tertiary Structure Potential Energy Function Conformational Search 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Anet F (1990): Inflection points and chaotic behavior in searching the conformational space of cyclononane. J Am Chem Soc 112:7172–7178CrossRefGoogle Scholar
  2. Anfinsen C (1959): The Molecular Basis of Evolution. New York: John Wiley & SonsGoogle Scholar
  3. Booker L (1987): Improving search in genetic algorithms. In Genetic Algorithms and Simulated Annealing. San Mateo, CA: Morgan KaufmannGoogle Scholar
  4. Bowie J, Eisenberg D (1991): An evolutionary approach to folding proteins from sequence information: application to small α-helical proteins. (Unpublished results.)Google Scholar
  5. Bowie J, Lüthy R, Eisenberg D (1991): A method to identify protein sequences that fold into a known three-dimensional structure. Science 253:164–170PubMedCrossRefGoogle Scholar
  6. Brooks B, Bruccoleri R, Olafson B, States D, Swaminathan S, Karplus M (1983): CHARMM: A program for macromolecular energy, minimization, and dynamics calculations. J Comp Chem 4:187–217CrossRefGoogle Scholar
  7. Cleveland G, Smith S (1989): Using genetic algorithms to schedule flow shop releases. In: Proceedings of the Third International Conference on Genetic Algorithms. San Mateo, CA: Morgan KaufmannGoogle Scholar
  8. Covell DG, Jernigan RL (1990): Conformations of folded proteins in restricted spaces. Biochemistry 29:3287–3294PubMedCrossRefGoogle Scholar
  9. Crippen G (1991): Prediction of protein folding from amino acid sequence over discrete conformational spaces. Biochemistry 30:4232–4237PubMedCrossRefGoogle Scholar
  10. Dandekar T, Argos P (1992): Potential of genetic algorithms in protein folding and protein engineering simulations. Protein Engineering 5:637–645PubMedCrossRefGoogle Scholar
  11. Davidor Y (1989): Analogous crossover. In: Proceedings of the Third International Conference on Genetic Algorithms. San Mateo, CA: Morgan KaufmannGoogle Scholar
  12. Fasman G (1989): Development of the prediction of protein structure. In: Prediction of Protein Structure and the Principles of Protein Conformation. New York: Plenum PressCrossRefGoogle Scholar
  13. Fogarty T (1989): Varying the probability of mutation in the genetic algorithm. In: Proceedings of the Third International Conference on Genetic Algorithms. San Mateo, CA: Morgan KaufmannGoogle Scholar
  14. Friedrichs MS, Wolynes PG (1989): Genetic algorithms for model bimolecular optimization problems. (Unpublished.)Google Scholar
  15. Goldberg D (1989): Genetic Algorithms in Search, Optimization, and Machine Learning. San Mateo, CA: Addison-WesleyGoogle Scholar
  16. Grefenstette J, Baker J (1989): How genetic algorithms work: A critical look at implicit parallelism. In: Proceedings of the Third International Conference on Genetic Algorithms. San Mateo, CA: Morgan KaufmannGoogle Scholar
  17. Holland J (1975): Adaptation in Natural and Artificial Systems. Ann Arbor, MI: University of Michigan PressGoogle Scholar
  18. Judson RS (1992): Teaching polymers to fold. J Phys Chem 96:10102–10104CrossRefGoogle Scholar
  19. Judson RS, Colvin ME, Meza JC, Huffer A, Gutierrez D (1992): Do intelligent configuration search techniques outperform random search for large molecules? Int J Quant Chem 44:277–290CrossRefGoogle Scholar
  20. Lau KF, Dill KA (1989): A lattice statistical mechanics model of the conformational and sequence spaces of proteins. Macromolecules 22:3986–3997CrossRefGoogle Scholar
  21. Lavery R, Sklenar H, Zakrzewska K, Pullman B (1986): The flexibility of nucleic acids: (II) The calculation of internal energy and applications to mono nucleotide DNA. J Biomol Struct Dynam 3:989–1014Google Scholar
  22. Le Grand SM, Merz KM, Jr (1993a): The application of the genetic algorithm to the minimization of potential energy functions. J Global Opt 3:49–66CrossRefGoogle Scholar
  23. Le Grand SM, Merz KM, Jr (1993b): The application of the genetic algorithm to protein tertiary structure prediction. J Mol Biol (to be submitted)Google Scholar
  24. Le Grand SM (1993): Doctoral thesis. The Pennsylvania State UniversityGoogle Scholar
  25. Momany F, McGuire R, Burgess A, Scheraga HA (1975): Energy parameters in Polypeptides. VII. Geometric parameters, partial atomic charges, nonbonded interactions, hydrogen bond interactions, and intrinsic torsional potentials for the naturally occurring amino acids. J Phys Chem 79(22):2361–2381CrossRefGoogle Scholar
  26. Montana D, Davis L (1989): Training feedforward neural networks using genetic algorithms. In: Proceedings of the Third International Conference on Genetic Algorithms. San Mateo, CA: Morgan KaufmannGoogle Scholar
  27. Nemethy G, Scheraga H (1990): Theoretical studies of protein conformation by means of energy computations. FASEB Journal 4(14):3189–3197PubMedGoogle Scholar
  28. Ponder JW, Richards FM (1987): Tertiary templates for proteins: Use of packing criteria in the environment of allowed sequences for different structural classes. J Mol Biol 193:775–791PubMedCrossRefGoogle Scholar
  29. Radcliffe N, Wilson G (1990): Natural solutions give their best. New Scientist 126:47–50Google Scholar
  30. Schaffer J, Morishima A (1987): An adaptive crossover distribution mechanism for genetic algorithms. In: Genetic Algorithms and their Applications: Proceedings of the Second International Conference on Genetic Algorithms. Hillsdale, NJ: Lawrence ErlbaumGoogle Scholar
  31. Sippl MJ (1990): Calculation of conformational ensembles from potentials of mean force: an approach to the knowledge-based prediction of local structures in globular proteins. J Mol Biol 213:859–883PubMedCrossRefGoogle Scholar
  32. Sirag D, Weisser P (1987): Toward a unified thermodynamic genetic operator. In: Genetic Algorithms and their Applications: Proceedings of the Second International Conference on Genetic Algorithms. Hillsdale, NJ: Lawrence ErlbaumGoogle Scholar
  33. Skolnick J, Kolinski A (1990): Simulations of the folding of a globular protein. Science 250:1121–1125PubMedCrossRefGoogle Scholar
  34. Snow ME (1992): Powerful simulated-annealing algorithm locates global minimum of protein-folding potentials from multiple starting conformations. J Comp Chem 13:597–584CrossRefGoogle Scholar
  35. Sun S (1993): Reduced representation model of protein structure prediction: statistical potential and genetic algorithms. Protein Science 2:762–785PubMedCrossRefGoogle Scholar
  36. Tuffery P, Etchebest C, Hazout S, Lavery R (1991): A new approach to the rapid determination of protein side chain conformations. J Biomol Struct Dynam 8(6): 1267–1289Google Scholar
  37. Unger R, Moult J (1993): Genetic algorithms for protein folding simulations. J Mol Biol 231:75–81PubMedCrossRefGoogle Scholar
  38. Vasquez M, Nemethy G, Scheraga HA (1983): Computed conformational states of the 20 naturally occurring amino acid residues and of the prototype residue a-aminobutyric acid. Macromolecules 16:1043–1049CrossRefGoogle Scholar
  39. Walbridge C (1989): Genetic algorithms: What computers can learn from Darwin. Technology Review 92:46–53Google Scholar
  40. Wayner P (1991): Genetic algorithms. Byte 16(Jan):361–364Google Scholar
  41. Weiner S, Kollman P, Nguyen D, Case D (1986): An all atom force field for simulations of proteins and nucleic acids. J Comp Chem 7(2):230–252CrossRefGoogle Scholar
  42. Whitley D, Hanson T (1989a): The GENTTOR algorithm and selective pressure: Why rank-based allocation of reproductive trials is best. In: Proceedings of the Third International Conference on Genetic Algorithms. San Mateo, CA: Morgan KaufmannGoogle Scholar
  43. Whitley D, Hanson T (1989b): Optimizing neural nets using faster, more accurate genetic search. In: Proceedings of the Third International Conference on Genetic Algorithms. San Mateo, CA: Morgan KaufmannGoogle Scholar
  44. Whitley D, Starkweather T, Bogart C (1990): Genetic algorithms and neural networks: optimizing connections and connectivity. Parallel Computing 14:347–361CrossRefGoogle Scholar
  45. Whitley D, Starkweather T (1990): GENITORII: A distributed genetic algorithm. J Exp Theor Artif Intell 2:189–214CrossRefGoogle Scholar
  46. Wilson S, Cui W (1990): Applications of simulated annealing to peptides. Biopolymers 29:225–235PubMedCrossRefGoogle Scholar
  47. Wilson C, Doniach S (1989): A Computer model to dynamically simulate protein folding: studies with crambin. Proteins 6:193–209PubMedCrossRefGoogle Scholar

Copyright information

© Birkhäuser Boston 1994

Authors and Affiliations

  • Scott M. Le Grand
  • Kenneth M. MerzJr.

There are no affiliations available

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