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Proceedings of the National Academy of Sciences of the United States of America logoLink to Proceedings of the National Academy of Sciences of the United States of America
. 1996 May 14;93(10):4984–4989. doi: 10.1073/pnas.93.10.4984

How optimization of potential functions affects protein folding.

M H Hao 1, H A Scheraga 1
PMCID: PMC39392  PMID: 8643516

Abstract

The relationship between the optimization of the potential function and the foldability of theoretical protein models is studied based on investigations of a 27-mer cubic-lattice protein model and a more realistic lattice model for the protein crambin. In both the simple and the more complicated systems, optimization of the energy parameters achieves significant improvements in the statistical-mechanical characteristics of the systems and leads to foldable protein models in simulation experiments. The foldability of the protein models is characterized by their statistical-mechanical properties--e.g., by the density of states and by Monte Carlo folding simulations of the models. With optimized energy parameters, a high level of consistency exists among different interactions in the native structures of the protein models, as revealed by a correlation function between the optimized energy parameters and the native structure of the model proteins. The results of this work are relevant to the design of a general potential function for folding proteins by theoretical simulations.

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Selected References

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  1. Berg BA, Celik T. New approach to spin-glass simulations. Phys Rev Lett. 1992 Oct 12;69(15):2292–2295. doi: 10.1103/PhysRevLett.69.2292. [DOI] [PubMed] [Google Scholar]
  2. Bernstein F. C., Koetzle T. F., Williams G. J., Meyer E. F., Jr, Brice M. D., Rodgers J. R., Kennard O., Shimanouchi T., Tasumi M. The Protein Data Bank: a computer-based archival file for macromolecular structures. J Mol Biol. 1977 May 25;112(3):535–542. doi: 10.1016/s0022-2836(77)80200-3. [DOI] [PubMed] [Google Scholar]
  3. Bryngelson J. D., Wolynes P. G. Spin glasses and the statistical mechanics of protein folding. Proc Natl Acad Sci U S A. 1987 Nov;84(21):7524–7528. doi: 10.1073/pnas.84.21.7524. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Ferrenberg AM, Swendsen RH. New Monte Carlo technique for studying phase transitions. Phys Rev Lett. 1988 Dec 5;61(23):2635–2638. doi: 10.1103/PhysRevLett.61.2635. [DOI] [PubMed] [Google Scholar]
  5. Ferrenberg AM, Swendsen RH. Optimized Monte Carlo data analysis. Phys Rev Lett. 1989 Sep 18;63(12):1195–1198. doi: 10.1103/PhysRevLett.63.1195. [DOI] [PubMed] [Google Scholar]
  6. Go N. Theoretical studies of protein folding. Annu Rev Biophys Bioeng. 1983;12:183–210. doi: 10.1146/annurev.bb.12.060183.001151. [DOI] [PubMed] [Google Scholar]
  7. Goldstein R. A., Luthey-Schulten Z. A., Wolynes P. G. Optimal protein-folding codes from spin-glass theory. Proc Natl Acad Sci U S A. 1992 Jun 1;89(11):4918–4922. doi: 10.1073/pnas.89.11.4918. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Goldstein R. A., Luthey-Schulten Z. A., Wolynes P. G. Protein tertiary structure recognition using optimized Hamiltonians with local interactions. Proc Natl Acad Sci U S A. 1992 Oct 1;89(19):9029–9033. doi: 10.1073/pnas.89.19.9029. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Hagler A. T., Huler E., Lifson S. Energy functions for peptides and proteins. I. Derivation of a consistent force field including the hydrogen bond from amide crystals. J Am Chem Soc. 1974 Aug 21;96(17):5319–5327. doi: 10.1021/ja00824a004. [DOI] [PubMed] [Google Scholar]
  10. Hendlich M., Lackner P., Weitckus S., Floeckner H., Froschauer R., Gottsbacher K., Casari G., Sippl M. J. Identification of native protein folds amongst a large number of incorrect models. The calculation of low energy conformations from potentials of mean force. J Mol Biol. 1990 Nov 5;216(1):167–180. doi: 10.1016/S0022-2836(05)80068-3. [DOI] [PubMed] [Google Scholar]
  11. Lee J. New Monte Carlo algorithm: Entropic sampling. Phys Rev Lett. 1993 Jul 12;71(2):211–214. doi: 10.1103/PhysRevLett.71.211. [DOI] [PubMed] [Google Scholar]
  12. Leopold P. E., Montal M., Onuchic J. N. Protein folding funnels: a kinetic approach to the sequence-structure relationship. Proc Natl Acad Sci U S A. 1992 Sep 15;89(18):8721–8725. doi: 10.1073/pnas.89.18.8721. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Maiorov V. N., Crippen G. M. Contact potential that recognizes the correct folding of globular proteins. J Mol Biol. 1992 Oct 5;227(3):876–888. doi: 10.1016/0022-2836(92)90228-c. [DOI] [PubMed] [Google Scholar]
  14. Nishimura M., Obayashi H., Ohta M., Uchiyama T., Hao Q., Saida T. No association of the 11778 mitochondrial DNA mutation and multiple sclerosis in Japan. Neurology. 1995 Jul;45(7):1333–1334. doi: 10.1212/wnl.45.7.1333. [DOI] [PubMed] [Google Scholar]
  15. Sali A., Shakhnovich E., Karplus M. How does a protein fold? Nature. 1994 May 19;369(6477):248–251. doi: 10.1038/369248a0. [DOI] [PubMed] [Google Scholar]
  16. Shakhnovich E. I., Gutin A. M. A new approach to the design of stable proteins. Protein Eng. 1993 Nov;6(8):793–800. doi: 10.1093/protein/6.8.793. [DOI] [PubMed] [Google Scholar]
  17. Shakhnovich E. I., Gutin A. M. Engineering of stable and fast-folding sequences of model proteins. Proc Natl Acad Sci U S A. 1993 Aug 1;90(15):7195–7199. doi: 10.1073/pnas.90.15.7195. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Shakhnovich EI. Proteins with selected sequences fold into unique native conformation. Phys Rev Lett. 1994 Jun 13;72(24):3907–3910. doi: 10.1103/PhysRevLett.72.3907. [DOI] [PubMed] [Google Scholar]
  19. Shrivastava I., Vishveshwara S., Cieplak M., Maritan A., Banavar J. R. Lattice model for rapidly folding protein-like heteropolymers. Proc Natl Acad Sci U S A. 1995 Sep 26;92(20):9206–9209. doi: 10.1073/pnas.92.20.9206. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Sippl M. J. 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. 1990 Jun 20;213(4):859–883. doi: 10.1016/s0022-2836(05)80269-4. [DOI] [PubMed] [Google Scholar]
  21. Tanaka S., Scheraga H. A. Medium- and long-range interaction parameters between amino acids for predicting three-dimensional structures of proteins. Macromolecules. 1976 Nov-Dec;9(6):945–950. doi: 10.1021/ma60054a013. [DOI] [PubMed] [Google Scholar]
  22. Teeter M. M., Hendrickson W. A. Highly ordered crystals of the plant seed protein crambin. J Mol Biol. 1979 Jan 15;127(2):219–223. doi: 10.1016/0022-2836(79)90242-0. [DOI] [PubMed] [Google Scholar]
  23. Yue K., Fiebig K. M., Thomas P. D., Chan H. S., Shakhnovich E. I., Dill K. A. A test of lattice protein folding algorithms. Proc Natl Acad Sci U S A. 1995 Jan 3;92(1):325–329. doi: 10.1073/pnas.92.1.325. [DOI] [PMC free article] [PubMed] [Google Scholar]

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