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Strategies for protein folding and design

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Abstract

Fundamental challenges in molecular biology can be addressed by using simple models on a lattice, where statistical mechanics and combinatoric techniques can be employed. The basic premise is that it is sensible to test any proposed method on the simplest of models in order to assess their validity before launching a full-scale attack on realistic problems. In this paper we follow this strategy and we present different efficient schemes to perform protein design and to extract effective amino acid interaction potentials.

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References

  1. C. Anfinsen, Principles that govern the folding of protein chains, Science181 (1973) 223–230.

    Google Scholar 

  2. J.U. Bowie, R. Luthy, and D. Eisenberg, A method to identify protein sequences that fold into a known 3-dimensional structure, Science253 (1991) 164–170.

    Google Scholar 

  3. C. Branden and J. Tooze, Introduction to Protein Structure, Garland Publishing, New York, 1991.

    Google Scholar 

  4. S.H. Bryant and C.E. Lawrence, An empirical energy function for threading protein sequence through the folding motif, Proteins: Struct. Funct. Genet.16 (1993) 92–112.

    Google Scholar 

  5. J. Bryngelson, J.N. Onuchic, J.N. Socci, and P.G. Wolynes, Funnels, pathways and the energy landscape of protein folding: A synthesis, Proteins: Struc. Funct. Genet.21 (1995) 167–195.

    Google Scholar 

  6. C.J. Camacho and D. Thirumalai, Kinetics and thermodynamics of folding in model proteins, Proc. Nat. Acad. Sci. USA90 (1993) 6369–6372.

    Google Scholar 

  7. H.S. Chan and K.A. Dill, Sequence space soup of proteins and copolymers, J. Chem. Phys.95 (1991) 3775–3787.

    Google Scholar 

  8. M.H.J. Cordes, A.R. Davidson, and R.T. Sauer, Sequence space, folding and protein design, Curr. Opin. in Struct. Biol.6 (1996) 3–10.

    Google Scholar 

  9. D.G. Covell and R. Jernigan, Conformations of folded proteins in restricted spaces, Biochemistry29 (1990) 3287–3294.

    Google Scholar 

  10. T.E. Creighton, Proteins: Structures and Molecular Properties, W. H. Freeman, New York, 1992.

    Google Scholar 

  11. G.M. Crippen, Prediction of protein folding from amino acid sequence over discrete conformation space, Biochemistry30 (1991) 4232–4237.

    Google Scholar 

  12. G.M. Crippen, Failures of inverse folding and threading with gapped alignement, Proteins: Struct. Funct. Genet.26 (1996) 167–171.

    Google Scholar 

  13. J.M. Deutsch and T. Kurosky, New algorithm for protein design, Phys. Rev. Lett.76 (1996) 323–326.

    Google Scholar 

  14. K.A. Dill, S. Bromberg, S. Yue, K. Fiebig, K.M. Yee, P.D. Thomas, and H.S. Chan, Principles of protein folding — a perspective from simple exact models, Protein Science4 (1995) 561–602.

    Google Scholar 

  15. H. Flockner, M. Braxenthaler, P. Lackner, M. Jaritz, M. Ortner, and M.J. Sippl, Progress in fold recognition, Proteins: Struct. Funct. Genet.23 (1995) 376–386.

    Google Scholar 

  16. M.S. Friederichs and P.G. Wolynes, Towards protein tertiary structure recognition by means of associative memory hamiltonians, Science246 (1989) 371–373.

    Google Scholar 

  17. A. Godzik and J. Skolnick, Sequence structure matching in globular proteins: Application to supersecondary and tertiary structure determination, Proc. Nat. Acad. Sci. USA89 (1992) 12098–12102.

    Google Scholar 

  18. R.A. Goldstein, Z.A. Luthey-Schulten, and P.G. Wolynes, Optimal protein folding codes from spin glass theory, Proc. Nat. Acad. Sci. USA89 (1992) 4918–4922.

    Google Scholar 

  19. R.A. Goldstein, Z.A. Luthey-Schulten, and P.G. Wolynes, Protein tertiary structure recognition using optimized hamiltonians with local interactions, Proc. Nat. Acad. Sci. USA89 (1992) 9029–9033.

    Google Scholar 

  20. M. Hendlick, P. Lackner, S. Weitckus, H. Floeckner, R. Froschauerer, K. Gottsbacher, G. Casari, and M.J. Sippl, 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.216 (1990) 167–180.

    Google Scholar 

  21. D.A. Hinds and M. Levitt, A lattice model for protein structure prediction at low resolution, Proc. Nat. Acad. Sci. USA89 (1992) 2536–2540.

    Google Scholar 

  22. D.T. Jones, W.R. Taylor, and J.M. Thorton, A new approach to protein fold recognition, Nature358 (1992) 86.

    Google Scholar 

  23. S. Kamtekar, J.M. Schiffer, H. Xiong, J.M. Babik, and M.H. Hecht, Protein design by binary patterning of polar and nonpolar amino acids, Science262 (1993) 1680.

    Google Scholar 

  24. Y. Kuroda, T. Nakai, and T. Ohkubo, Solution structure of a de-novo helical protein by 2DNMR spectroscopy, J. Mol. Biol.236 (1994) 862–868.

    Google Scholar 

  25. K.F. Lau and K.A. Dill, A lattice statistical mechanics model of the conformational and sequences spaces of proteins, Macromolecules22 (1989) 3986–3997.

    Google Scholar 

  26. K.F. Lau and K.A. Dill, Theory for protein mutability and biogenesis, Proc. Nat. Acad. Sci. USA87 (1990) 638–642.

    Google Scholar 

  27. H. Li, C. Tang, and N. Wingreen, Nature of driving force for protein folding: A result from analyzing the statistical potential, Phys. Rev. Lett.79 (1997) 765–768.

    Google Scholar 

  28. A. Lombardi, J.W. Bryson, and W.F. DeGrado, De novo design of heteroritmetic coiled coils, Biopolym. Peptide Sci.40 (1997) 495–504.

    Google Scholar 

  29. V.N. Maiorov and G.M. Crippen, Contact potential that recognizes the correct folding of globular proteins, J. Mol. Biol.227 (1992) 876–888.

    Google Scholar 

  30. C. Micheletti, J.R. Banavar, A. Maritan, and F. Seno, Steric constraints in model proteins, Phys. Rev. Lett.80 (1998) 5683–5686.

    Google Scholar 

  31. C. Micheletti, J.R. Banavar, A. Maritan, and F. Seno, Protein structures and optimal folding from a geometrical variational principle, Phys. Rev. Lett.82 (1999) 3372.

    Google Scholar 

  32. C. Micheletti, A. Maritan, and J.A. Banavar, A comparative study of existing and new design techniques for protein models, J. Chem. Phys.110 (1998) 9730–9738.

    Google Scholar 

  33. C. Micheletti, F. Seno, A. Maritan, and J.R. Banavar, Protein design in a lattice model of hydrophobic and polar amino acids, Phys. Rev. Lett.80 (1998) 2237–2240.

    Google Scholar 

  34. C. Micheletti, F. Seno, A. Maritan, and J.R. Banavar, Design of proteins with hydrophobic and polar amino acids, Proteins: Struct. Funct. Genet.32 (1998) 80–87.

    Google Scholar 

  35. L.A. Mirny and E.I. Shaknovich, How to derive a protein folding potential? A new approach to an old proble, J. Mol. Biol.264 (1996) 1164–1179.

    Google Scholar 

  36. S. Miyazawa and R.L. Jernigan, Estimation of effective interreside contact energies from protein crystal structures: Quasi-chemical approximation, Macromolecules18 (1985) 534–552.

    Google Scholar 

  37. S. Miyazawa and R.L. Jernigan, Residue-residue potentials with a favorable contact pair term an unfavorable high packing density term, for simulation and threading, J. Mol. Biol.256 (1996) 623–644.

    Google Scholar 

  38. M.P. Morrissey and E.I. Shakhnovich, Design of proteins with selected thermal properties, Folding and Design1 (1996) 391–405.

    Google Scholar 

  39. J. Moult, The current state of the art in protein structure prediction, Current Opinion in Biotechnology7 (1996) 422–427; Proteins: Struct. Funct. Genet.23 (1995), special issue.

    Google Scholar 

  40. K. Nishikawa and Y. Matsuo, Development of pseudoenergy potentials for assessing protein 3-D - 1-D compatibility and detecting weak homologies, Protein Eng.6 (1993) 811–820.

    Google Scholar 

  41. J.N. Onuchic, P.G. Wolynes, and N.D. Socci, Toward an outline of the topography of a realistic protein: Folding funnel, Proc. Nat. Acad. Sci. USA92 (1995) 3626–3630.

    Google Scholar 

  42. C. Pabo, Designing proteins and peptides, Nature301 (1983) 200.

    Google Scholar 

  43. B.H. Park and M. Levitt, The complexity and accuracy of discrete state models of protein structure, J. Mol. Biol.249 (1995) 493–507.

    Google Scholar 

  44. M. Pellegrini and S. Doniach, Computer simulation of antibody binding specificity, Proteins: Struct. Funct. Genet.15 (1993) 436–444.

    Google Scholar 

  45. J.W. Ponder and F.M. Richards, Tertiary templates for proteins use of packing criteria in the enumeration of allowed sequences for different structctures, J. Mol. Biol.193 (1987) 775–791.

    Google Scholar 

  46. T.P. Quinn, N.B. Tweedy, R.W. Williams, J.S. Richardson, and D.C. Richardson, De-nove design, synthesis and characterization of a beta sandwich protein, Proc. Nat. Acad. Sci. USA91 (1994) 8747–8751.

    Google Scholar 

  47. A. Rossi, private communication.

  48. A. Sali, E.I. Shakhnovich, and M. Karplus, How does a protein fold?, Nature369 (1994) 248.

    Google Scholar 

  49. F. Seno, A. Maritan, and J.R. Banavar, Interaction potentials for protein folding, Proteins: Struct. Funct. Genet.30 (1998) 244–248.

    Google Scholar 

  50. F. Seno, C. Micheletti, A. Maritan, and J.R. Banavar, Variational approach to protein design and extraction of interaction potentials, Phys. Rev. Lett.81 (1998) 2172–2175.

    Google Scholar 

  51. F. Seno, M. Vendruscolo, A. Maritan, and J.R. Banavar, Optimal protein design procedure, Phys. Rev. Lett.77 (1996) 1901–1904.

    Google Scholar 

  52. E.I. Shakhnovich, Proteins with selected sequences fold into unique native conformation, Phys. Rev. Lett.72 (1994) 3907.

    Google Scholar 

  53. E.I. Shakhnovich and A.M. Gutin, Engineering of stable and fast folding sequences of models proteins, Proc. Nat. Acad. Sci. USA90 (1993) 7195–7199.

    Google Scholar 

  54. I. Shrivastava, S. Vishveshwara, M. Cieplak, A. Maritan, and J.R. Banavar, Lattice model for rapidly folding protein-like heteropolymers, Proc. Nat. Acad. Sci. USA92 (1995) 9206–9209.

    Google Scholar 

  55. M.J. Sippl, 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 (1990) 859–883.

    Google Scholar 

  56. M.J. Sippl, Knowledge based potentials for proteins, Current Opinion in Structural Biology5 (1995) 229–235.

    Google Scholar 

  57. M.J. Sippl, M. Jaritz, M. Hendlich, M. Ortner, and P. Lackner, Statistical Mechanics, Protein Structure and Protein Substrate Interactions, S. Doniach, Ed., Plenum Publishers, New York, 1994.

    Google Scholar 

  58. R. Srinivasan and G.D. Rose, LINUS: A hierarchic procedure to predict the fold of a protein, Proteins: Struct. Funct. Genet.22 (1995) 81–99.

    Google Scholar 

  59. S. Tanaka and H.A. Scheraga, Medium and long range interaction parameters between amino acids for predicting three-dimensional structures of proteins, Macromolecules9 (1976) 945–950.

    Google Scholar 

  60. P.D. Thomas and K. Dill, An iterative method for extracting energy-like quantities from protein structure, Proc. Nat. Acad. Sci. USA93 (1996) 11628–11633.

    Google Scholar 

  61. P.D. Thomas and K. Dill, Statistical potentials extracted from protein structures: How accurate are they?, J. Mol. Biol.257 (1996) 457–469.

    Google Scholar 

  62. K. Yue and K.A. Dill, Inverse protein folding problem: Designing polymer sequences, Proc. Nat. Acad. Sci. USA89 (1992) 4163.

    Google Scholar 

  63. K. Yue and K.A. Dill, Sequence-structure relationship in proteins and copolymers, Phys. Rev. E48 (1993) 2267–2279.

    Google Scholar 

  64. K. Yue and K.A. Dill, Forces of tertiary structural organization in globular proteins, Proc. Nat. Acad. Sci. USA92 (1995) 146–150.

    Google Scholar 

  65. K. Yue, K. Fiebig, P.D. Thomas, H.S. Chan, E.I. Shackhnovich, and K.A. Dill, A test of lattice protein folding algorithms, Proc. Nat. Acad. Sci. USA92 (1995) 325.

    Google Scholar 

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Authors and Affiliations

  1. The Abdus Salam Centre for Theoretical Physics, INFM-International School for Advanced Studies (S.I.S.S.A.), Via Beirut 2-4, 34014, Trieste, Italy

    Cristian Micheletti & Amos Maritan

  2. INFM-Dipartimento di Fisica, Università di Padova, Via Marzolo 8, 35131, Padova, Italy

    Flavio Seno

  3. Department of Physics and Center for Materials Physics, 104 Davey Laboratory, The Pennsylvania State University, 16802, University Park, Pennsylvania, USA

    Jayanth R. Banavar

Authors
  1. Cristian Micheletti
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  2. Flavio Seno
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  3. Amos Maritan
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  4. Jayanth R. Banavar
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This work was supported in part by INFM, INFN sez. di Trieste, NASA and NATO.

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Micheletti, C., Seno, F., Maritan, A. et al. Strategies for protein folding and design. Annals of Combinatorics 3, 431–450 (1999). https://doi.org/10.1007/BF01608796

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