Evaluation of the Stability of Folding Nucleus upon Mutation

  • Mathieu Lonquety
  • Zoé Lacroix
  • Jacques Chomilier
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5265)


The development of a method that accurately predicts protein folding nucleus is critical at least on two points. On one hand, they can participate to misfolded proteins and therefore they are related to several amyloid diseases. On the other hand, as they constitute structural anchors, their prediction from the sequence can be valuable to improve database screening algorithms. The concept of Most Interacting Residues (MIR) aims at predicting the amino acids more likely to initiate protein folding. An alternative approach describes a protein 3D structure as a series of Tightened End Fragments (TEF). Their spatially close ends have been shown to be mainly located in the folding nucleus. While the current sequence-driven approach seems to capture all MIR, the structure-driven method partially fails to predict known folding. We present a stability-based analysis of protein folding to increase the recall and precision of these two methods.

Results: Prediction of the folding nucleus by MIR algorithm is in agreement with mutation stability prediction.

Availability: The database is available at: . The MIR calculation program is available at: and the TEF program at: .



Protein folding folding nucleus structure stability point mutations 


  1. 1.
    Brockwell, D.J., Smith, D.A., Radford, S.E.: Protein folding mechanisms: new methods and emerging ideas. Curr. Opin. Struct. Biol. 10(1), 16–25 (2000)CrossRefPubMedGoogle Scholar
  2. 2.
    Steward, R.E., MacArthur, M.W., Laskowski, R.A., Thornton, J.M.: Molecular basis of inherited diseases: a structural perspective. Trends Genet. 19(9), 505–513 (2003)CrossRefPubMedGoogle Scholar
  3. 3.
    Mogensen, J.E., Ipsen, H., Holm, J., Otzen, D.E.: Elimination of a misfolded folding intermediate by a single point mutation. Biochemistry 43(12), 3357–3367 (2004)CrossRefPubMedGoogle Scholar
  4. 4.
    Cerdà-Costa, N., Esteras-Chopo, A., Avilés, F.X., Serrano, L., Villegas, V.: Early kinetics of amyloid fibril formation reveals conformational reorganisation of initial aggregates. J. Mol. Biol. 366(4), 1351–1363 (2007)CrossRefPubMedGoogle Scholar
  5. 5.
    Wetlaufer, D.B.: Nucleation, rapid folding, and globular intrachain regions in proteins. Proc. Natl. Acad. Sci. USA 70(3), 697–701 (1973)CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Abkevich, V.I., Gutin, A.M., Shakhnovich, E.I.: Specific nucleus as the transition state for protein folding: evidence from the lattice model. Biochemistry 33(33), 10026–10036 (1994)CrossRefPubMedGoogle Scholar
  7. 7.
    Fersht, A.R.: Optimization of rates of protein folding: the nucleation-condensation mechanism and its implications. Proc. Natl. Acad. Sci. USA 92(24), 10869–10873 (1995)CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Shakhnovich, E., Abkevich, V., Ptitsyn, O.: Conserved residues and the mechanism of protein folding. Nature 379(6560), 96–98 (1996)CrossRefPubMedGoogle Scholar
  9. 9.
    Fersht, A.R.: Nucleation mechanisms in protein folding. Curr. Opin. Struct. Biol. 7(1), 3–9 (1997)CrossRefPubMedGoogle Scholar
  10. 10.
    Papandreou, N., Berezovsky, I.N., Lopes, A., Eliopoulos, E., Chomilier, J.: Universal positions in globular proteins. Eur. J. Biochem. 271(23-24), 4762–4768 (2004)CrossRefPubMedGoogle Scholar
  11. 11.
    Sacile, R., Ruggiero, C.: Hunting for “key residues” in the modeling of globular protein folding: an artificial neural network-based approach. IEEE Trans Nanobioscience 1(2), 85–91 (2002)CrossRefPubMedGoogle Scholar
  12. 12.
    Religa, T.L., Markson, J.S., Mayor, U., Freund, S.M.V., Fersht, A.R.: Solution structure of a protein denatured state and folding intermediate. Nature 437(7061), 1053–1056 (2005)CrossRefPubMedGoogle Scholar
  13. 13.
    Alexander, P.A., He, Y., Chen, Y., Orban, J., Bryan, P.N.: The design and characterization of two proteins with 88% sequence identity but different structure and function. Proc. Natl. Acad. Sci. 104(29), 11961–11963 (1968)Google Scholar
  14. 14.
    Ittah, V., Haas, E.: Nonlocal interactions stabilize long range loops in the initial folding intermediates of reduced bovine pancreatic trypsin inhibitor. Biochemistry 34(13), 4493–4506 (1995)CrossRefPubMedGoogle Scholar
  15. 15.
    Berezovsky, I.N., Grosberg, A.Y., Trifonov, E.N.: Closed loops of nearly standard size: common basic element of protein structure. FEBS 466(2-3), 283–286 (2000)CrossRefGoogle Scholar
  16. 16.
    Berezovsky, I.N., Kirzhner, V.M., Kirzhner, A., Trifonov, E.N.: Protein folding: looping from hydrophobic nuclei. Proteins 45(4), 346–350 (2001)CrossRefPubMedGoogle Scholar
  17. 17.
    Lamarine, M., Mornon, J.P., Berezovsky, N., Chomilier, J.: Distribution of tightened end fragments of globular proteins statistically matches that of topohydrophobic positions: towards an efficient punctuation of protein folding? Cell Mol. Life Sci. 58(3), 492–498 (2001)CrossRefPubMedGoogle Scholar
  18. 18.
    Poupon, A., Mornon, J.P.: Predicting the protein folding nucleus from sequences [correction of a sequence]. FEBS Lett. 452(3), 283–289 (1999)CrossRefPubMedGoogle Scholar
  19. 19.
    Baussand, J., Deremble, C., Carbone, A.: Periodic distributions of hydrophobic amino acids allows the definition of fundamental building blocks to align distantly related proteins. Proteins 67(3), 695–708 (2007)CrossRefPubMedGoogle Scholar
  20. 20.
    Miyazawa, S., Jernigan, R.L.: Residue-residue potentials with a favorable contact pair term and an unfavorable high packing density term, for simulation and threading. J. Mol. Biol. 256(3), 623–644 (1996)CrossRefPubMedGoogle Scholar
  21. 21.
    Chomilier, J., Lamarine, M., Mornon, J.P., Torres, J.H., Eliopoulos, E., Papandreou, N.: Analysis of fragments induced by simulated lattice protein folding. C. R. Biol. 327(5), 431–443 (2004)CrossRefPubMedGoogle Scholar
  22. 22.
    Poupon, A., Mornon, J.P.: Populations of hydrophobic amino acids within protein globular domains: identification of conserved “topohydrophobic” positions. Proteins 33(3), 329–342 (1998)CrossRefPubMedGoogle Scholar
  23. 23.
    Cheng, J., Randall, A., Baldi, P.: Prediction of protein stability changes for single-site mutations using support vector machines. Proteins 62(4), 1125–1132 (2006)CrossRefPubMedGoogle Scholar
  24. 24.
    Capriotti, E., Fariselli, P., Casadio, R.: I-Mutant2.0: predicting stability changes upon mutation from the protein sequence or structure. Nucleic Acids Res. 33(Web Server issue), W306–W310 (2005)CrossRefGoogle Scholar
  25. 25.
    Zhou, H., Zhou, Y.: Distance-scaled, finite ideal-gas reference state improves structure-derived potentials of mean force for structure selection and stability prediction. Protein Sci. 11(11), 2714–2726 (2002)CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Gilis, D., Rooman, M.: PoPMuSiC, an algorithm for predicting protein mutant stability changes: application to prion proteins. Protein Eng. 13(12), 849–856 (2000)CrossRefPubMedGoogle Scholar
  27. 27.
    Parthiban, V., Gromiha, M.M., Schomburg, D.: CUPSAT: prediction of protein stability upon point mutations. Nucleic Acids Res. 34(Web Server issue), W239–W242 (2006)CrossRefGoogle Scholar
  28. 28.
    Schymkowitz, J., Borg, J., Stricher, F., Nys, R., Rousseau, F., Serrano, L.: The FoldX web server: an online force field. Nucleic Acids Res. 33(Web Server issue), W382–W388 (2005)CrossRefGoogle Scholar
  29. 29.
    Kumar, M.D.S., Bava, K.A., Gromiha, M.M., Prabakaran, P., Kitajima, K., Uedaira, H., Sarai, A.: ProTherm and ProNIT: thermodynamic databases for proteins and protein-nucleic acid interactions. Nucleic Acids Res. 34(Database issue), D204–D206 (2006)CrossRefGoogle Scholar
  30. 30.
    Fersht, A.R., Daggett, V.: Protein folding and unfolding at atomic resolution. Cell 108(4), 573–582 (2002)CrossRefPubMedGoogle Scholar
  31. 31.
    Shakhnovich, E.: Protein folding thermodynamics and dynamics: where physics, chemistry, and biology meet. Chem. Rev. 106(5), 1559–1588 (2006)CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Mathieu Lonquety
    • 1
    • 2
  • Zoé Lacroix
    • 1
    • 3
  • Jacques Chomilier
    • 2
  1. 1.Scientific Data Management LaboratoryArizona State UniversityTempeUSA
  2. 2.IMPMCUniversité Pierre et Marie Curie, UMR 7590 CNRSParisFrance
  3. 3.Pharmaceutical Genomics DivisionTranslational Genomics Research InstituteScottsdaleUSA

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