Protein folding rates correlate with heterogeneity of folding mechanism

Physical Review Letters

Volumn 93, Issue 20, 2004, Pages

  Oztop B ^a,c   Ejtehadi, M R ^a,b   Plotkin, S S ^a  

^a UNIVERSITY OF BRITISH COLUMBIA   (Canada)

^b SHARIF UNIVERSITY OF TECHNOLOGY   (Iran)

^c BILKENT UNIVERSITY   (Turkey)

Author keywords

[No Author keywords available]

Indexed keywords

AMINO ACIDS; CHEMICAL RELAXATION; COMPUTER SIMULATION; CONCENTRATION (PROCESS); CONFORMATIONS; DATA ACQUISITION; DATA REDUCTION; FREE ENERGY; MATHEMATICAL MODELS; PROBABILITY DENSITY FUNCTION;

FOLDING RATES; HETEROGENEITY; NATIVE CONTACTS; TRANSITION STATE;

PROTEINS;

EID: 19744372247     PISSN: 00319007     EISSN: None     Source Type: Journal    
DOI: 10.1103/PhysRevLett.93.208105     Document Type: Article

References (34)

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15. Initially ℓℳ/N was used to predict rates in water [K.W. Plaxco et al., J. Mol. Biol. 277, 985 (1998)], while ℓℳis a better predictor for both 2- and 3-state proteins [D. N. Ivankov et al., Protein Science 12, 2057 (2003)]. Here we remove effects due to varying stability by considering only rates at the transition midpoint vs ℓℳ. Stability in fact correlates with chain length for 2-state proteins in water [r = 0.58, P(r) = 0.012]. This may be in part why RCO ≡ ACO/N acts as a better predictor of rates than ℓℳ under these conditions. Other measures such as cliqu-ishness [C. Micheletti, Proteins: Struct, Funct, Genet. 51, 74 (2003)] or chain length [J. Kubelka et al., Curr. Opin. Struct. Biol. 14, 76 (2004)] can aid the prediction of rates in water, or rate limits.
16. Initially ℓℳ/N was used to predict rates in water [K.W. Plaxco et al., J. Mol. Biol. 277, 985 (1998)], while is a better predictor for both 2- and 3-state proteins [D. N. Ivankov et al., Protein Science 12, 2057 (2003)]. Here we remove effects due to varying stability by considering only rates at the transition midpoint vs ℓℳ. Stability in fact correlates with chain length for 2-state proteins in water [r = 0.58, P(r) = 0.012]. This may be in part why RCO ≡ ACO/N acts as a better predictor of rates than ℓℳ under these conditions. Other measures such as cliqu-ishness [C. Micheletti, Proteins: Struct, Funct, Genet. 51, 74 (2003)] or chain length [J. Kubelka et al., Curr. Opin. Struct. Biol. 14, 76 (2004)] can aid the prediction of rates in water, or rate limits.
17. Initially ℓℳ/N was used to predict rates in water [K.W. Plaxco et al., J. Mol. Biol. 277, 985 (1998)], while ℓℳis a better predictor for both 2- and 3-state proteins [D. N. Ivankov et al., Protein Science 12, 2057 (2003)]. Here we remove effects due to varying stability by considering only rates at the transition midpoint vs ℓℳ. Stability in fact correlates with chain length for 2-state proteins in water [r = 0.58, P(r) = 0.012]. This may be in part why RCO ≡ ACO/N acts as a better predictor of rates than ℓℳ under these conditions. Other measures such as cliqu-ishness [C. Micheletti, Proteins: Struct, Funct, Genet. 51, 74 (2003)] or chain length [J. Kubelka et al., Curr. Opin. Struct. Biol. 14, 76 (2004)] can aid the prediction of rates in water, or rate limits.
18. Initially ℓℳ/N was used to predict rates in water [K.W. Plaxco et al., J. Mol. Biol. 277, 985 (1998)], while ℓℳis a better predictor for both 2- and 3-state proteins [D. N. Ivankov et al., Protein Science 12, 2057 (2003)]. Here we remove effects due to varying stability by considering only rates at the transition midpoint vs ℓℳ. Stability in fact correlates with chain length for 2-state proteins in water [r = 0.58, P(r) = 0.012]. This may be in part why RCO ≡ ACO/N acts as a better predictor of rates than ℓℳ under these conditions. Other measures such as cliqu-ishness [C. Micheletti, Proteins: Struct, Funct, Genet. 51, 74 (2003)] or chain length [J. Kubelka et al., Curr. Opin. Struct. Biol. 14, 76 (2004)] can aid the prediction of rates in water, or rate limits.
19. For a detailed description of the simulation model see, for example, Z. Guo, D. Thirumalai, and J. D. Honeycutt, J. Chem. Phys. 97, 525 (1992); J.E. Shea, Y.D. Nochomivitz, Z. Guo and C. L. Brooks III, J. Chem. Phys. 109, 2895 (1998), or C. Clementi, H. Nymeyer, and J. N. Onuchic, J. Mol. Biol. 298, 937 (2000).
20. For a detailed description of the simulation model see, for example, Z. Guo, D. Thirumalai, and J. D. Honeycutt, J. Chem. Phys. 97, 525 (1992); J.E. Shea, Y.D. Nochomivitz, Z. Guo and C. L. Brooks III, J. Chem. Phys. 109, 2895 (1998), or C. Clementi, H. Nymeyer, and J. N. Onuchic, J. Mol. Biol. 298, 937 (2000).
21. For a detailed description of the simulation model see, for example, Z. Guo, D. Thirumalai, and J. D. Honeycutt, J. Chem. Phys. 97, 525 (1992); J.E. Shea, Y.D. Nochomivitz, Z. Guo and C. L. Brooks III, J. Chem. Phys. 109, 2895 (1998), or C. Clementi, H. Nymeyer, and J. N. Onuchic, J. Mol. Biol. 298, 937 (2000).
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25. S.S. Plotkin and J.N. Onuchic, Proc. Natl. Acad. Sci. U.S.A. 97, 6509 (2000); J. Chem. Phys. 116, 5263 (2002).
26. The Protein Data Bank (PDB) codes of these proteins are: 1AEY, 1APS, 1BF4, 1FKB, 1HRC, 1LMB, 1MJC, 1NYF, 1PGB, IRIS, 1SRL, 1TEN, 1TIT, 1UBQ, 1YCC, 2AIT, 2CI2, 2PTL, 2VIK. The various references containing experimental data for rates and φ values for these proteins can be found at www.physics.ubc.ca/~steve/exptl.html.
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28. S. Takada, Proc. Natl. Acad. Sci. U.S.A. 96, 11698 (1999); D. Baker, Nature (London) 405, 39 (2000).
29. PDB codes: 1AB7, 1AEY, 1APS, 1CSP, 1FKB, 1HRC, 1LMB, 1MJC, 1NMG, 1NYF, 1SHG, 1SRL, 1UBQ, 1YCC, 2AIT, 2CI2, 2PTL, 2U1A.
30. M.R. Ejtehadi, S.P. Avail, and S.S. Plotkin, Proc. Natl. Acad. Sci. U.S.A. 101, 15088 (2004).
31. M. Lindberg, J. Tangrot, and M. Oliveberg, Nat Struct. Biol. 9, 818 (2002).
32. A. R. Fersht, Structure and Mechanism in Protein Science (W.H. Freeman and Co., New York, 1999).
33. J. N. Onuchic, N. D. Socci, Z. Luthey-Schulten, and P.G. Wolynes, Fold. Des, l, 441 (1996).
34. -3.