Golden triangle for folding rates of globular proteins

Garbuzynskiy, Sergiy O.; Ivankov, Dmitry N.; Bogatyreva, Natalya S.; Finkelstein, Alan

doi:10.1073/pnas.1210180110

Cited by 60 publications

(116 citation statements)

References 36 publications

(30 reference statements)

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“…For the same database of 65 two-state proteins, the prediction results are listed in Table 6. From Table 6 we find the present prediction is better than other empirical models in the correlation coefficient R and it is comparable with model ACO, SMCO and L eff but better than others in the standard error σ. Garbuzynskiy et al [42] recently proved that the measured protein folding rates fall within a narrow triangle (called Golden triangle). Our results give an explanation for the origin of the Golden triangle.…”

Section: Results Of Statistical Analysis Of 65 Two-state Protein Foldmentioning

confidence: 99%

“…(i) Study the relationship between folding free energy ∆G and N. At a given temperature, it was reported that the folding free energy ∆G of a polypeptide chain is approximately proportional to chain length [42,56]. Accordingly, we investigated ∆G with N for 65 two-state proteins and found a good linear relationship existing in these two quantities:…”

Section: Statistical Investigations On the Folding Rates Of 65 Two-stmentioning

confidence: 99%

“…Recently Garbuzynskiy and coworkers [42] collected folding rate data for 107 proteins-69 two-state proteins and 38 multistate proteins. Of the 69 two-state proteins, four (PDB code 1VII, 2PDD, 1PRB and 2A3D, respectively) will not be considered in our study because their folding experiments were carried out at high temperature, and extrapolation of these experimental results to 25°C inevitably contains a large error.…”

Section: Datasetsmentioning

confidence: 99%

“…Here ∆G (free energy decrease per molecule) is taken from the measured value ∆G/(k B T)=lnk f lnk u in the literature (k f and k u are experimental folding and unfolding rate, respectively) [42] at T≠T c with T lower than T c by an amount not too small. The data of ∆G and N for 65 proteins can be found in Table 1.…”

Section: Statistical Investigations On the Folding Rates Of 65 Two-stmentioning

confidence: 99%

“…On the other hand, in studying the temperature dependence of folding rate, we used the experimental data of 16 proteins collected by Ghosh et al [33] (Table 2). [42]. *, The chain length L means the number of folded residues according to PDB.…”

Section: Datasetsmentioning

confidence: 99%

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Statistical analyses of protein folding rates from the view of quantum transition

Luo

2014

Sci. China Life Sci.

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Understanding protein folding rate is the primary key to unlock the fundamental physics underlying protein structure and its folding mechanism. Especially, the temperature dependence of the folding rate remains unsolved in the literature. Starting from the assumption that protein folding is an event of quantum transition between molecular conformations, we calculated the folding rate for all two-state proteins in a database and studied their temperature dependencies. The non-Arrhenius temperature relation for 16 proteins, whose experimental data had previously been available, was successfully interpreted by comparing the Arrhenius plot with the first-principle calculation. A statistical formula for the prediction of two-state protein folding rate was proposed based on quantum folding theory. The statistical comparisons of the folding rates for 65 two-state proteins were carried out, and the theoretical vs. experimental correlation coefficient was 0.73. Moreover, the maximum and the minimum folding rates given by the theory were consistent with the experimental results. quantum folding, protein folding rate, temperature dependence, number of torsion mode, folding free energy Citation:Lv J, Luo LF. Statistical analyses of protein folding rates from the view of quantum transition. Sci China Life Sci, 2014Sci, , 57: 1197Sci, -1212Sci, , doi: 10.1007 It is well known that a protein chain can spontaneously fold into its unique native structure [1,2]. To paraphrase Levinthal's paradox, if a protein were to attain its correctly folded configuration by sequentially sampling all the possible conformations, it would require a period of time longer than the age of the universe to arrive at its correct native conformation [3]. Apart from theory, it is a fact that experimentally measured times for spontaneous folding of single-domain globular proteins range from microseconds [46] to tens of minutes [7]. Thus, how configurations of proteins are determined and what makes them fold so quickly are questions that constitute a longstanding puzzle in molecular biology. While two prominent models have been proposed to study the protein folding mechanism, folding nucleus [8,9] and folding tunnel [1014], the importance of topology and contact order in protein folding has been recognized over the last 15 years, and many new models to predict the protein folding rate have been published [1529]. Curiously, it is notable that the rate at which proteins fold is highly sensitive to temperature, showing non-Arrhenius behavior, i.e., the temperature dependence of the rate constant is, in fact, not exponential for these reactions. The nonlinearity of logarithm folding rate on temperature 1/T has been conventionally interpreted by the nonlinear temperature dependence of the configurational diffusion constant on rough energy landscapes [30] or by the temperature dependence of hydrophobic interaction [31,32]. Another model was proposed more recently to interpret the difference between folding and unfolding by introducing the number of dena...

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Section: Results Of Statistical Analysis Of 65 Two-state Protein Foldmentioning

confidence: 99%

Section: Statistical Investigations On the Folding Rates Of 65 Two-stmentioning

confidence: 99%

Section: Datasetsmentioning

confidence: 99%

Section: Statistical Investigations On the Folding Rates Of 65 Two-stmentioning

confidence: 99%

Section: Datasetsmentioning

confidence: 99%

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Statistical analyses of protein folding rates from the view of quantum transition

Luo

2014

Sci. China Life Sci.

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Dynamic Regulation of Biosystems by Nucleic Acids with Non‐canonical Structures

2021

Chemistry and Biology of Non‐Canonical Nucleic Acids

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Reduction of the Search Space for the Folding of Proteins at the Level of Formation and Assembly of Secondary Structures: A New View on the Solution of Levinthal′s Paradox

Finkelstein

Garbuzynskiy

2015

ChemPhysChem

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The complete volume of the protein conformation space is, by many orders of magnitude, smaller at the level of secondary structure elements than that at the level of amino acid residues; the latter, according to Levinthal's estimate, scales approximately as 10(2 L), with L being the number of residues in the chain, whereas the former, as demonstrated in this paper, scales no faster than ∼L(N), with N being the number of the secondary structure elements, which is approximately equal to L/15. This drastic decrease in the exponent (L/15 instead of 2 L) explains why sampling of the conformation space does not contradict the ability of the protein chain to find its most stable fold.

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Golden triangle for folding rates of globular proteins

Cited by 60 publications

References 36 publications

Statistical analyses of protein folding rates from the view of quantum transition

Statistical analyses of protein folding rates from the view of quantum transition

Dynamic Regulation of Biosystems by Nucleic Acids with Non‐canonical Structures

Reduction of the Search Space for the Folding of Proteins at the Level of Formation and Assembly of Secondary Structures: A New View on the Solution of Levinthal′s Paradox

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