Dwell-Time Distribution Analysis of Polyprotein Unfolding Using Force-Clamp Spectroscopy

Brujić, Jasna; Hermans, Rodolfo I.; Garcia-Manyes, Sergi; Walther, Kirstin A.; Fernández, Julio M.

doi:10.1529/biophysj.106.099481

Cited by 64 publications

(72 citation statements)

References 29 publications

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“…The probability of picking up and unfolding shorter length polyprotein constructs is significantly larger than that of picking up and unfolding full length constructs, and it is in agreement with data collected using I27 polyprotein constructs using two types of cantilevers, namely glass and gold substrates, and using constructs with or without a terminal cysteine residue [61]. The effect of the variation in probability of picking up and unfolding different numbers of domains has also been observed in the analysis of single-molecule force spectroscopy data from force-clamp spectroscopic techniques [31,55]. Experiments using a polyprotein containing 12 domains observed an unequal unfolding probability and measured that the population of observed events decreased by 22% for each extra observed unfolding event [55].…”

Section: Resultssupporting

confidence: 83%

“…A previous study using single-molecule force-clamp spectroscopy measured the kinetics of protein unfolding at a constant force and found that while the majority of the experimental data could be described by a single unfolding rate constant, the remainder could not, suggesting alternative unfolding barriers in the energy landscape of the protein [31]. Another single-molecule study examined the energy fluctuations in the unfolding energy landscape of a protein and a broad distribution of unfolding rates, implying large fluctuations in the energies of the folded protein [62].…”

Section: Resultsmentioning

confidence: 99%

“…As each domain in the chain unfolds, each unfolding event appears as a peak in the resulting sawtooth pattern. This process is stochastic, and it is not possible to predict the order in which a chain of identical domains will unfold [31].The final peak in every force versus extension trace is the detachment peak, which is not included in the analysis.…”

Section: Introductionmentioning

confidence: 99%

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Optimizing the calculation of energy landscape parameters from single-molecule protein unfolding experiments

et al. 2015

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Single-molecule force spectroscopy using an atomic force microscope (AFM) can be used to measure the average unfolding force of proteins in a constant velocity experiment. In combination with Monte Carlo simulations and through the application of the Zhurkov-Bell model, information about the parameters describing the underlying unfolding energy landscape of the protein can be obtained. Using this approach, we have completed protein unfolding experiments on the polyprotein (I 27) 5 over a range of pulling velocities. In agreement with previous work, we find that the observed number of protein unfolding events observed in each approach-retract cycle varies between one and five, due to the nature of the interactions between the polyprotein, the AFM tip, and the substrate, and there is an unequal unfolding probability distribution. We have developed a Monte Carlo simulation that incorporates the impact of this unequal unfolding probability distribution on the median unfolding force and the calculation of the protein unfolding energy landscape parameters. These results show that while there is a significant, unequal unfolding probability distribution, the unfolding energy landscape parameters obtained from use of the Zhurkov-Bell model are not greatly affected. This result is important because it demonstrates that the minimum acceptance criteria typically used in force extension experiments are justified and do not skew the calculation of the unfolding energy landscape parameters. We further validate this approach by determining the error in the energy landscape parameters for two extreme cases, and we provide suggestions for methods that can be employed to increase the level of accuracy in single-molecule experiments using polyproteins.

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Section: Resultssupporting

confidence: 83%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimizing the calculation of energy landscape parameters from single-molecule protein unfolding experiments

et al. 2015

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“…At each force, P(t u ) was fitted with a single exponential curve (dashed lines) according to the two-state model for protein unfolding. Although force-clamp experiments on an extensive pool of unfolding data have revealed deviations from twostate kinetics in ubiquitin (35,37), the single exponential fit gives rise to a reasonable first approximation of the rate of unfolding at each particular force (39). The significant effect of temperature on the mechanical stability of ubiquitin is demonstrated in Fig.…”

Section: Resultsmentioning

confidence: 93%

Direct Quantification of the Attempt Frequency Determining the Mechanical Unfolding of Ubiquitin Protein

Popa

Fernández

Garcia-Manyes

2011

Journal of Biological Chemistry

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Understanding protein dynamics requires a comprehensive knowledge of the underlying potential energy surface that governs the motion of each individual protein molecule. Single molecule mechanical studies have provided the unprecedented opportunity to study the individual unfolding pathways along a well defined coordinate, the end-to-end length of the protein. In these experiments, unfolding requires surmounting an energy barrier that separates the native from the extended state. A deep understanding of protein dynamics relies on the accurate determination of the free energy surface that governs the (un)folding reaction. In the case of small proteins, the (un)folding process is generally described by a two-state scenario, whereby the native and unfolded states are separated by a prominent energy barrier (1). The conversion between these two states has been traditionally given the treatment of a firstorder chemical reaction, where the concentration of the reactant species, the native state of the protein, decreases exponentially with time (2). Such a simplified kinetic analysis provides the framework to study the (un)folding reaction in terms of the Eyring transition state theory (TST), 3 which allows quantification of the rate constant for a given chemical reaction as a function of temperature (3, 4)where ‡ is the prefactor, k B is the Boltzmann constant, T is the absolute temperature, and ⌬G ‡ is the activation energy. In this simplified equation,where C°is the standard state concentration, h is the Planck's constant, and n is the order of the reaction. The factor (k B T/h) is a frequency factor, equal to 6 ps Ϫ1 at 300 K, for the crossing of the transition state. The generalized transmission coefficient, ␥(T), relates the actual rate of the reaction to that obtained from the simple transition state theory, in which ␥(T) ϭ 1. Over the last two decades, a great number of studies have reported the effect of temperature on the (un)folding kinetics of a plethora of distinct proteins using different biochemistry bulk techniques. With a few exceptions (5), the apparent consensus (6) is that although protein unfolding usually follows simple Arrhenius kinetics, the folding kinetics exhibit more complex dependences with temperature, resulting in curvature in the ln k f versus 1/T plots (7-12). The origin of such non-Arrhenius behavior during the folding reaction can be generically explained either in terms of the rate of escape from different minima along a rough energy landscape, thus yielding super-Arrhenius kinetics, or most likely, based on the hydrophobic effect, involving a large change in heat capacity during the folding process (13-15), with the heat capacity of the transition state placed somewhere in between the folded and unfolded states (16).Single molecule force spectroscopy has provided a new vista on the molecular mechanisms underlying protein (un)folding at the subnanometer scale (17). In stark contrast with protein unfolding experiments conducted using traditional bulk experiments, where the radius of g...

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“…As pointed out by Brujic et al, 6 the probability that n domains in an N domain chain will be unfolded is related to the unfolding probability for an isolated domain by the binomial distribution PðN; n; tÞ ¼ N! n!ðN À nÞ!…”

Section: Michaelis-menten Model Of Enzyme Catalyzed Unfolding Of a Mumentioning

confidence: 96%

Deformation Model for Thioredoxin Catalysis of Disulfide Bond Dissociation by Force

Pereverzev

Prezhdo

2009

Cel. Mol. Bioeng.

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Abstract-We develop a theoretical model describing the effect of applied force on the rate of enzymatic dissociation of disulfide bonds (Wiita, A. P. et al., Nature 450:124-127, 2007). The timescale characterizing the extension of the 8-domain protein chain, in which every domain contains a disulfide bond, exhibits anomalous force dependence: The extension time first increases and then decreases with increasing force. This type of force dependence indicates catch-binding and is under active investigation in a variety of systems. The disulfide system presents the first example of a chemical catch-bond. The key difference between the deformation model developed here and the two-pathway model used in the original publication is the bond-deformation term in the force dependence of the dissociation rate constant. The bond-deformation concept provides a different interpretation of the phenomenon. Rather than invoking a new dissociation pathway, which is difficult to rationalize for a simple S-S bond, catch-binding is explained by a force-induced deformation in the protein system disfavoring bond dissociation by thioredoxin. The analysis of the experimental data is performed within the Michaelis-Menten kinetic mechanism of enzyme catalysis. A simplified version of the MichaelisMenten mechanism containing only four parameters is found to provide a quantitative description of the key features of the experimental data.

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Dwell-Time Distribution Analysis of Polyprotein Unfolding Using Force-Clamp Spectroscopy

Cited by 64 publications

References 29 publications

Optimizing the calculation of energy landscape parameters from single-molecule protein unfolding experiments

Optimizing the calculation of energy landscape parameters from single-molecule protein unfolding experiments

Direct Quantification of the Attempt Frequency Determining the Mechanical Unfolding of Ubiquitin Protein

Deformation Model for Thioredoxin Catalysis of Disulfide Bond Dissociation by Force

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