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**|**Biophys J**|**v.99(5); 2010 September 8**|**PMC2931718

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Biophys J. 2010 September 8; 99(5): 1620–1627.

PMCID: PMC2931718

Emanuele Paci: ku.ca.sdeel@icap.e

Received 2010 April 19; Accepted 2010 June 17.

Copyright © 2010 by the Biophysical Society.

This document may be redistributed and reused, subject to certain conditions.

Single-molecule force spectroscopy is providing unique, and sometimes unexpected, insights into the free-energy landscapes of proteins. Despite the complexity of the free-energy landscapes revealed by mechanical probes, forced unfolding experiments are often analyzed using one-dimensional models that predict a logarithmic dependence of the unfolding force on the pulling velocity. We previously found that the unfolding force of the protein filamin at low pulling speed did not decrease logarithmically with the pulling speed. Here we present results from a large number of unfolding simulations of a coarse-grain model of the protein filamin under a broad range of constant forces. These show that a two-path model is physically plausible and produces a deviation from the behavior predicted by one-dimensional models analogous to that observed experimentally. We also show that the analysis of the distributions of unfolding forces (*p*[*F*]) contains crucial and exploitable information, and that a proper description of the unfolding of single-domain proteins needs to account for the intrinsic multidimensionality of the underlying free-energy landscape, especially when the applied perturbation is small.

Over the past decade, single-molecule atomic force microscopy studies have revealed the complexity of the folding and unfolding of single-domain proteins. Examples include the observation of parallel unfolding pathways (1,2), unfolding intermediates (3), and compact collapsed structures along the folding pathway (4). Recently, the mechanical unfolding kinetics of the native to intermediate (*N→I*) transition of the protein filamin was characterized in a broad range of pulling velocities (1–4000 nm/s), and an unexpected plateau in the modal (i.e., most probable) unfolding force, *F*^{}, was observed at low pulling velocities/loading rates (Fig. 1) (5). The convex curvature in the plot of *F*^{} as a function of ln[*R _{F}*

$${k}_{u}\left(F\right)={k}_{u}\left(0\right)\mathrm{exp}\left(-F{x}_{u}/{k}_{B}T\right),$$

where *k _{u}*(0) is the unfolding rate in the absence of force and

The modal unfolding force, *F*^{}, as a function of the loading rate, *R*_{F}_{}, calculated at each *F*^{}. The pulling velocities at which the *F*^{} were determined are indicated (*top* of the *graph*). The loading rates, *R*_{F}_{}, were **...**

The convex curvature is typically explained by the presence of two sequential barriers along the unfolding pathway of the protein (Fig. 2). At low pulling velocities, unfolding or unbinding occurs over the outer barrier, located far from the native state (Fig. 2 *A*), while at high pulling velocities, the outer barrier is sufficiently lowered by the force that the inner barrier becomes rate-limiting instead (Fig. 2 *B*). This sequential barriers (SB) model was first proposed by Merkel et al. (13), who used it to rationalize the mechanical unbinding kinetics of the streptavidin-biotin complex. If the free-energy landscape of the protein can be represented by a single reaction coordinate, the end-to-end extension, the SB model predicts that the plot of *F*^{} as a function of ln[*R _{F}*

$${F}^{}$$

where *x*_{in/out} can be thought of as the distance between the native minimum and the inner or outer barrier (Fig. 2), and *C*_{in/out} contains information about the heights of inner or outer barriers in the absence of force. Therefore, the steepness of each the quasilinear regime is inversely related to *x*_{in/out}.

One-dimensional free-energy profiles with two sequential barriers. The reaction coordinate is typically taken to be the end-to-end extension of the protein, allowing the force-dependencies, *x*_{in/out}, to be interpreted as the difference in extension between **...**

In contrast to the conventional explanation, the convex curvature observed for filamin was explained using a kinetic scheme with two unfolding pathways (see Fig. 5, denoted henceforth as the two-path kinetic scheme) (5). From Fig. 1, it can be seen that the SB model describes the *R _{F}*

The distribution of unfolding forces at each pulling velocity (*black histograms*). (*Continuous lines*/*red online*) Fits to the kinetic scheme shown in the last panel (*G*_{eq} and G are in units of *k*_{B}*T*; *x*_{eq} and *x*_{u} are in units of Å; Δ*κ* **...**

Due to the difficulty in performing single-molecule mechanical unfolding experiments, the unfolding kinetics of a protein is often studied in a narrow range of pulling velocities, and/or with limited sampling within the range of the pulling velocities investigated. In addition, the measured unfolding forces are often noisy (e.g., Δ*F*_{rms} ~15–25 pN), which makes the accurate estimation of the distribution of unfolding forces, *p*(*F*), difficult. For this reason, the *p*(*F*) is less commonly analyzed and an analysis of *F*^{} is preferred instead. Analyzing only *F*^{}, however, means that a large amount of the information (e.g., the variance and skew) contained in *p*(*F*) is neglected. Theoretical analyses of the forced unbinding of receptor-ligand complexes (17,21) have shown that the variation of *F*^{} with ln[*R _{F}*

In this article, we present results from a large number of unfolding simulations of a coarse-grain model of the protein filamin under a broad range of constant forces. These show that a two-path model is physically plausible and produces a deviation from the behavior predicted by one-dimensional models, analogous to that observed experimentally. We also show that the analysis of the distributions of unfolding forces (*p*[*F*]) contains crucial and exploitable information, and that a proper description of the unfolding of single-domain proteins needs to account for the intrinsic multidimensionality of the underlying free-energy landscape, especially when the applied perturbation is small.

All simulations were performed using a structure-based coarse-grain model (22), where each residue is represented by a C* _{α}* atom and only native interactions are attractive. The polymeric properties of the chain are governed by an amino-acid-specific dihedral potential. The topology of the model and interaction parameters were based on the lowest energy NMR structure of filamin (PDB ID: 1KSR). The dynamics of the coarse-grain model were simulated using a Langevin integrator with a friction coefficient of 1 ps

The *τ _{u}* as a function of force was fitted to the Dudko-Hummer-Szabo (DHS) rate equation (see Eq. 4 below), the Saffman-Delbrück (SB) model, and the two-path kinetic scheme (Fig. 3

$${\tau}_{u}\left(F\right)={\tau}_{\text{in}}\left(0\right)\mathrm{exp}\left[-F{x}_{\text{in}}/{k}_{\text{B}}T\right]+{\tau}_{\text{out}}\left(0\right)\mathrm{exp}\left[-F{x}_{\text{out}}/{k}_{\text{B}}T\right],$$

where *τ*_{in/out}(0) is the time needed to unfold over the inner (i.e., in) or outer (i.e., out) barrier at zero force, and *x*_{in/out} represents the force-dependence of the height of the inner or outer barrier. For the two-path model, the unfolding time for each unfolding pathway (i.e., starting from either A1 or A2) was modeled using the Bell model (7),

$$k{\left(0\right)}^{-1}\mathrm{exp}\left(-F{x}_{u}/{k}_{\text{B}}T\right),$$

where *x _{u}* is the force-dependence of the unfolding time. The equilibrium between the states A1 and A2 was modeled assuming linear force-dependence of the relevant free-energy, i.e.,

$$\mathrm{\Delta}{G}_{\text{eq}}\left(F\right)=\mathrm{\Delta}{G}_{\text{eq}}\left(0\right)-F{x}_{\text{eq}},$$

where *x*_{eq} is the force-dependence of the equilibrium free-energy.

There were 66, 34, 91, 107, 231, 149, and 199 unfolding force measurements at pulling velocities of 1–4000 nm/s, respectively. The distribution of unfolding forces, *p*(*F*), at each pulling velocity was determined by constructing a histogram of the measured unfolding forces using a bin-width of ~4–5 pN. The bin-width was chosen according to the Freedman-Diaconis rule, i.e.,

$$\text{bin}-\text{width}\approx 2\left[IQ\right]{n}^{-1/3},$$

where *IQ* is the interquartile range. The root-mean-square fluctuation in the unfolding force due to the fluctuations of the cantilever,

$$\mathrm{\Delta}{F}_{\text{rms}}\approx \sqrt{{k}_{c}\times {k}_{\text{B}}T}\approx 5\phantom{\rule{0.25em}{0ex}}pN,$$

was of similar magnitude. A total of 56 data-points (i.e., eight points per pulling velocity) was used for the fitting procedure.

To fit the *p*(*F*) to the various kinetic models, we calculated the theoretical force distributions according to the Evans-Ritchie formalism (6),

$$p\left(F\right)=\frac{{k}_{u}\left(F\right)}{R\left(F,L,v\right)}\mathrm{exp}\left[-\underset{0}{\overset{F}{\int}}\frac{{k}_{u}\left(f\right)}{R\left(f,L,v\right)}df\right],$$

(1)

where *F* is the force, *k _{u}*(

The loading rate, *R*(*F*, *L*, *v*) was calculated by modeling the polymeric spacer as a wormlike chain (23)

$$R\left(F,L,v\right)=v{\left[\frac{1}{{\kappa}_{c}}+\frac{2\beta L{l}_{p}\left(1+\beta F{l}_{p}\right)}{3+5\beta F{l}_{p}+8{\left(\beta F{l}_{p}\right)}^{5/2}}\right]}^{-1},$$

(2)

where *β* = 1/*k*_{B}*T*; *v* is the pulling velocity (1–4000 nm/s); *κ _{c}* = 6 pN/nm is the cantilever spring constant;

For the two-path kinetic scheme, *k _{u}*(

$${k}_{u}\left(F\right)=\frac{{K}_{\text{eq}}\left(F\right){k}_{A2-D}\left(F\right)+{k}_{A1-D}\left(F\right)}{{K}_{\text{eq}}\left(F\right)+1},$$

(3)

where *k _{A}*

The rate constants *k _{A}*

$$k\left(F\right)=k\left(0\right)\left[1-\frac{F{x}_{u}}{2{G}^{\mathrm{\u2021}}\left(0\right)}\right]\mathrm{exp}\left[\frac{{G}^{\mathrm{\u2021}}\left(0\right)}{{k}_{B}T}\left\{1-{\left(1-\frac{F{x}_{u}}{2{G}^{\mathrm{\u2021}}\left(0\right)}\right)}^{2}\right\}\right],$$

(4)

where *k*(0) is the usual Kramer's unfolding rate in the absence of force, and *G*^{‡}(0) and *x _{u}* are the zero force barrier height and transition state distance, respectively. We assumed

$$\mathit{k}\left(\text{0}\right)={10}^{7}\mathrm{exp}\left[{G}^{\u2021}\left(0\right)/{k}_{\text{B}}T\right],$$

thus reducing the DHS rate equation to a two-parameter model.

The equilibrium constant *K*_{eq}(*F*) between the states A2 and A1 is given by

$$-{k}_{\text{B}}T\mathrm{ln}\left[{K}_{\text{eq}}\left(F\right)\right]={G}_{\text{eq}}\left(0\right)-F{x}_{\text{eq}}-\frac{{F}^{2}}{2}\left[\mathrm{\Delta}\kappa \right],$$

(5)

where

$${G}_{\text{eq}}\left(0\right)={G}_{A2}\left(0\right)-{G}_{A1}\left(0\right)\text{and}\phantom{\rule{0.25em}{0ex}}{x}_{\text{eq}}={x}_{A2}-{x}_{A1}$$

is the difference in the free-energies and extensions of A2 and A1 at zero force, respectively; and Δ*κ* is a parameter that accounts for the force-dependence of the extensions of the states (8,11).

The best-fit parameters for each kinetic model were determined by fitting the *p*(*F*) from all seven pulling velocities simultaneously (unless indicated otherwise). The best-fitting parameters were determined by minimizing the sum of squared deviation from the experimental *p*(*F*), and a visual inspection of the resulting fits. Parameter space was explored using a two-step procedure programmed in MATLAB (The MathWorks, Natick, MA):

- Step 1. A broad search of parameter space using a genetic algorithm; and
- Step 2. A fine optimization of the best-fitting parameters from Step 1 using the Nelder-Mead algorithm.

To make the search of parameter space more efficient, all parameters were constrained to be in the following bounds: 1–30 *k*_{B}*T* for free-energies, and 1–30 Å for *x _{u}* and

The pitfalls of solely relying on the relationship between *F*^{} and ln[*R _{F}*

$${x}_{\text{in}/\text{out}}~{\left(d\mathrm{ln}\left[{\tau}_{u}\right]/dF\right)}^{-1}~d{F}^{}$$

It should be noted that the steep curvature at low forces cannot be accounted for by the DHS equation, i.e., Eq. 4 (Fig. 3 *A*, *dashed line*/*red online*). As will be clear later, the main reason for the convex curvature is the complex multistate unfolding kinetics of the filamin model. To clearly illustrate the underlying physical picture, we will use the Bell model as a simple parameterization of the rate constant in the rest of the simulation analysis.

As shown in Fig. 3 *A* (*dark continuous line*/*blue online*), the simulation results can be reproduced by the SB model. Inspection of the time series of the N-C distance at 10 pN (Fig. 3 *C*, *left*) and 20 pN (Fig. 3 *C*, *right*), however, reveal that the SB model is not consistent with the unfolding mechanism of the coarse-grain model. The time series at 10 pN shows that before unfolding, the system equilibrates rapidly between two states, A1 and A2, centered at N-C distances of ~70 Å and 95 Å, respectively. Full unfolding then proceeds from either A1 or A2. At 20 pN, the fast interconversion between A1 and A2 is lost and unfolding occurs predominantly from the more extended state, A2. There are thus two parallel unfolding pathways available to the system in the small force regime (Fig. 3 *C* and Fig. 4). In contrast, the SB model predicts that the system will only unfold via one pathway in the small force regime (Fig. 2). Taken together, the two sets of time series suggest that the unfolding kinetics of the system can be explained by a three-state kinetic scheme with parallel unfolding pathways (Fig. 3 *B*). The kinetic scheme is able to reproduce the force-dependence of *τ _{u}* relatively well (Fig. 3

The time series of the fraction of native contacts between pairs of neighboring strands at 10 pN. (*Light*/*green online* and *dark*/*black online*) Lines represent the nativeness of strands 2 and 7, respectively (see panel *C*). Strand 1 unfolds immediately upon **...**

Although a coarse-grain model of a protein was used in the simulations, the model is realistic in the sense that it captures key proteinlike features—a defined native structure, and native interactions that vary depending on the type of interactions observed in the experimentally determined structure. Thus, while the simplified model used here may not reproduce all the experimentally characterized properties of a particular protein, it is perfectly valid for looking at the general properties of proteins. In this regard, the simulations show that the curvature observed experimentally for filamin is physically possible for an Immunoglobulin-like domain, and that an alternative kinetic model can account for the convex curvature at low pulling velocities.

Unlike the SB model, there is no requirement in the two-path kinetic scheme for the unfolding pathways to be confined to a single reaction coordinate, or for unfolding to be a sequential process. The proposed kinetic scheme is thus a multidimensional generalization of the SB model that is consistent with the new view (25) of protein folding/unfolding.

Given a limited number of data-points in typical plots of ln [*τ _{u}*] versus

As mentioned in the Introduction, the plot of *F*^{} as a function of ln[*R _{F}*

As a control, we fitted the *p*(*F*) to a model where the A1→D pathway in the two-path kinetic scheme is ignored, and the A2→D pathway is modeled as before using the DHS rate equation. The resulting model is a variant of the traditional SB model where unfolding can also be initiated from the intermediate state at high pulling velocities. The fits to the SB variant (Fig. 5, *dashed lines*/*blue online*) are clearly not as good as those to the two-path kinetic scheme (Fig. 5, *continuous lines*/*red online*), especially at 20–200 nm/s where the fitted distributions are significantly displaced from the *p*(*F*). In 250 iterations of the fitting procedure (see Methods), a large proportion (28%) returned, numerically, very similar parameter estimates for the SB variant (Fig. 5). Significantly, further local optimization (i.e., Nelder-Mead minimization) of poorer fits yielded the same parameters, indicating that the parameter values that we obtained for the SB variant are likely to be optimal. Taken together, these results suggest that the additional unfolding pathway is crucial to the good fit of *p*(*F*) generated by the two-path model, and not a redundant feature of the kinetic scheme.

Force spectroscopy is providing unique insights into the free-energy landscapes of proteins thanks to the single-molecule feature of the technique and the quantifiable effect of the perturbation applied on the free-energy landscape. Forced unfolding experiments are most often interpreted by fitting the data to two-state models. Such analyses relate the features (e.g., *x _{u}*) of the underlying free-energy landscape to the dependence of the modal unfolding force (

Here, by using a combination of atomistic simulations and an analysis of the distributions of unfolding forces (*p*[*F*]) for the protein filamin, which was previously shown to exhibit anomalous unfolding kinetics (5), we attempted to illustrate that an analysis solely based on *F*^{} may give a misleading picture of the free-energy landscape. Specifically, we demonstrated that while kinetic models may fit the modal or average value of the unfolding force/time to good approximation, they may dramatically violate the whole distribution of forces. For filamin, we find that the *p*(*F*) can be well described by a model with two unfolding pathways, but not by a model with sequential barriers. At sufficiently low pulling velocities or forces, it is likely that a protein unfolds through both its forced and unforced pathway (11,18). In this regard, previous work (20,30,31) has shown that the forced and unforced pathways are distinct. In addition, extensive simulations of a coarse-grain model of filamin show that the two-path model is physically plausible for a small single-domain protein, and can produce a curvature similar to that observed experimentally.

Recently, it was elegantly demonstrated that anomalous kinetic signatures can also be explained by the smooth deformation of a single unfolding pathway by the applied force (32). In this scenario, the force changes the properties of the ground state and/or transition state resulting in the alteration of the force-dependence of the unfolding pathway. We note that the approach used here, which is analogous to a master-equation-type description of the kinetics, is compatible with the single-path picture. The interpretation of the kinetic equations in terms of the structure of the underlying free-energy landscape, however, is more subtle; e.g., how many coordinates are required to adequately describe the free-energy landscape? Are there two competing, coexisting transition barriers; or is there a single, albeit kinetically distinct, pathway at different forces? We previously showed that the experimental data for ddFLN4 can be fitted to a kinetic model with two pathways with very good agreement (5). However, the currently available experimental data does not allow a distinction between a free-energy landscape with two coexisting pathways and one with a single pathway. To better understand the free-energy landscape of this rather simple and very common protein fold, further experiments as well as all-atom simulations at experimental pulling velocities would be required.

Indeed, the correspondence of any model with reality cannot be guaranteed due to: 1), the use of simplifying assumptions; 2), the impossibility of examining all possible kinetic models; 3), the lack of detailed structural information due to the vast difference in timescales between full-atom molecular-dynamics simulations and mechanical unfolding experiments; and/or 4), the difficulty in performing the relevant experiments.

The conclusions drawn here should not be taken as a criticism of the validity of existing analyses (e.g., the Bell model), which are often justified in the context of the experiments (e.g., comparing between mutants). Instead, the results here illustrate the currently underexploited potential of force spectroscopy techniques, particularly the wealth of information contained in the *p*(*F*).

The main problem in the analysis of the *p*(*F*) is the need to obtain large amounts of data (e.g., 1000 unfolding events per pulling velocity) in a large range of pulling velocities (stability and force noise). However, with the advent of improved instruments and techniques (33,34), such problems are likely to be circumvented in the near future. Despite such difficulties, current datasets may already be sufficient to discriminate between kinetic models and/or suggest alternative interpretations, which may serve as the basis of future investigations.

We thank Peter Rein ten Wolde for comments on a previous version of the manuscript and Godfrey Beddard, Dave Brockwell, and Neal Crampton for stimulating discussions.

E.P. and Z.T.Y. are supported by the Wellcome Trust and the University of Leeds. M.S. was supported in the framework of the Elite Network of Bavaria by the International Graduate School-NanoBioTechnology (grant No. IDK-NBT). M.R. was supported by an SFB 863 Grant of Deutsche Forschungsgemeinschaft.

Michael Schlierf's present address is B CUBE, Molecular Bioengineering, TU Dresden, Dresden, Germany.

This is an Open Access article distributed under the terms of the Creative Commons-Attribution Noncommercial License (http://creativecommons.org/licenses/by-nc/2.0/), which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

1. Li L., Huang H.H., Fernandez J.M. Mechanical unfolding intermediates observed by single-molecule force spectroscopy in a fibronectin type III module. J. Mol. Biol. 2005;345:817–826. [PubMed]

2. Peng Q., Li H. Atomic force microscopy reveals parallel mechanical unfolding pathways of T4 lysozyme: evidence for a kinetic partitioning mechanism. Proc. Natl. Acad. Sci. USA. 2008;105:1885–1890. [PubMed]

3. Schwaiger I., Kardinal A., Rief M. A mechanical unfolding intermediate in an actin-crosslinking protein. Nat. Struct. Mol. Biol. 2004;11:81–85. [PubMed]

4. Garcia-Manyes S., Dougan L., Fernandez J.M. Direct observation of an ensemble of stable collapsed states in the mechanical folding of ubiquitin. Proc. Natl. Acad. Sci. USA. 2009;106:10534–10539. [PubMed]

5. Yew Z.T., Schlierf M., Paci E. Direct evidence of the multidimensionality of the free-energy landscapes of proteins revealed by mechanical probes. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 2010;81:031923. [PubMed]

6. Evans E., Ritchie K. Dynamic strength of molecular adhesion bonds. Biophys. J. 1997;72:1541–1555. [PubMed]

7. Bell G.I. Models for the specific adhesion of cells to cells. Science. 1978;200:618–627. [PubMed]

8. Sachs F., Lecar H. Stochastic models for mechanical transduction. Biophys. J. 1991;59:1143–1145. [PubMed]

9. Hummer G., Szabo A. Kinetics from nonequilibrium single-molecule pulling experiments. Biophys. J. 2003;85:5–15. [PubMed]

10. Dudko O.K., Hummer G., Szabo A. Intrinsic rates and activation free energies from single-molecule pulling experiments. Phys. Rev. Lett. 2006;96:108101. [PubMed]

11. Yew Z.T., Krivov S., Paci E. Free-energy landscapes of proteins in the presence and absence of force. J. Phys. Chem. B. 2008;112:16902–16907. [PubMed]

12. Evans E. Probing the relation between force, lifetime, and chemistry in single molecular bonds. Annu. Rev. Biophys. Biomol. Struct. 2001;30:105–128. [PubMed]

13. Merkel R., Nassoy P., Evans E. Energy landscapes of receptor-ligand bonds explored with dynamic force spectroscopy. Nature. 1999;397:50–53. [PubMed]

14. Thomas W.E., Trintchina E., Sokurenko E.V. Bacterial adhesion to target cells enhanced by shear force. Cell. 2002;109:913–923. [PubMed]

15. Marshall B.T., Long M., Zhu C. Direct observation of catch bonds involving cell-adhesion molecules. Nature. 2003;423:190–193. [PubMed]

16. Evans E., Leung A., Zhu C. Mechanical switching and coupling between two dissociation pathways in a P-selectin adhesion bond. Proc. Natl. Acad. Sci. USA. 2004;101:11281–11286. [PubMed]

17. Bartolo D., Derényi I., Ajdari A. Dynamic response of adhesion complexes: beyond the single-path picture. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 2002;65:051910. [PubMed]

18. West D.K., Olmsted P.D., Paci E. Mechanical unfolding revisited through a simple but realistic model. J. Chem. Phys. 2006;124:154909. [PubMed]

19. Best R.B., Paci E., Dudko O.K. Pulling direction as a reaction coordinate for the mechanical unfolding of single molecules. J. Phys. Chem. B. 2008;112:5968–5976. [PubMed]

20. Williams P.M., Fowler S.B., Clarke J. Hidden complexity in the mechanical properties of titin. Nature. 2003;422:446–449. [PubMed]

21. Derényi I., Bartolo D., Ajdari A. Effects of intermediate bound states in dynamic force spectroscopy. Biophys. J. 2004;86:1263–1269. [PubMed]

22. Karanicolas J., Brooks C.L., 3rd The structural basis for biphasic kinetics in the folding of the WW domain from a formin-binding protein: lessons for protein design? Proc. Natl. Acad. Sci. USA. 2003;100:3954–3959. [PubMed]

23. Dudko O.K., Hummer G., Szabo A. Theory, analysis, and interpretation of single-molecule force spectroscopy experiments. Proc. Natl. Acad. Sci. USA. 2008;105:15755–15760. [PubMed]

24. Schlierf M., Rief M. Single-molecule unfolding force distributions reveal a funnel-shaped energy landscape. Biophys. J. 2006;90:L33–L35. [PubMed]

25. Dill K.A., Chan H.S. From Levinthal to pathways to funnels. Nat. Struct. Biol. 1997;4:10–19. [PubMed]

26. Forman J.R., Yew Z.T., Clarke J. Non-native interactions are critical for mechanical strength in PKD domains. Structure. 2009;17:1582–1590. [PMC free article] [PubMed]

27. Serquera D., Lee W., Itzhaki L.S. Mechanical unfolding of an ankyrin repeat protein. Biophys. J. 2010;98:1294–1301. [PubMed]

28. Brujic J., Hermans R.I., Fernandez J.M. Single-molecule force spectroscopy reveals signatures of glassy dynamics in the energy landscape of ubiquitin. Nat. Phys. 2006;2:282–286.

29. Dougan L., Li J., Fernandez J.M. Single homopolypeptide chains collapse into mechanically rigid conformations. Proc. Natl. Acad. Sci. USA. 2009;106:12605–12610. [PubMed]

30. Paci E., Karplus M. Unfolding proteins by external forces and temperature: the importance of topology and energetics. Proc. Natl. Acad. Sci. USA. 2000;97:6521–6526. [PubMed]

31. Klimov D.K., Thirumalai D. Native topology determines force-induced unfolding pathways in globular proteins. Proc. Natl. Acad. Sci. USA. 2000;97:7254–7259. [PubMed]

32. Suzuki Y., Dudko O.K. Single-molecule rupture dynamics on multidimensional landscapes. Phys. Rev. Lett. 2010;104:048101. [PubMed]

33. Schlierf M., Berkemeier F., Rief M. Direct observation of active protein folding using lock-in force spectroscopy. Biophys. J. 2007;93:3989–3998. [PubMed]

34. Liu R., Garcia-Manyes S., Fernandez J.M. Mechanical characterization of protein L in the low-force regime by electromagnetic tweezers/evanescent nanometry. Biophys. J. 2009;96:3810–3821. [PubMed]

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