Letter pubs.acs.org/JPCL
Can Local Probes Go Global? A Joint Experiment−Simulation Analysis of λ6−85 Folding Shahar Sukenik,† Taras V. Pogorelov,*,†,# and Martin Gruebele*,†,‡ †
Department of Chemistry, School of Chemical Sciences, and Beckman Institute for Advanced Science and Technology, #National Center for Supercomputing Applications, and ‡Department of Physics and Center for Biophysics and Quantitative Biology, University of Illinois at Urbana−Champaign, Champaign, Illinois 61801, United States S Supporting Information *
ABSTRACT: The process of protein folding is known to involve global motions in a cooperative affair; the structure of most of the protein sequences is gained or lost over a narrow range of temperature, denaturant, or pressure perturbations. At the same time, recent simulations and experiments reveal a complex structural landscape with a rich set of local motions and conformational changes. We couple experimental kinetic and thermodynamic measurements with specifically tailored analysis of simulation data to isolate local versus global folding probes. We find that local probes exhibit lower melting temperatures, smaller surface area changes, and faster kinetics compared to global ones. We also see that certain local probes of folding match the global behavior more closely than others. Our work highlights the importance of using multiple probes to fully characterize protein folding dynamics by theory and experiment.
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native contacts are important for folding. However, not all native contacts are equally important; only a subset of native contacts is important either because they limit the rate of folding (e.g., a loop residue whose rearrangement is the ratelimiting step in folding) or because they are critical for overall stability (e.g., a hydrophobic core residue that cannot be mutated without unfolding the protein).16,17 We hypothesize that probe-dependent disparities in experimental unfolding studies can result from different local probes even while the global folding reaction is well-approximated by a single global coordinate. A local probe can detect relatively small changes in structure, such as a path that leads to a local minimum in the free energy. Such probes tend to be spatially localized and will report on motion of a single motif. Examples include isotopically labeled infrared or Raman spectra, contact pair quenching, or Trp fluorescence quenching.18−20 A global probe will reveal the cooperative motions that result in largescale structural change. Examples include differential scanning calorimetry, circular dichroism, or overall IR and Raman measurements.21−23 However, when does a local probe report on the thermodynamics and kinetics of the global folding reaction? We combine recent advances in experimental and simulation analysis to examine the emergence of global probes in the fivehelix bundle λ6−85. By coupling kinetic and equilibrium protein folding experiments with an analysis of long all-atom molecular dynamics (MD) trajectories, we compare a global probe with a
he study of protein folding has seen tremendous advancement in recent years, thanks to a closer coupling between theory, experiments, and simulation.1−3 A key aspect of folding is its cooperativity. Many small proteins show a single cooperative transition from the folded to the unfolded ensemble under chemical, temperature, or pressure stress, as manifested in a sigmoidal unfolding curve.4−6 In contrast to this observation, the cooperative transition can shift around depending on the identity of the experimental probe being used.7,8 Such probe dependence is usually taken to indicate “non-two-state” folding, that is, the existence of folding intermediates that are detected differently by disparate probes. A view of protein folding that encompasses both the overall global reaction coordinate (apparently two-state) and the local structural changes that occur in the multidimensional “folding funnel”3,9 requires a comparison of multiple probes. Important progress has been made in this direction by several groups. For example, NMR spectroscopy by Sosnick and co-workers10 and Muñoz and co-workers11 shows that in equilibrium, certain residues monitor folding globally but others do not. Lapidus and co-workers12 used a microfluidic system and four different spectroscopic experimental probes to examine the submillisecond kinetics of protein G and compared these with Markov-state models constructed from large-scale simulations of the folding process. Here, again, the authors find different rates depending on the monitored probe. We recently showed, using contact-quenched tryptophan (Trp) residues, that different local probes can respond differently even if the overall folding process is well-approximated by cooperative two-state folding.13 The principle of minimal frustration14 and subsequent analysis of simulations15 both propose that mainly © 2016 American Chemical Society
Received: March 13, 2016 Accepted: April 21, 2016 Published: April 21, 2016 1960
DOI: 10.1021/acs.jpclett.6b00582 J. Phys. Chem. Lett. 2016, 7, 1960−1965
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Figure 1b), wavelength-integrated FI, and fluorescence peak wavelength shift (FP, raw data for both fluorescence analyses in Figure 1c) shown in Figure 1d. As expected, the melting temperature (Tm) for a given probe differs between mutants, likely due in part to the double-point mutation (Figure 1e). More striking are the consistent differences between Tm among different probes for a single mutant. This can be understood through the nature of the probes: CD reports on the overall loss of secondary structure in the protein (a global probe). FI reports primarily on the distance-dependent quenching of Trp by the Tyr on an adjacent helix (a local contact probe). FP monitors primarily the Stokes shift of the Trp side chain when exposed to the surrounding solvent (an intermediate probe).26 Two limiting cases are offered for local probes: they can either report on isolated local structural changes, or they can report on local changes that are strongly coupled to global unfolding (highly cooperative or two-state-like unfolding transition). In the former case, we hypothesize that the local probe will exhibit faster kinetics and a lower melting temperature than a global probe because isolated local structural changes are smaller and require less free energy. In the latter case, the local probe is expected to approach the global melting temperature as an upper limit and the global rate as a lower limit. Of course, this hypothesis assumes that global unfolding is truly complete. If stable residual structure exists in the “unfolded” state (e.g., a stable helix), its local probe may have an even higher melting temperature and even slower kinetics than the “global” unfolding to an incompletely denatured state. Such an observation could then be used to characterize residual unfolded structure. For the probes that we used, the former case holds true: we see that for all mutants, the Tm is highest by CD (global probe), lowest by FI (local probe), and intermediate by FP (intermediate probe) in Figure 1e (fit details described in SI section S3). The mutants also show three distinct behaviors. At one extreme, λ12 is most stable, has FI/FP stabilities not far below the CD stability, and has FI/FP cooperativities very similar to those from the CD cooperativity. (The cooperativities δg1 measure how quickly the transitions in Figure 1d occur and are listed in SI Table S2.) At the other extreme, λ13 is least stable, has the lowest FI/FP stabilities relative to CD, and has FI/FP cooperativities different from those of CD and from one another. λ32 is an intermediary, with λ13-like low stability, λ12-like similarity of the FI/FP melting point to the CD melting point, and intermediate consistency of δg1 values. Thus, we predict that among the local FI probes, FI for λ12 comes much closer to reporting on global unfolding than FI for λ13. Next, we examine the wavelength-integrated FI as a function of time after a laser T-jump. We previously analyzed T-jump data via changes in fluorescence lifetime and found that this probe was sensitive to formation of a short-lived intermediate (“microstate”) with helices 1 and 3 in contact but helix 2 detached.13 Here we reanalyze that data to take advantage of the increased FI that results from loss of Trp−Tyr contact upon unfolding. We expect the kinetic FI probe to correspond more closely to the thermodynamic results in Figure 1 than would fluorescence lifetime decays. Indeed, the FI kinetics analysis yields results in line with the FI-detected thermodynamics and different from lifetime-detected kinetics (see SI Figure S1 and Table S3). The FI relaxation amplitudes in Figure 2a match with a high degree of accuracy the FI change of the equilibrium measurements (Figure S1). The FI relaxation rates reveal that
series of local probes. We find that the global probe is associated with a higher apparent thermal stability, a larger change in solvent-accessible surface area, and slower structural dynamics compared with local probes. We also see that one of the local probes approaches the global behavior, indicating that carefully selected local probes can be intimately tied to, and report on, global structural changes. We previously used double mutants of λ-repressor (Figure 1a, sequences in Table S1), which exhibit mutant- and probe-
Figure 1. λ6−85 structure and stability. (a) A folded representation of λ12, highlighting helices 1 to 3 in blue and positions of Tyr−Trp probes (shown in red). (b) Raw CD and (c) fluorescence spectra of λ12 upon a temperature increase (dark to light colors). (d) Thermal denaturation of λ12 as detected by CD, fluorescence integrated intensity (FI), and fluorescence peak wavelength shift (FP). Black curve: in 50 mM potassium phosphate buffer, pH 7; red curve: buffer and 1.5 M stabilizing osmolyte TMAO; blue curve: buffer and 1.5 M denaturing osmolyte urea. Dashed lines represent the buffer-only melting temperature determined by model fit (described in SI, section S3). (e) Tm from a global fit of all melting spectra for each probe and for each mutant. Error bars are standard errors from the fit.
dependent stability,8,24 to report on local distances between three of the five helices that make up its fold.13 We achieved this by inserting proximal Trp and Tyr residues into adjacent helices. Trp fluorescence quenching by Tyr is exponentially sensitive to the distance between the two residues.25 The positions of these residues, highlighted in Figure 1a, are optimized to report on distances between helices 1 and 2, 1 and 3, and 2 and 3 (protein mutants λ12, λ13, and λ32, respectively). As the protein unfolds and the two labeled helices draw apart, quenching of Trp by Tyr diminishes, causing an increase in integrated fluorescence intensity (FI; Figure 1c and SI). We start with equilibrium experiments to show that local probes can report on global folding. We first examine the thermal stability of the three mutants using three distinct experimental probes: circular dichroism (CD, raw data in 1961
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the aromatic residues Trp22−Tyr33 (local probe Q12), Trp22− Phe51 (local probe Q13,), and Tyr33−Phe51 (local probe Q32) to mimic the experimental fluorescence quenching pairs. The cluster with the largest number of members is designated as the folded ensemble, which defines the native contacts used in calculating the value of the probe for each frame (see SI section S5 for additional details). Approximately 40 overlaid structures from the folded cluster are shown in the insets of Figure 3a,
Figure 2. T-jumps of λ-repressor mutants. (a) Time-resolved integrated fluorescence (normalized to the integral prior to jump). For λ12, measurements are done in 2.2 M GuHCl for comparison at a similar temperature range. Temperature increases from darker to lighter colors. (b) Decays from exponential fits show the difference in observed relaxation kinetics. Lines are linear fits of the data (symbols). Shaded areas correspond to 95% confidence intervals.
parting of helices 1 and 2 is the process with the slowest relaxation at low temperatures. The distance between helices 1 and 3, as well as helices 3 and 2, relaxes more rapidly (Figure 2b). The slope of rate versus temperature for λ13 displays almost no temperature dependence, highlighting a possible local mechanistic difference for that specific mutant. Consistent with the thermodynamic data, the kinetic data indicate that while the drawing apart of helices 1 and 2 is coupled to slow global unfolding of the entire protein, helices 1 and 3 and 3 and 2 are able to draw apart in structural changes that occur faster at lower temperatures than those for helices 1 and 2. Although FI of contact-quenched Trp−Tyr pairs is, by definition, a local probe (compared with CD-monitored unfolding), some local probes (helices 1−3 in λ13) report on global structural change, while others report on a truly local change. Next, we show that simulations match experimental trends. One of the major advantages of using the λ6−85 protein to examine different unfolding probes is the set of extensive, unbiased MD simulations recently performed by D.E. Shaw Research (DESR) and made available to researchers.2 These simulations capture dozens of global folding and unfolding events of the λ12 mutant, as measured by a native-contact-based reaction coordinate. For these simulations, the term “reaction coordinate” is synonymous with our experimental “probe” because it is this coordinate that partitions the folded and unfolded ensembles and determines the resulting kinetics and thermodynamics of protein folding in the simulation. Importantly, while experiments aimed at comparison with these simulations rely on chemical denaturation,10 we focus on temperature-driven denaturation, a process that is more directly comparable to these simulations. We select four different computational probes to analyze the four ∼150 μs long simulation trajectories from DESR. The computational probes were selected based on RMSD clustering.27 RMSD was calculated between all heavy atoms for the global probe Qf and only between the heavy atoms of
Figure 3. Analysis of MD trajectories by different probes. (a) Probability distribution of Q (histograms, left y-axis) and PTP (lines, right y-axis). Vertical dashed lines correspond to the unfolded ensemble upper Q cutoff and folded ensemble lower Q cutoff. The inset shows an overlay of ∼40 structures from the folded ensemble determined by each probe. Error bars are standard deviations of PTP for the four trajectories. (b) Average folding (lighter color) and unfolding (darker color) times for each probe. The average is indicated by the red square. Box lines determine 25%, median, and 75% of the data spread (black symbols). Whiskers are 5 and 95% of the data.
highlighting the relatively structured folded ensemble of Qf or Q12, the less structured one of Q13, and the intermediate one of Q32. Measuring the fraction of helical residues also shows the same trend, as seen in SI section S5 and Figure S2. The folded cluster is used to define native contacts for the probe, which satisfy spatial (r < 4.5 Å) and sequential distance (four or more amino acids apart in sequence) constraints between heavy atoms. The weighted native contact content is then determined for each frame in each trajectory, and the resulting probability distribution for each probe is plotted in Figure 3a (histograms, left axes). The histograms highlight the prominent saddle points between the folded (high Q) and unfolded (low Q) peaks in Qf and Q12, compared to the smaller ones in Q13 and Q32. To characterize the transition path (TP) as described by the probe, we use the approach of Best and Hummer15,28 to quantify the probability for a given Q value to be on a TP, PTP. Briefly, we determine TPs as parts of a trajectory leading directly from one ensemble to the other, without backtracking to the cutoff of origin (shown as dashed vertical lines, Figure 3a). We then calculate the probability for a 1962
DOI: 10.1021/acs.jpclett.6b00582 J. Phys. Chem. Lett. 2016, 7, 1960−1965
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The Journal of Physical Chemistry Letters
main assumptions: (1) Addition of osmolytes does not introduce new states that were not sampled in the four λ12 simulations (because we do not have trajectories of the protein in the presence of osmolytes), and (2) because the simulated m values are obtained from λ12 protein simulations only, the overall ASA change resulting from mutations is assumed to be small (because we do not have trajectories of the different protein mutants). The resulting simulated m values are show in Figure 4.
probe value to be on the TP, through the Bayesian relation PTP = P(Q|TP)P(TP)/P(Q),15 where P(Q|TP) is the probability distribution of Q in TP frames, P(TP) is the fraction of TP frames in all trajectories, and P(Q) is the probability distribution of Q in all frames (histogram in Figure 3a). The resulting PTP is shown as solid lines (right axes) in Figure 3a. While the PTP maximum of Qf approaches 0.5 (the highest value possible for diffusion-limited dynamics)29 and Q12 is higher than 0.4, Q13 and Q32 only approach 0.3, implying that the former two computational probes are better global reaction coordinates than the latter two. This means that the Q13 and Q32 probes include more nonreactive states (ones that will not lead to folding) that overlap with the transition state ensemble. We next examine the folding and unfolding rates from the simulation as sampled by the different computational probes. The folding and unfolding rates are obtained from the lifetimes in the unfolded and folded states, respectively (shown in Figure 3b). As reported by Shaw and co-workers,2 we see slower folding times compared to unfolding times for all probes; therefore, the simulation temperature slightly favors the unfolded state. Qf and Q12 have longer folding and unfolding times compared to Q13 and Q32. Specifically, the average unfolding times are ∼7 μs for Q12 and ∼2 μs for both Q13 and Q32. That 7:2 scaling is even greater than the 2:1 scaling at low temperature for the experimental relaxation times in Figure 2b. Full details of un/folding rates are available in SI, Table S4. Finally, we compare the effect of different osmolytes on local and global probes experimentally and computationally. We measure protein thermal stability in the presence of the TMAO (stabilizing, preferentially depleted at protein surface) and urea (destabilizing, preferentially accumulated at the protein surface)30,31 to quantify changes in surface area upon unfolding. The change in protein stability as a function of osmolyte concentration (termed the m value) is proportional to the change in the protein’s accessible surface area (ASA) upon un/ folding.32 See section S6 in the SI for details. For λ12, the m values as detected by CD, FI, and FP are nearly the same within error, but for λ13 and λ32, they show variability in both TMAO and urea (Table S2). This can be explained by the difference between a local and global unfolding process; helices 1 and 2 separate in concert with the entire protein unfolding (FI and CD show similar m values), while helices 1 and 3 part with different ASA changes compared to the global unfolding detected by CD. As in the thermodynamic and kinetic experiments, λ32 again occupies an intermediate ground. Indeed, the change in surface area exposure (ΔASA) according to the cutoffs for the different probes (dashed lines in Figure 3a) show that overall, surface area changes scale with probe locality, Qf > Q12 > Q32 > Q13 (Figure S3). We can compare the experimental m value with a simulated m value by using the folded and unfolded ensembles as defined by the four computational probes (dashed vertical lines in Figure 3a). To calculate the simulated m value, we first quantify the change in ASA of a specific surface type (i.e., backbone or specific side chain) between the folded and unfolded ensembles, ΔASA, by averaging the ASA in the two states. ΔASA is then multiplied by an experimentally derived preferential interaction parameter quantifying the free energy of transfer of that surface type from water to a 1 M osmolyte solution.33,34 The simulated m value is thus calculated with units of energy per molar osmolyte (see section S6 in the SI for details).33,34 To calculate the simulated m value, we make two
Figure 4. Calculated using λ12 trajectory (sim) and experimentally (exp) measured m values as induced by (a) TMAO, and (b) urea. Ticks on the y-axis specify which experimental or simulated data set was used (all fit parameters in Table S2). The errors are standard deviations from the fits (exp) and standard deviations between four independent trajectories (sim).
We note a striking difference between TMAO and urea. The m value calculated from simulations for TMAO varies around the experiment by ±10% on average, but the one calculated for urea consistently underestimates experiment by −50% on average. This can be explained by an alteration of the unfolded ensemble by urea. It has previously been reported that the unfolded ensemble predicted by the DESR simulations contains an excessively compact unfolded state when compared with a denaturant-induced unfolded ensemble.10,35 We propose that the unfolded ensemble obtained by temperature denaturation is more compact than that by denaturant,9,36,37 hence the higher accuracy of ΔASA calculations for TMAO. Indeed, when we modeled the m value by using more extended unfolded conformations,38,39 the discrepancy between experiment and simulation increased (see SI section S6 for details). Thus, we conclude that the simulations overcontract the urea-denatured state but that the experimental unfolded ensemble is still compact relative to random coils. This observation is also supported by other experimental data.40 We note that the simulated m values of λ13 display the largest disparity from experiment. This points to changes in the folded and unfolded ensembles of the λ13 mutant that are not properly reproduced in the wild-type simulation. A simulation of the λ13 mutant would be useful to resolve this question. To conclude, we find that Trp FI measurements can report on localized structural changes that can couple to a more global structural change, such as secondary structure monitored by CD. We were able to quantify the extent of coupling and rank three local coordinates. This experimental finding is corroborated by the extensive set of simulations made available by DESR. Specifically, we see that thermal stability, folding kinetics, and surface area changes are smaller for local probes compared to those for global ones. We also see that certain experimental local probes (helices 1−2 Trp fluorescence) can 1963
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(13) Prigozhin, M. B.; Chao, S.-H.; Sukenik, S.; Pogorelov, T. V.; Gruebele, M. Mapping Fast Protein Folding with Multiple-Site Fluorescent Probes. Proc. Natl. Acad. Sci. U. S. A. 2015, 112, 7966− 7971. (14) Onuchic, J. N.; Luthey-Schulten, Z.; Wolynes, P. G. Theory of Protein Folding: The Energy Landscape Perspective. Annu. Rev. Phys. Chem. 1997, 48, 545−600. (15) Best, R. B.; Hummer, G.; Eaton, W. A. Native Contacts Determine Protein Folding Mechanisms in Atomistic Simulations. Proc. Natl. Acad. Sci. U. S. A. 2013, 110, 17874−17879. (16) Dave, K.; Jäger, M.; Nguyen, H.; Kelly, J. W.; Gruebele, M. High-Resolution Mapping of the Folding Transition State of a WW Domain. J. Mol. Biol. 2016, 428, 1617. (17) Mateu, M. G.; Fersht, A. R. Nine Hydrophobic Side Chains Are Key Determinants of the Thermodynamic Stability and Oligomerization Status of Tumour Suppressor p53 Tetramerization Domain. EMBO J. 1998, 17, 2748−2758. (18) Ahmed, Z.; Beta, I. A.; Mikhonin, A. V; Asher, S. A. UV− Resonance Raman Thermal Unfolding Study of Trp-Cage Shows That It Is Not a Simple Two-State Miniprotein. J. Am. Chem. Soc. 2005, 127, 10943−10950. (19) Schätzle, M.; Kiefhaber, T. Shape of the Free Energy Barriers for Protein Folding Probed by Multiple Perturbation Analysis. J. Mol. Biol. 2006, 357, 655−664. (20) Davis, C. M.; Cooper, A. K.; Dyer, R. B. Fast Helix Formation in the B Domain of Protein A Revealed by Site-Specific Infrared Probes. Biochemistry 2015, 54, 1758−1766. (21) Chen, E.; Goldbeck, R. A.; Kliger, D. S. Nanosecond TimeResolved Polarization Spectroscopies: Tools for Probing Protein Reaction Mechanisms. Methods 2010, 52, 3−11. (22) Makhatadze, G. I.; Kim, K.-S.; Woodward, C.; Privalov, P. L. Thermodynamics of Bpti Folding. Protein Sci. 1993, 2, 2028−2036. (23) Ma, H.; Gruebele, M. Low Barrier Kinetics: Dependence on Observables and Free Energy Surface. J. Comput. Chem. 2006, 27, 125−134. (24) Prigozhin, M. B.; Gruebele, M. The Fast and the Slow: Folding and Trapping of λ6−85. J. Am. Chem. Soc. 2011, 133, 19338−19341. (25) Edelhoch, H.; Brand, L.; Wilchek, M. Fluorescence Studies with Tryptophyl Peptides. Biochemistry 1967, 6, 547−559. (26) Vivian, J. T.; Callis, P. R. Mechanisms of Tryptophan Fluorescence Shifts in Proteins. Biophys. J. 2001, 80, 2093−2109. (27) Daura, X.; van Gunsteren, W. F.; Mark, a E. Folding-Unfolding Thermodynamics of a Beta-Heptapeptide from Equilibrium Simulations. Proteins: Struct., Funct., Genet. 1999, 34, 269−280. (28) Best, R. B.; Hummer, G. Reaction Coordinates and Rates from Transition Paths. Proc. Natl. Acad. Sci. U. S. A. 2005, 102, 6732−6737. (29) Hummer, G. From Transition Paths to Transition States and Rate Coefficients. J. Chem. Phys. 2004, 120, 516. (30) Arakawa, T.; Timasheff, S. N. The Stabilization of Proteins by Osmolytes. Biophys. J. 1985, 47, 411−414. (31) Harries, D.; Rosgen, J. A Practical Guide on How Osmolytes Modulate Macromolecular Properties. Methods Cell Biol. 2008, 84, 679−735. (32) Street, T. O.; Bolen, D. W.; Rose, G. D. A Molecular Mechanism for Osmolyte-Induced Protein Stability. Proc. Natl. Acad. Sci. U. S. A. 2006, 103, 13997−14002. (33) Auton, M.; Bolen, D. W. Predicting the Energetics of OsmolyteInduced Protein Folding/unfolding. Proc. Natl. Acad. Sci. U. S. A. 2005, 102, 15065−15068. (34) Wang, A. J.; Bolen, D. W. A Naturally Occurring Protective System in Urea-Rich Cells: Mechanism of Osmolyte Protection of Proteins against Urea Denaturation. Biochemistry 1997, 36, 9101− 9108. (35) Piana, S.; Klepeis, J. L.; Shaw, D. E. Assessing the Accuracy of Physical Models Used in Protein-Folding Simulations: Quantitative Evidence from Long Molecular Dynamics Simulations. Curr. Opin. Struct. Biol. 2014, 24, 98−105.
be part of large-scale structural changes and therefore approach the global observables. Various trapped states are a possible structural explanation for the current observations; the existence of a trap could reduce PTP for Q13 and Q32 but not for Q12. Such traps could form and remelt in different temporal sequences, dependent on the detection method used,13 until the whole system snaps together in an event that appears twostate-like when monitored only by one global coordinate.
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ASSOCIATED CONTENT
S Supporting Information *
The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jpclett.6b00582. Experimental and analysis procedures as well as extensive fitting data (PDF)
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AUTHOR INFORMATION
Corresponding Authors
*E-mail:
[email protected] (T.V.P.). *E-mail:
[email protected] (M.G.). Notes
The authors declare no competing financial interest.
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ACKNOWLEDGMENTS We are grateful to D.E. Shaw Research for providing access to the simulation data. We thank R. B. Best for helpful discussions. This work was funded by a grant from the National Institutes of Health, GM 93318-01-08.
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REFERENCES
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