In Vivo Protein Dynamics on the Nanometer Length Scale and

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Letter

In vivo Protein Dynamics on the Nanometer Length Scale and Nanosecond Time Scale Divina B. Anunciado, Vyncent P. Nguyen, Gregory Blake Hurst, Mitchel J Doktycz, Volker S. Urban, Paul Langan, Eugene Mamontov, and Hugh M. O'Neill J. Phys. Chem. Lett., Just Accepted Manuscript • DOI: 10.1021/acs.jpclett.7b00399 • Publication Date (Web): 07 Apr 2017 Downloaded from http://pubs.acs.org on April 13, 2017

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The Journal of Physical Chemistry Letters

In vivo Protein Dynamics on the Nanometer Length Scale and Nanosecond Time Scale Divina B. Anunciado1, Vyncent P. Nyugen1, Gregory B. Hurst2, Mitchel J. Doktycz3, Volker Urban1, Paul Langan1, Eugene Mamontov4*, Hugh O’Neill1,5*

1

Biology and Soft Matter Division, 2Chemical Sciences Division, 3Biosciences Division,

4

Chemical and Engineering Materials Division, Oak Ridge National Laboratory, Oak Ridge,

Tennessee 37831, United States 5

Department of Biochemistry and Cellular and Molecular Biology, University of Tennessee,

Knoxville, Tennessee 37996, United States

*Corresponding authors: Eugene Mamontov, Email: [email protected]. Tel. 865 771 1387 Hugh O’Neill, Email: [email protected]. Tel. 865 574 5283

Abstract Selectively-labeled GroEL protein was produced in living deuterated bacterial cells to enhance its neutron scattering signal above that of the intra-cellular milieu. Quasi-elastic neutron scattering shows that the in-cell diffusion coefficient of GroEL was (4.7 ± 0.3)×10-12 m2/s, a factor of 4 slower than its diffusion coefficient in buffer solution. Internal protein dynamics showed a relaxation time of (65 ± 6) ps, a factor of 2 slower compared to the protein in solution. 1 ACS Paragon Plus Environment


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Comparison to literature suggests that the effective diffusivity of proteins depends on the length and time scale being probed. Retardation of in-cell diffusion compared to the buffer becomes more significant with the increasing probe length scale suggesting that intra-cellular diffusion of biomolecules is non-uniform over the cellular volume. The approach outlined here enables investigation of protein dynamics within living cells to open up new lines of research using “incell neutron scattering” to study the dynamics of complex biomolecular systems.

TOC Graphics

Keywords In vivo dynamics; protein diffusion; quasielastic neutron scattering.

2 ACS Paragon Plus Environment

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Microscopic dynamics of biomolecules underlie macroscopic dynamics and function of biological systems. Studies of component-specific microscopic dynamics in complex systems are challenging even for isolated biomolecules, but much more so for biomolecules in native environments. Thus, many measurements of protein dynamics are done using hydrated powders or, at best, relatively diluted solutions, with protein concentration in the range of 10-100 g/L. Yet most proteins perform their biological functions in a crowded cellular environment, where the concentration of macromolecular species can exceed 400 g/L.1 Molecular crowding affects biological processes such as binding, protein-protein interactions, protein folding, protein aggregation, and protein stability.2-5 Here, we hypothesize and confirm that, even on the local length scale, the intracellular environment has an effect on the microscopic protein dynamics, both global and internal, and, therefore, the function of biomolecules. The choice of an experimental technique suitable for analysis of microscopic dynamics in vivo is not straightforward. For instance, fluorescence-based techniques require an exogenous fluorescent probe that can change the properties of the system under investigation. Stable isotope labeling in cell culture is an alternative approach for selective in vivo incorporation of a label into a biological system. This approach involves metabolic incorporation of a “light” or “heavy” form of a reagent substituted with stable isotopic nuclei such as deuterium (D), carbon-13, or nitrogen15, during cell growth. Stable isotope labeling has been exploited for in-cell studies of biomolecules using nuclear magnetic resonance (NMR) as isotope-labeled NMR-visible biomolecules stand out in an unlabeled NMR inactive background (see Ref6 for a recent review). In a similar way, neutrons can be used to selectively highlight individual components in a complex environment because the neutron scattering cross-sections of hydrogen and deuterium are very different, making it possible to selectively highlight different components within a



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complex system. In particular, quasielastic neutron scattering (QENS) can provide information not only on the temporal, but also spatial, aspects of biomacromolecular dynamics,7-9 which is a unique advantage when compared to other techniques, including NMR. GroEL is considered an archetypal chaperonin in E. coli and is co-expressed from a common operon with its co-chaperonin GroES.10 Both chaperonins are essential in protein folding as demonstrated by mutational studies.11 The holocomplex is composed of 14 GroEL subunits (57 kDa) assembled as two heptameric rings and 7 GroES (10 kDa) subunits assembled as one heptameric ring (9).12 Here, we report on the intra-cellular dynamics of recombinant E. coli GroEL as measured using QENS. The approach used to express hydrogenated GroEL in deuterated E. coli cells was based on a previously reported method for in-cell NMR studies of 15

N-labeled ubiquitin protein, except in the current work the deuterated culture was fed with a

mixture of hydrogenated amino acids.13 The estimated expression level of GroEL was ~40 mg/g cells (wet weight), similar to previously reported values (Figure S1).14 Figure 1 shows the QENS spectra obtained by subtracting the corresponding background signal due to the buffer. For the uninduced cells solvated in D2O, the background scattering signal is from D2O itself. On the other hand, for the cells expressing GroEL, the background scattering signal is from the uninduced cells solvated in D2O. The data in the inset demonstrate how the increasing complexity of the system leads to progressive increase in the scattering signal, enabling background subtraction for each sample. The deuterated cells in the D2O buffer yield additional scattering compared to the D2O buffer itself. In turn, the hydrogenated protein in the deuterated cells yields additional scattering compared to the deuterated cells alone. Importantly, the progressively increasing scattering signal could not be obtained by merely rescaling of either (1) the almost elastic scattering intensity near zero energy transfer, or (2) the


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broad signal. Instead, the addition of the deuterated cells to the D2O buffer yields the scattering signal that is narrower compared to the scattering signal resulting from the addition of the hydrogenated protein to the deuterated cells. This suggests that the cells and the intra-cellular protein yield scattering signals that are substantially different in width, that is, relaxation times.

Figure 1. The residual scattering signal from the uninduced cells in D2O buffer (blue symbols, the buffer signal subtracted) and the protein in the cells (red symbols, the uninduced cells signal subtracted) measured at 297 K. The data have been averaged over all scattering angles (momentum transfers). Inset: raw data sets prior to subtraction from one another. Black: D2O buffer rescaled by a factor of 0.85 to account for the volume fraction occupied by the cells in the buffer. Blue: uninduced cells in the D2O buffer. Red: cells with expressed intra-cellular GroEl-GroES protein in the D2O buffer. Also shown as a dashed line is the spectrometer resolution function obtained by measuring the signal from a sample cooled down to 5 K.



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While Figure 1 shows the data averaged over all scattering directions for the best visualization statistics, our next task on the way to quantitative data analysis is to visualize the residual scattering signals as a function of the scattering momentum transfer, Q. Figure 2 shows the scattering data converted into the dynamic susceptibility by dividing the scattering signal I(Q,E) by the Bose population factor, nb(E) ≈ kBT/E, where kB is Boltzmann’s constant. This representation allows convenient data visualization because the positions of the susceptibility maxima correspond to the characteristic relaxation times (characteristic frequencies or energies) in the sample.

Figure 2. The log-log plot of dynamic susceptibility calculated from the scattering signal, I(Q,E), as χ”(Q,E)=I(Q,E)/nb(E), where nb(E) is Bose population factor. The D2O buffer signal subtracted from the protein in D2O signal and the cells in in D2O signal was rescaled by a factor of 0.85 to account for the excluded volume.


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The top two panels in Figure 2 show the Q-dependence of the residual signals presented in Figure 1 expressed as dynamic susceptibilities. The bottom two panels show the dynamic susceptibilities measured from the reference sample of protein dissolved in the D2O buffer and the buffer itself. Typically, in the range of up to ca. 80 µeV there is either a plateau or a shallow minimum, separating the susceptibility signal at these lower energy transfers from the increase at higher energy transfers, which extends beyond the accessible dynamic range. This separation is least pronounced for the uninduced cells. Thus, the dynamics on the picosecond time scale (a fraction of meV energy scale) is evident in the scattering data, but cannot be analyzed quantitatively because of the limited dynamic range. On the other hand, the susceptibility at lower energy transfers can yield characteristic frequencies (relaxation times) with detailed analysis of the scattering signal. QENS data fits of I(Q,E) involve least squares minimization of the difference between the experiment and the model spectra. The latter are constituted of the model scattering function, S(Q,E), convolved with the instrument resolution function, and a linear background. The parameters and functional dependence of the model scattering function describe the microscopic dynamics of the system under investigation. First, data fits in the -100 µeV to +100 µeV dynamic range were carried out with the reference sample in order to establish the baseline for the protein dynamics, both global and internal, in solution for eventual comparison with the intra-cellular protein dynamics. Prominence of the contribution from the global translational-rotational protein diffusivity in QENS data collected from proteins in solutions is well documented.7, 15-21 Following subtraction of the D2O buffer signal, the residual QENS signal from the solvated protein can be represented



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by the convolution of broadening due to the global protein diffusion (glob) and the internal protein dynamics (int):

S (Q, E ) = S glob (Q, E ) ⊗ Sint (Q, E )

(1)

For the commonly assumed Lorentzian broadening line shape with half-width at half-maximum Γ, which represents simple diffusion processes, this convolution can be calculated analytically to yield a two-component model scattering function:

Γglob (Q)

  Γint (Q ) ⊗  A(Q ) + (1 − A(Q )) = 2 2  π Γglob (Q ) + E π Γint (Q) + E   Γglob (Q) (Γglob (Q ) + Γint (Q )) A(Q) + (1 − A(Q)) 2 2 π Γglob (Q ) + E π (Γglob (Q ) + Γint (Q)) 2 + E 2

S (Q, E ) =

(

(

2

2

)

)

(

(

)

(2)

)

The parameter A(Q), known as elastic incoherent structure factor,9 accounts for the localized character of the internal motions, always approaching unity in the limit of zero Q. There is no corresponding parameter for the protein global motions because they are not localized in space. Thus, the difference between the two broadening parameters obtained in the two-component fit, (Γglob(Q) + Γint(Q)) and Γglob(Q), yields Γint(Q) that describes the internal dynamics, whereas the narrower broadening parameter, Γglob(Q), directly describes the global protein diffusion. As one can see in the top panel of Figure 3, the global diffusion of the protein in the D2O buffer can be well described by a Γglob(Q) = hDQ2 law, with a combined rotational-translational diffusion coefficient D = (17.8 ± 0.8)×10-12 m2/s. The middle panel shows that the internal protein dynamics in the D2O buffer is represented, as expected, by a Q-independent signal, with 8 ACS Paragon Plus Environment

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a characteristic relaxation time of (h/Γint) = (39 ± 9) ps. The bottom panels shows a fit of the A(Q) parameter for the protein in the D2O buffer with a model describing a particle diffusing within a sphere of a radius a: A(Q) = (3j1(Qa)/(Qa))2, where j1 is the spherical Bessel function,9 that yields the effective confinement sphere radius of a = (1.36 ± 0.03) Å.

Figure 3. The fitted parameters of the model scattering function describing the microscopic dynamics. Open symbols: reference sample of protein in the D2O buffer. Filled red symbols: intra-cellular protein. Black cross symbols (middle and bottom panels): an alternative model fit for the intra-cellular protein also described in the text. The displayed lines are global protein diffusion fits with a Γglob(Q) = hDQ2 law, where h is the reduced Planck constant (top panel), a Q-independent fit of the protein internal dynamics (middle panel), and fits of the internal protein dynamics with a “diffusion in a sphere” model described in the text (bottom panel).



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At the next step, we applied the same model scattering function S(Q,E) to fit the QENS data from the intra-cellular protein. As shown in the top panel of Figure 3, its global diffusion could also be described by a Γglob(Q) = hDQ2 law, with a combined rotational-translational diffusion coefficient D = (4.7 ± 0.3)×10-12 m2/s, which is a factor of 4 lower compared to the protein global diffusion coefficient in the D2O buffer. Because of the low diffusivity, the two lowest Q data points for the intracellular protein measurement are not shown as they could not be fitted given the energy resolution of the present experiment. The internal protein dynamics, represented by a Q-independent signal in the middle panel, showed a characteristic relaxation time of (h/Γint) = (65 ± 6) ps, being slower by less than a factor 2 compared to the protein in the D2O buffer. Importantly, as one can see in the bottom panel, the A(Q) does not differ much for the intracellular protein, yielding essentially the same confinement sphere radius of a = (1.28 ± 0.01) Å. That is, the geometry of the localized internal protein motions is not sensitive to the protein environment, whether intra-cellular, or water buffer, even though the characteristic protein relaxations inside the cell become slower by a factor of 4 and 2 for the global and internal dynamics, respectively. We need to consider a possibility that significant crowding effects together with inhomogeneous environments found in the cell may result in the elastic scattering from the immobilized proteins, whereas the proteins that remain mobile on the time scale of the experiment energy resolution may give rise to a non-Lorentzian, “stretched” scattering function. Then, as an alternative to Equation 2, a different fit model may need to be considered:

S (Q, E ) = A(Q) + (1 − A(Q))

1 πE0 (Q)

( E (Q) / E0 (Q)) −α (Q ) cos 1 + 2( E (Q) / E0 (Q))

1−α ( Q )

sin

πα (Q) 2

πα (Q) 2

+ ( E (Q) / E0 (Q))

(3) 2 (1−α ( Q ))


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The geometrical meaning of the parameter A(Q) describing the internal protein motions remains the same as before, but now it represents the spectral weight of not the slow global dynamics component, but the elastic scattering. The second term, modulated by (1-A(Q)), is a Cole-Cole scattering function, commonly used, e.g., in dielectric measurements to account for the “stretched” relaxation character in the frequency space, and more recently employed to describe QENS data.22 Here the parameter E0 is analogous to the half-width at half maximum (HWHM, Γ) of a Lorentzian function, which is the limiting case of Equation 3 when the “stretching” parameter α = 0. The second term in Equation 3 accounts for the empirically observed “stretched” dynamics without differentiating between its possible origins, whether purely internal, or a combination of global and internal. A fit with Equation 3 yields parameters shown as black crosses in Figure 3 (middle and bottom panel). The difference in the fit quality using Equation 2 and Equation 3 is not sufficient to give a preference to either model, but one needs to consider the geometry of the internal protein motions described by the A(Q) parameter as plotted on the bottom panel. The scattering model described by Equation 3 exhibits much steeper Q-dependence and would give the much increased effective confinement sphere radius for the internal protein motions of a = (2.7 ± 0.2) Å. This would be rather counterintuitive in view of the more crowded intra-cellular environment. Thus, the model given by Equation 2 provides a more plausible description of the intra-cellular global and internal protein dynamics. Nevertheless, even the less plausible model given by Equation 3 would lead to qualitatively the same conclusion regarding the effect of the intracellular environment on protein motions, that molecular crowding leads to slowing down of the protein compared to the protein in the D2O buffer alone.



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The fitted global rotational-translational D values could be rescaled by a factor of ca. 0.79 to arrive at the purely translational diffusion coefficients of (14.1 ± 0.6)×10-12 m2/s and (3.7 ± 0.2)×10-12 m2/s for the protein in solution and intra-cellular protein, respectively.15 Our QENS data show retardation factors of ~ 2 and ~ 4 for the most localized internal and the nanometer length-scale diffusion dynamics, respectively. In-cell rotational diffusion of myoglobin and hemoglobin measured using NMR were 1.4 and 2.2 times slower compared to dilute solution23 while in-cell single molecule confocal microscopy studies of protomyosin α showed a 2.5-fold reduction in the intramolecular distance fluctuations compared to in vitro.24 On the other hand, data in the literature report slowing down in vivo by a factor of 10-17 for translational diffusion of GFP in E. coli25-27 and a diffusion coefficient of (0.16 ± 0.15) ×10-12 m2/s for GroEL28 using fluorescence recovery after photobleaching. The disparity in the values obtained using different approaches suggests that slowing down of the protein in the intra-cellular environment compared to the protein in solution depends greatly on the length scale probed. Furthermore, single particle tracking experiments have shown that individual intra-cellular proteins are characterized by nearly diffusive behavior,

29-30

whereas large biomolecular complexes such as mRNA and

protein-mRNA complexes exhibit sub-diffusive long-range mobility.31-32 These studies also support that the character of intra-cellular macromolecular global motion can be dependent on both the probe (that is, monitored particle) size and the time scale of the measurement. 33 It can be inferred that larger biomacromolecules, such as GroEL, whose motion is diffusive on the pico to nanosecond time scale probed by QENS, would likely be sub-diffusive when traced over the longer time and length scales. Therefore, the intracellular diffusivity of biomolecules is nonuniform over the cell dimensions and is affected by factors such as local fluctuations in the viscosity of the cytosol or biomolecular interactions, or a combination of both.


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In conclusion, we employed quasielastic neutron scattering to probe simultaneously the diffusivity and internal dynamics of a protein in bacterial cells. The diffusivity slowed down by a factor of 4 when measured on the nanometer length scale, whereas the internal dynamics slowed down by less than a factor of 2 while fully retaining its spatial characteristics. Comparison with literature confirms the general trend of retardation of in-cell diffusion compared to the buffer becoming more significant with the increasing probe length scale. The approach outlined here enables investigation of global and internal protein dynamics within living cells without the need for bulky fluorescent labels. While QENS has been already used to study both water34-39 and biomolecules8, 40-43 in living cells, the advantages provided by selective isotope labeling in cells as demonstrated in the present work open up new lines of biological research using ‘in-cell QENS’ to extract information on dynamics of biomolecular systems that is unobtainable by other analysis techniques.

Materials and Methods Escherichia coli BL21(DE3), transformed with a plasmid encoding E. coli GroEL, was adapted to grow in D2O Enfors minimal media44 with 0.5% w/v deuterated glycerol as the carbon source. The samples for QENS were prepared by growing a culture to a final OD600nm = 1.0 (~3 x 108 cells/mL) using an Amersham Biosciences Ultrospec 10 cell density meter, followed by transferring the cells to the same media supplemented with 0.5% w/v hydrogenated NZ-amine A (Amresco J853) so the final OD600nm was now ~0.5. The culture was then divided into two equal aliquots. GroEL expression was induced in one aliquot by the addition of isopropylthiogalactoside (IPTG) to 1mM, and incubation for 5 hours at 20oC. The control sample was treated in the same manner except no IPTG was added to the culture.



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SDS-PAGE analysis shows that the only substantial difference in the protein bands in the uninduced and induced cells is the band that corresponds to the over-expressed GroEL protein (See Figure S1). Both cultures were then washed several times with PBS D2O buffer and after the final centrifugation step the excess liquid surrounding the pellet was removed. The cell pellet was transferred to the aluminum sample holders in a glove bag to minimize exposure to moisture in air. Recombinant GroEL was purified from E. coli extracts using a previously described procedure with minor modifications45. We used BASIS neutron backscattering spectrometer46 at the Spallation Neutron Source, ORNL, operating its incident bandwidth selection choppers at 30 Hz with the incident neutron band center at 6.15 Å. With the final detected neutron wavelength fixed at 6.267 Å by Si(111) crystal analyzers, this operation regime provided a range of neutron energy transfers suitable for analysis between -100 µeV and +500 µeV. Each sample was loaded in indium gasket-sealed flat-plate aluminum sample holders (50 x 30 x 0.1 mm) immediately prior to the measurements. All measurements were performed at a temperature of 297 K, except for the last one, which was carried out at the end of the experiment at 5 K to obtain the resolution function. Normalization of the scattering intensities to a flat-plate vanadium standard was applied.

Author Information Corresponding Authors *Email: [email protected] *Email: [email protected].

Notes


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The authors declare no competing financial interests.

Acknowledgement The authors gratefully acknowledge technical assistance provided by Qiu Zhang. This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a nonexclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/ doe-publicaccess-plan). The neutron scattering experiments at Oak Ridge National Laboratory’s (ORNL) Spallation Neutron Source were supported by the Scientific User Facilities Division, Office of Basic Energy Sciences, U.S. Department of Energy (DOE). The authors acknowledge funding from the Adaptive Biosystems Imaging project (ERKP851) and Center for Structural Molecular Biology (Project ERKP291) supported by the Office of Biological and Environmental Research, U.S. DOE.

Supporting Information Available SDS-PAGE analysis of uninduced and induced E. coli cells, circular dichroism spectrum of purified GroEL



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21. Grimaldo, M.; Roosen-Runge, F.; Zhang, F. J.; Seydel, T.; Schreiber, F., Diffusion and Dynamics of gamma-Globulin in Crowded Aqueous Solutions. J Phys Chem B 2014, 118 (25), 7203-7209. 22. Mamontov, E.; O'Neill, H., Microscopic relaxations in a protein sustained down to 160 K in a non-glass forming organic solvent. Biochimica et Biophysica Acta (BBA) - General Subjects 2017, 1861 (1, Part B), 3513-3519. 23. Wang, D.; Kreutzer, U.; Chung, Y.; Jue, T., Myoglobin and hemoglobin rotational diffusion in the cell. Biophysical Journal 1997, 73 (5), 2764-2770. 24. Konig, I.; Zarrine-Afsar, A.; Aznauryan, M.; Soranno, A.; Wunderlich, B.; Dingfelder, F.; Stuber, J. C.; Pluckthun, A.; Nettels, D.; Schuler, B., Single-molecule spectroscopy of protein conformational dynamics in live eukaryotic cells. Nat Meth 2015, 12 (8), 773-779. 25. Nenninger, A.; Mastroianni, G.; Mullineaux, C. W., Size Dependence of Protein Diffusion in the Cytoplasm of Escherichia coli. Journal of Bacteriology 2010, 192 (18), 4535-4540. 26. Elowitz, M. B.; Surette, M. G.; Wolf, P. E.; Stock, J. B.; Leibler, S., Protein mobility in the cytoplasm of Escherichia coli. J Bacteriol 1999, 181 (1), 197-203. 27. Konopka, M. C.; Shkel, I. A.; Cayley, S.; Record, M. T.; Weisshaar, J. C., Crowding and confinement effects on protein diffusion in vivo. J Bacteriol 2006, 188 (17), 6115-23. 28. Charbon, G.; Wang, J.; Brustad, E.; Schultz, P. G.; Horwich, A. L.; Jacobs-Wagner, C.; Chapman, E., Localization of GroEL determined by in vivo incorporation of a fluorescent amino acid. Bioorganic & medicinal chemistry letters 2011, 21 (20), 6067-70. 29. Rowland, D. J.; Tuson, H. H.; Biteen, J. S., Resolving Fast, Confined Diffusion in Bacteria with Image Correlation Spectroscopy. Biophys J 2016, 110 (10), 2241-51. 30. Lill, Y.; Kaserer, W. A.; Newton, S. M.; Lill, M.; Klebba, P. E.; Ritchie, K., Single-molecule study of molecular mobility in the cytoplasm of Escherichia coli. Physical Review E 2012, 86 (2), 021907. 31. Golding, I.; Cox, E. C., Physical nature of bacterial cytoplasm. Phys Rev Lett 2006, 96 (9). 32. Lampo, T. J.; Stylianidou, S.; Backlund, M. P.; Wiggins, P. A.; Spakowitz, A. J., Cytoplasmic RNAProtein Particles Exhibit Non-Gaussian Subdiffusive Behavior. Biophysical Journal 2017, 112 (3), 532-542. 33. Parry, B. R.; Surovtsev, I. V.; Cabeen, M. T.; O'Hem, C. S.; Dufresne, E. R.; Jacobs-Wagner, C., The Bacterial Cytoplasm Has Glass-like Properties and Is Fluidized by Metabolic Activity. Cell 2014, 156 (1-2), 183-194. 34. Jasnin, M.; Moulin, M.; Haertlein, M.; Zaccai, G.; Tehei, M., Down to atomic-scale intracellular water dynamics. EMBO Rep 2008, 9 (6), 543-7. 35. Stadler, A. M.; Embs, J. P.; Digel, I.; Artmann, G. M.; Unruh, T.; Buldt, G.; Zaccai, G., Cytoplasmic water and hydration layer dynamics in human red blood cells. J Am Chem Soc 2008, 130 (50), 16852-3. 36. Frolich, A.; Gabel, F.; Jasnin, M.; Lehnert, U.; Oesterhelt, D.; Stadler, A. M.; Tehei, M.; Weik, M.; Wood, K.; Zaccai, G., From shell to cell: neutron scattering studies of biological water dynamics and coupling to activity. Faraday Discuss 2009, 141, 117-30; dsicussion 175-207. 37. Jasnin, M.; Stadler, A.; Tehei, M.; Zaccai, G., Specific cellular water dynamics observed in vivo by neutron scattering and NMR. Phys Chem Chem Phys 2010, 12 (35), 10154-60. 38. Natali, F.; Dolce, C.; Peters, J.; Gerelli, Y.; Stelletta, C.; Leduc, G., Water Dynamics in Neural Tissue. J Phys Soc Jpn 2013, 82. 39. Liberton, M.; Page, L. E.; O'Dell, W. B.; O'Neill, H.; Mamontov, E.; Urban, V. S.; Pakrasi, H. B., Organization and flexibility of cyanobacterial thylakoid membranes examined by neutron scattering. J Biol Chem 2013, 288 (5), 3632-40. 40. Stadler, A. M.; Digel, I.; Artmann, G. M.; Embs, J. P.; Zaccai, G.; Buldt, G., Hemoglobin dynamics in red blood cells: correlation to body temperature. Biophys J 2008, 95 (11), 5449-61. 41. Marty, V.; Jasnin, M.; Fabiani, E.; Vauclare, P.; Gabel, F.; Trapp, M.; Peters, J.; Zaccai, G.; Franzetti, B., Neutron scattering: a tool to detect in vivo thermal stress effects at the molecular dynamics level in micro-organisms. J R Soc Interface 2013, 10 (82). 17 ACS Paragon Plus Environment


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Figure 1 215x279mm (150 x 150 DPI)

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In Vivo Protein Dynamics on the Nanometer Length Scale and

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