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Ultrafast Fluctuations of High Amplitude Electric Fields in Lipid Membranes Paul Stevenson, and Andrei Tokmakoff J. Am. Chem. Soc., Just Accepted Manuscript • DOI: 10.1021/jacs.6b12412 • Publication Date (Web): 09 Mar 2017 Downloaded from http://pubs.acs.org on March 9, 2017
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Paul Stevensona,b and Andrei Tokmakoffb*. a
Department of Chemistry, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA, 02139 USA b
Department of Chemistry, James Frank Institute, and The Institute for Biophysical Dynamics, The University of Chicago, 929 E 57th Street, Chicago IL, 60637 USA ABSTRACT: Understanding electrostatics of lipid membranes at a molecular level has the potential to offer great insight into the mechanism of voltage-gated processes, but has been challenging to study experimentally. In this study, we characterize the equilibrium electric field fluctuations at the interfacial region of lipid bilayers by using a combination of ultrafast time-resolved infrared spectroscopy, molecular dynamics (MD) simulations and spectral modelling. By monitoring the dynamics of the ester carbonyl stretching vibration in hydrated phosphocholine lipid bilayers, we are able to measure a correlation function for the femtosecond and picosecond fluctuations in the local electric field of the membrane, estimating the standard deviation in these fluctuations as ~10MV/cm. The addition of gramicidin D at a 1:20 mole ratio with DMPC results in the formation of protein-lipid hydrogen bonds, which alter the dynamics of the ester groups. Using MD simulations, we conclude that ultrafast local field fluctuations exist whether or not water interacts with the ester groups, however, water does accelerate the time-scale of these fluctuations.
INTRODUCTION The function of integral membrane proteins is influenced by their dynamic interactions with their lipid environment1–4. For example, the membrane fluidity of the lipid environment surrounding ATPase modulates the enzymatic activity of this protein5. Similarly, it has been found that the presence of cholesterol in lipid bilayers, and the effect it has on lateral diffusion of lipids, modulates the timescales of the rhodopsin photocycle6. In these examples, lipid bilayers provide a bath of fluctuating forces, which in turn influence protein conformation and function. These forces originate in a hierarchy of coupled dynamics that operate on varying length scales7 and over many decades in time, from ultrafast short-range head group fluctuations and lipid chain isomerization to slower diffusive motions and capillary waves that modulate membrane thickness and curvature. The forces that act on membrane proteins arise, directly or indirectly, from electric fields. They originate in the motion of charged groups and induced dipoles
within this heterogeneous and anisotropic electrostatic environment, and are of particular importance in understanding the mechanisms of voltage gated processes8. The functional role of different tiers of dynamics in biological systems has been considered for decades in the context of protein conformational dynamics 9– 12. When fast and slow dynamics are coupled to each other13, sub-nanosecond fluctuations, though much faster than typical functional timescales, may prove critical for determining the outcome of biological processes. Enzymatic catalysis is a notable (and controversial) example in which the specific nature of fast conformational fluctuations may dictate enzymatic catalysis rates14,15. Specifically, the picosecond dynamics of the P450cam enzyme with different substrates correlated well with functional behavior of the system16. The regioselectivity of the enzyme was found to decrease as the picosecond dynamics became faster17. Conduction of ions through ion channels is another case where fluctuations of the protein are functionally essential18. Similarly, simulations have demonstrated
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that a microsecond conformational change between two helical structures can be regulated by the picosecond fluctuations in hydrogen bond strength in the system11. A more general argument for the importance of sub-nanosecond membrane fluctuations is their role in the microscopic friction experienced by nanoscopic objects within the membrane. Diffusive motion and activated kinetics in a particular region of space will depend on the magnitude and correlation times of the fluctuating electric fields generated by lipids, protein, and water. In this way, the kinetics of short-range membrane-associated processes are intimately tied to the fast dynamics of the membrane. Further support for this perspective requires experimental studies of fast membrane dynamics. Although small angle x-ray or neutron scattering and NMR/ESR experiments can access fast time-scales, Experimental observation of electric field fluctuations in membrane proteins is a challenging proposition. Infrared (IR) absorption spectroscopy can be used to interrogate the electrostatic environments in the membrane19, as the frequencies of functional groups in lipid membranes (such as ester carbonyls) are sensitive to the local electric field. Vibrational Stark spectroscopy experiments20,21 have determined quantitative values for the shifts in carbonyl vibrational frequencies in response to the applied electric field, finding values of η = ~1 cm-1/(MV/cm). Quantitative relationships between electric field and vibrational frequency have been put to extensive use in IR spectroscopy of amide I vibrations in proteins, where experimental spectra can be linked directly to structure and dynamics in MD simulations. However, it is only with the advent of ultrafast time-resolved spectroscopies that direct experimental insight into the fluctuations of these environments has been possible. A combined experimental and simulation approach offers the potential to understand both the timescales and magnitudes of fluctuating electric fields in membranes, which motivates the present study. To distinguish different electrostatic environments and their dynamics, we turn to femtosecond two-dimensional infrared spectroscopy (2D IR). 2D IR tracks time-dependent changes in the vibrational frequency and amplitude of bond vibrations, allowing one to target local regions of large molecules without the need to tag the sample with an exogenous label. Tracking the evolution of these intrinsic labels on an ultrafast timescale can reveal intricate details about the nature of the interactions within the system, and has been utilized to study a diverse range of problems, spanning liquid
water dynamics, membrane protein structure and protein-ligand interactions22–25. The time-dependence of the carbonyl stretch lineshapes in 2D IR characterizes the frequencyfrequency correlation function (FFCF) for vibrational frequency fluctuations about their equilibrium value, C (t )(0) 26. Since vibrational frequency shifts are proportional to changes in local electric fields, E , this experiment is effectively a measurement of the time-correlation function for the fluctuations of electric fields about their average: CEE E (t ) E (0) , or equivalently the spectrum of ultrafast electrical fluctuating forces. With these force correlations, the microscopic friction in the system can be determined through the fluctuationdissipation theorem.
Figure 1 - Gramicidin D in a lipid bilayer, with ester carbonyls highlighted as small spheres, and two Tryptophan sidechains shown as large spheres. The structure of DMPC is shown in the inset. Bottom right shows the frequency shift from a test charge of +1e as a function of distance using the spectral map discussed in the text.
Here, we combine 2D IR with MD simulations to resolve the amplitude and timescales of electrostatic fluctuations proximate to the ester carbonyls in a model phospholipid bilayer. IR and 2D IR spectra are used to identify hydrogen bonding configurations to the lipid ester carbonyl and the time-dependence of 2D IR lineshapes quantify the fluctuations in vibrational frequency and electric field experienced by these environments. Furthermore, we study how
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these fluctuating fields are modified by the presence of a protein in the bilayer after introducing the ion channel Gramicidin D (shown in Figure 1). Gramicidin D (gD) acts as a non-specific defect in the membrane, but also offers the ability to look at specific hydrogen bonds between sidechains and lipid ester groups. MD simulations provide the molecular basis for interpreting the observed fluctuating fields. MATERIALS AND METHODS Sample Preparation 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC, Anatrace Inc) was dissolved in chloroform (Sigma-Aldrich) at a concentration of 10mM. For samples containing Gramicidin D (Sigma-Aldrich), an appropriate amount was added to give a final concentration of 0.5mM. Before addition, gD was first dissolved in EtOD (Cambridge Isotopes) to exchange labile NH groups for ND groups, then lyophilized overnight to remove solvent. This yields a 20:1 lipid-to-protein ratio, which minimizes the fraction of DMPC molecules not in contact with gD without forming the hexagonal lipid phase known to occur at high gD density27.
above the gel-fluid phase transition of DMPC31. This temperature was chosen to ensure both samples are completely in the fluid phase, since Gramicidin D has been observed to broaden the gel-fluid coexistence range of DMPC31. The instrument and methods used for 2D IR data acquisition has been described in detail elsewhere 32,33 and in the SI. For our experiments, all data was collected with perpendicular (ZZYY) polarization geometry in a temperature-controlled cell held at 313K. 2D IR data was acquired for waiting times from 0.15-10ps. Molecular Dynamics and Spectral Simulations Since gD is a mixture of Gramicidins A, B and C, we selected the dominant species of the mixture, Gramicidin A, for simulation. (For ease of comparison with experiment, we will always refer to the species being studied as gD, even though the simulation is not performed for the mixture). Coordinates were taken from the solution NMR structure34 (PDB 1JNO)
The lipid-chloroform solution (100μL) was dried on a CaF2 window. This sample was then placed under vacuum for 24 hours to remove any residual solvent. A small amount (~500nL) of D2O (Cambridge Isotopes) was pipetted on to the sample, to a water-to-lipid ratio of 10:1. It is necessary to use D2O rather than H2O because of the absorption of the H2O bending mode at 1650 cm-1. This sample was heated to above the phase transition (Tm=24°C, sample held at 40°C) and the procedure outlined in Ref 28 was used to prepare aligned bilayer stacks. More detail on these structures is given in the SI. Briefly, the lipid bilayers were sandwiched between two CaF2 windows at 40°C, and a mechanical shearing force was applied by slowly rotating the upper window. Aligned layers of bilayers were necessary to minimize signal from scattered light in the sample. Using the extinction coefficients for bulk D2O29, and assuming the ester group extinction coefficient is similar to that of the amide I vibration30, our FTIR spectra are consistent with a 10:1 water-lipid ratio. IR Spectroscopy FTIR spectra were collected on a FTIR spectrometer (Tensor-27, Bruker), with 128 averages per spectrum against a background of dry air. The low water content of the samples removes the need for further background subtraction. For FTIR and 2D IR measurements, the sample was placed in a temperature-controlled jacket at 313K, which is 16K
Figure 2 - FTIR spectra of the Amide and Ester carbonyls stretches of DMPC with (red) and without Gramicidin D (black). The enlarged region shows the redshift on addition of Gramicidin D. The second derivatives of the spectra are shown in dotted lines, highlighting the underlying two-peak structure. The amide I region spans ~1600cm-1 to 1700cm-1, the ester region spans ~1700cm-1 to 1770cm-1.
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Initial bilayer assemblies were generated using the CHARMM-GUI35–38, with 64 DMPC lipid molecules per leaflet. A six-stage equilibration procedure outlined in Ref 37 was used, for a total equilibration time of 375ps. Production MD simulations were run for 10ns in an NPT ensemble with the Nose-Hoover thermostat with separate temperature coupling groups for the protein, lipid and solvent. The last 5ns of this run were used for analysis. All stages were run at 313K, using the CHARMM36 forcefield.
projects along the C=O bond axis. In addition to explaining the sensitivity of the C=O vibrational frequency to hydrogen bonding, this finding is the key to developing interpretive computational spectroscopy tools that relate IR spectra to local molecular structure using MD simulations. Here we leverage the extensive literature on amide I vibrational spectroscopy to extend our computational analysis with MD simulations to lipid ester carbonyl stretches.
RESULTS AND DISCUSSION FTIR and 2D IR Spectra of Lipids Although FTIR and 2D IR spectra of the DMPC bilayers contain a wealth of information, it is contained within a congested, spectral region (spanning 3ps, though it is not possible from the spectrum alone to determine which process is responsible. Replacing 5% of the DMPC lipids with sphingomyelin introduces a spectrally distinct amide carbonyl stretch vibration (~1660 cm-1) to the system, which can be used to test for VET. Figure 5 shows the 2D IR spectra of the sphingomyelin-doped bilayers. At T=150 fs, a pair of amide resonances at 1648 and 1665 cm-1 also indicate distinct carbonyl environments for sphingomyelin with and without a hydrogen bond to the amide group. A crosspeak is clearly visible for the longest waiting times, demonstrating that VET occurs between different lipid groups, although considerably slower than the vibrational frequency fluctuations. A fuller discussion is given in the SI. This is in contrast to intermolecular cross-peaks between amide and ester at short T observed for peptides associated with or folded within lipids23,52.The existence of VET pathways does not exclude hydrogen bond switching as a factor. However, experiments on small esters in methanol have estimated hydrogen bond switching timescales to be ~18ps53, which is significantly greater than the time window of our experiments. A Heuristic Spectral Model for Lipid Bilayers To extract quantitative dynamical information about both peaks A and B, we model the timeevolution of our experimental 2D IR spectra using an established model for two fluctuating coupled
Figure 5 - 2D IR spectra of a 20:1 DMPC:Sphingomyelin bilayer at early and late waiting times
In fitting, we focused on the FFCF parameters, which are key to understanding the electric field dynamics of the system. Our metrics for successful reproduction of the spectra require that we capture the transition of the lineshape from diagonally elongated to rounded, but asymmetric in ω1; the time-dependent frequency shifts; and the effect of adding gD on the CLS decay. As a starting point for fit optimization, we assume the vibrational relaxation dynamics are similar to those of N-methylacetamide (NMA), a single amide I oscillator, whose dynamics have been studied with 2D IR in a number of solvents. A bi-exponential decay of the FFCF with a 56 fs timescale and a second timescale of ~1ps was found for NMA in D2O54. These processes are commonly interpreted as arising from water librational motions and hydrogen bond reorganization, respectively. A biexponential form
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was assumed for all FFCFs, and the timescales varied to optimize agreement between experiment and simulation. To incorporate the effects of VET and/or chemical exchange, a crosspeak with timedependent intensity was added, as outlined in Ref 32. The model and experimental data are compared in Figure 6. ω/ δ/
cm-1
cm-1
μ/μ0 Δ1 /
cm-1
Peak B
Peak A
Peak A (+ gD)
1743
1728
1732
20
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τ1 / fs
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Δ2 / cm-1
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τVET / ps
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strength to match experiment, it is possible to replicate the slowdown of the CLS decay observed experimentally. Experimental values for the vibrational Stark shift of carbonyls range from 0.5-1.5cm-1/(MV/cm)47,55, which combined with our FFCF amplitudes allows us to estimate the standard deviation of the electric field fluctuations as ~10MV/cm. These results are in excellent agreement with the vibrational Stark shift studies using unnatural amino acids as local probes19,56. Typical values of electric fields across biological lipid membranes are
