This protocol demonstrates how to obtain a low-resolution ab initio model and structural details of a detergent-solubilized membrane protein in solution using small-angle neutron scattering with contrast-matching of the detergent.
The biological small-angle neutron scattering instrument at the High-Flux Isotope Reactor of Oak Ridge National Laboratory is dedicated to the investigation of biological materials, biofuel processing, and bio-inspired materials covering nanometer to micrometer length scales. The methods presented here for investigating physical properties (i.e., size and shape) of membrane proteins (here, MmIAP, an intramembrane aspartyl protease from Methanoculleus marisnigri) in solutions of micelle-forming detergents are well-suited for this small-angle neutron scattering instrument, among others. Other biophysical characterization techniques are hindered by their inability to address the detergent contributions in a protein-detergent complex structure. Additionally, access to the Bio-Deuteration Lab provides unique capabilities for preparing large-scale cultivations and expressing deuterium-labeled proteins for enhanced scattering signal from the protein. While this technique does not provide structural details at high-resolution, the structural knowledge gap for membrane proteins contains many addressable areas of research without requiring near-atomic resolution. For example, these areas include determination of oligomeric states, complex formation, conformational changes during perturbation, and folding/unfolding events. These investigations can be readily accomplished through applications of this method.
Membrane proteins are encoded by an estimated 30% of all genes1 and represent a strong majority of targets for modern medicinal drugs.2 These proteins perform a wide array of vital cellular functions,3 but despite their abundance and importance — only represent about 1% of total structures deposited in the Research Collaboratory for Structural Bioinformatics (RCSB) Protein Data Bank.4 Due to their partially hydrophobic nature, structural determination of membrane-bound proteins has been exceedingly challenging.5,6,7
As many biophysical techniques require monodisperse particles in solution for measurement, isolating membrane proteins from native membranes and stabilizing these proteins in a soluble mimic of the native membranes has been an active area of research in recent decades.8,9,10 These investigations have led to the development of many novel amphiphilic assemblies to solubilize membrane proteins, such as nanodiscs,11,12,13 bicelles,14,15 and amphipols.16,17 However, the use of detergent micelles remains one of the most common and straightforward approaches for satisfying the solubility requirements of a given protein.18,19,20,21,22,23,24,25 Unfortunately, no single detergent or magic mixture of detergents currently exists that satisfies all membrane proteins; thus, these conditions must be empirically screened for the unique requirements of each protein.26,27
Detergents self-assemble in solution above their critical micelle concentration to form aggregate structures called micelles. Micelles are composed of many detergent monomers (typically ranging from 20-200) with hydrophobic alkyl chains forming a micelle core and hydrophilic head groups arranged in a micelle shell layer facing the aqueous solvent. The behavior of detergents and micelle formation has been classically described by Charles Tanford in The Hydrophobic Effect,28 and sizes and shapes of micelles from commonly used detergents in membrane protein studies have been characterized using small-angle scattering.29,30 Detergent organization about membrane proteins has also been studied, and the formation of protein-detergent complexes (PDCs) is expected with detergent molecules surrounding the protein in an arrangement that resembles the neat detergent micelles.31
One added advantage in using detergents is that the resulting micelle properties can be manipulated by incorporating other detergents. Many detergents exhibit ideal mixing, and select properties of mixed micelles may even be predicted from the components and ratio of mixing.22 However, the presence of detergent can still present challenges for biophysical characterizations by contributing to the overall signal. For example, with X-ray and light scattering techniques, signal from detergent in the PDC is practically indistinguishable from protein.32 Investigations with single-particle cryo-electron microscopy (cryo-EM) typically rely on trapped (frozen) particles; structural details of the protein are still obscured by certain detergents or a high concentration of detergent which adds to the background.33 Alternate approaches toward interpreting the full PDC structure (including the detergent) have been made through computational methods which seek to reconstruct the detergent around a given membrane protein.34
For the case of neutron scattering, the core-shell arrangement of detergent in the micelle produces a form factor which contributes to the observed scattering. Fortunately, solution components can be altered such that they do not contribute to the net observed scattering. This "contrast matching" process is achieved by substituting deuterium for hydrogen to achieve a scattering length density that matches that of the background (buffer). A judicious choice of detergent (with available deuterated counterparts) and their ratio of mixing must be considered. For detergent micelles, this substitution can be performed using a detergent with the same head group but having a deuterated alkyl chain (d-tail instead of h-tail). Since the detergents are well-mixed,35 their aggregates will have a scattering length density that is the mole-fraction weighted average of the two components (h-tails and d-tails). When this average contrast is consistent with that of the head group, the uniform aggregate structures can be fully matched to remove all contributions to observed scattering.
We present here a protocol to manipulate the neutron contrast of detergent micelles by incorporating chemically identical detergent molecules with deuterium-labeled alkyl chains.19,36,37 This permits complete simultaneous contrast matching of micelle core and shell, which is a unique capability of neutron scattering.35,38 With this significantly refined level of detail, contrast matching can enable otherwise unfeasible studies of membrane protein structures. Additionally, this contrast-matching approach could be extended to other systems involving detergent, such as polymer exchange reactions39 and oil-water dispersants,40 or even other solubilizing agents, such as bicelles,41 nanodiscs,42 or block copolymers.43 A similar approach as outlined in this manuscript, but employing a single detergent species with partial deuterium substitutions on the alkyl chain and/or head group, was recently published.37 While this can be expected to improve the random distribution of hydrogen and deuterium throughout the detergent compared to the approach presented here, the limited number of available positions on the detergent for substitution and two-step detergent synthesis required poses additional challenges for consideration.
Steps 1 and 2 of the protocol detailed below often overlap since initial experiment planning must be done to submit a quality proposal. However, proposal submission is considered here as the first step to emphasize that this process should be started well in advance of a neutron experiment. It should also be noted that a prerequisite step, which should be demonstrated by the proposal, is to have biochemical and physical characterization (including purity and stability) of the sample supporting the need for neutron studies. A general discussion of small-angle neutron scattering (SANS) is beyond the scope of this article. A brief but thorough introduction is available in the reference work Characterization of Materials by Kaufmann,44 and a comprehensive textbook focused on biological small-angle solution scattering has recently been published.45 Further recommended reading is given in the Discussion section. Small angle scattering uses the so called scattering vector Q as the central quantity that describes the scattering process. This article uses the widely accepted definition Q = 4π sin(θ)/λ, where θ is half the angle between incoming and scattered beam and λ is the wavelength of the neutron radiation in Angstroms. Other definitions exist that use different symbols such as 's' for the scattering vector, and that may differ by a factor 2π or by using nanometers in place of Angstrom (see also discussion of Figure 10).
1. Prepare and Submit a Neutron Facility Beam Time and Instrument Proposal
2. Determine Neutron Contrast Match Points and Necessary Contrast for Protein Measurement
3. Express and Purify the Membrane Protein of Interest
4. Make Final Preparations for Beam Time and Collect SANS Data
5. Reduce SANS Data from 2D Image to 1D Plot
6. Analyze Data for Structural Parameters of the Scattering Particle
7. Create Ab Initio Models from the SANS Data.
NOTE: DAMMIF76 and DAMMIN77 within the ATSAS software suite is used to reconstruct dummy atom models (DAMs) using a simulated annealing process from the GNOM output, which contains P(r) data, or information about the probability or frequency of interatomic distances within the scattering particle. These programs may be run in batch mode or on the ATSAS-Online web server.
A beam time and instrument proposal should clearly convey all information needed to the review committee so that a valid assessment of the proposed experiment can be made. Communication with an NSS is highly suggested for inexperienced users. The NSS can assess initial feasibility and guide proposal submission to emphasize feasibility, safety, and the potential for high-impact science. The information provided in the proposal should include background information and context for the significance of the research; knowledge that is expected to be gained and how this impacts the current understanding in the related field of science; a description of the work, samples, methods, and procedures that will be employed; and, if applicable, previous productivity of the team at the facility, including relevant publications and results. Useful resources, such as proposal templates and tips for preparing the proposal, are available to Users via the Neutron Science User Portal (neutrons.ornl.gov/users).
Experiment planning is a dynamic process that often begins during the initial stages of proposal submission, but may not be fully conceived until just prior to the experiment. However, keep in mind that any changes that deviate significantly from the description in the proposal (including changes to buffer conditions or sample composition) must be approved by the facility prior to the start of the experiment.
This protocol assumes that a method for expressing and purifying the membrane protein of interest into detergent micelles in solution has been successfully demonstrated. In this case, the membrane protein of interest is an intramembrane aspartyl protease (IAP), which has an established purification protocol and has been previously determined to be soluble and active in buffer containing DDM micelles.
Here, we demonstrate neutron contrast calculations using the MULCh: Contrast module for a solution of IAP protein in contrast-matched mixed DDM / d25-DDM micelles. The strategy outlined herein was to first determine the degree of mixing between the two detergents necessary to achieve a complete contrast match of the micelle. The end result was to determine the relative contrast of each component and the background (H2O/D2O ratio) necessary to contrast-match the scattering from detergent in order to observe only the protein scattering.
Figure 1 demonstrates a proper input for the MULCh Contrast module and the resulting output for contrast calculations of the DDM detergent head group and tail as subunits 1 and 2, respectively. The formula used for the head group is C12H14X7O11 with a volume of 348 Å3, and the alkyl chain tail formula is given as C12H25 with a volume of 350 Å3.
It is apparent from the Contrast module output that the DDM head group and tail have different scattering length densities, and thus contrast between the two components will be observed. For DDM, the head groups have a CMP in 49% D2O while the alkyl chain tails have a CMP in 2% D2O, with their average CMP occurring in 22% D2O. Therefore, the aim of the next step will be to design a mixed micelle that incorporates deuterium-substituted alkyl chains to increase the average CMP of the detergent tails to match the CMP of the head groups. The contrast calculation was repeated for a substituted detergent, DDM with a fully-deuterated tail (d25-DDM), which similarly results in contrast between the head group and tail. However, contrast values of the h-tails and d-tails are significantly different. Recall that detergent mixtures are known to produce well-mixed micelles in solution,22 thus a blend of these h- and d-tails in the micelle core should produce an average SLD equal to that of the head group shell, yielding a micelle with single CMP. Since the head group is common to both DDM and d25-DDM, the strategy is to find a mixture of these detergents that produces an average contrast value from the mixed tails that matches the contrast of the head groups.
The target average CMP for the detergent tails is that of the maltoside head groups, or 49% D2O. To estimate the ratio of mixing the average CMP of each h-tail and d-tail component must be known. These values for some common detergents and commercially-available deuterium labelled counterparts are provided in Table 1. Using these CMP values for DDM and d25-DDM, the mole fraction of h-tails and d-tails that satisfies the equation provided in step 2.2 is χd-tails = 0.43.
Figure 2 demonstrates a more advanced input to the MULCh Contrast module that can be used to confirm the final mixed-micelle contrast match conditions and determine the degree of deuteration necessary on the protein. Here, subunit 1 refers to the contrast-matched mixed micelle with 2 components, DDM and d25-DDM, while subunit 2 refers to the M. marisnigri JR1 intramembrane aspartyl protease (MmIAP) given by its amino acid sequence. The SLD of the protein has been elevated by expression in deuterated growth media to yield a CMP for the protein in buffer containing ~92% D2O. The degree of separation between the protein's match point in 92% D2O and the measurement condition (48.5% D2O) suggests that sufficient scattering signal will be obtained from the protein.
Production of d-MmIAP was carried out with Rosetta 2 E. coli cells harboring the pET22b-MmIAP vector. Minimal medium with unlabeled glycerol in 90% D2O was selected as the growth medium. After adaptation to 90% D2O minimal medium, the culture volume was scaled up to 400 mL and used to inoculate 3.6 L of fresh 90% D2O minimal medium in a 5.5 L bioreactor vessel.
Figure 3 provides a trace of the process values during the fed-batch cultivation. At the time of inoculation, the temperature set point was 30 °C, the dissolved oxygen (DO) set point was 100%, the agitation set point was 200 rpm, and the flow rate of compressed air was 4 L/min. The pH was held above a set point of 6.9 using a 10% w/v solution of sodium hydroxide in 90% D2O. Once the DO spike signaled depletion of the initial 5 g/L glycerol, feeding(30% w/v glycerol, 0.2% MgSO4 in 90% D2O) was initiated, which continued throughout the cultivation. After around 7 hours of feeding, the culture temperature was reduced to 18 °C and isopropyl-β-D-1-thiogalactopyranoside was added to a final concentration of 1 mM. Upon harvesting the culture, approximately 145 g wet weight of cell paste was collected via centrifugation (6,000 x g for 45 min)
Purification of d-MmIAP proceeded as for the protonated enzyme except that enzyme yield was significantly lower and final purity was somewhat lower than typical. From 5 L, ~20 g of wet membranes was isolated, all of which was solubilized in DDM for purification and loaded onto the first Ni2+ affinity column. To maximize purification yield, the flow-through fraction was diluted and re-rerun on Ni2+ affinity column. The procedure was repeated a third time before polishing the concentrated d-MmIAP sample by size exclusion chromatography (Figure 4).
Figure 5 depicts a quartz sample cell and its related sample changer setup. Sample environments are managed by the Bio-SANS operations team and NSS. A variety of different environments can be arranged to perform measurements with temperature control, humidity control, mechanical tumbling, high temperature, and sustained pressure, among others.
Figure 6 demonstrates Bio-SANS operations and execution of the automated table scans. The table scan is configured to be intuitive and user-friendly. On the first tab (Load Samples), information is provided about the sample changer and setup, such as identification of samples at each position in the sample changer, cell thicknesses, and sample type (open beam, sample, background, etc.). Additional metadata tags can be applied here also. On the next tab (Plan Experiment), the instrument configuration and any associated parameters (such as temperature control) are defined. This step is arranged by the NSS at the start of the experiment. The user must simply input the measurement times for each sample (in seconds). The final tab (Execute Scans) provides a summary of the table scan data and allows the automated scan to be executed.
After the 2D scattering images are recorded, this data must be reduced to a 1D plot of the intensity versus Q – a function of the scattering angle. During data reduction, image corrections such as pixel sensitivity masks and dark background subtractions are also applied. MantidPlot software was designed to interpret the raw Bio-SANS instrument data. The NSS will generally assist in reducing and retrieving the final data at the end of the experiment.
Figure 7 provides an overview of the remote analysis cluster access to MantidPlot and the Script Window used for data reduction. Raw data are accessible at the remote analysis cluster (analysis.sns.gov) via UCAMS/XCAMS user authentication. After logging into the remote desktop, the MantidPlot software can be launched from a terminal window by simply executing 'MantidPlot' under any path. Open the Script Window in MantidPlot and edit the User Reduction Script as directed by the NSS. Executing this script will generate the reduced data files, which can then be transferred to a local machine for analysis using secure FTP.
Figure 8 demonstrates plotting and viewing the data after executing the User Reduction Script. Reduced data will appear in the Workspaces window. By default, final merged data will have the suffix _f appended. Right-click will allow Plot Spectrum with Errors to be selected and data to be plotted. Display preferences are accessed by selecting Preferences from the 'View' menu bar. Formatting of axes and plotting range is easily accessible by double-clicking the axes. Scattering profiles of buffers, which contain no scattering particles of significant size should appear as a relatively flat line (constant intensity). For samples, intensity should be observed at lower scattering angles (Q) with exponential decay to a flat incoherent background.
Figure 9 provides an overview of proper buffer subtraction using the SAS Data Analysis (or primusqt) software package following data reduction. Quite often the incoherent background (intensity at high-Q) is slightly mismatched between the sample and buffer. For proper subtraction, data in this region should overlap. The scale correction applies an intensity scale factor to the data which results in overlapping data, and the subtraction can be performed. If the scale factor results in buffer data points with larger intensity than corresponding points in the sample, then these points will be negative and not rendered in a log plot. If negative values in the buffer-subtracted file are abundant, make a minor reduction in the buffer scale factor and perform the subtraction again.
Figure 10 provides example usages of the Primus Guinier and Distance Distribution Wizards. A Guinier analysis provides a preliminary estimate of the particles radius of gyration from the low-angle scattering data. As data approach zero, a linear region should be present in the data. Fitting of a line in this region allows the Rg to be determined from the slope and an I0 to be extrapolated from the intensity intercept at Q=0. An upward trend in the data as Q approaches zero indicates aggregation in the sample, while downward trends are usually indicative of interparticle repulsions. These trends are most apparent when the distribution of residuals is non-stochastic. The upper limit of the Guinier fit for globular particles is constrained by Q*Rg < 1.3 ('s' is used in place of Q in the ATSAS software).
The Rg determined for d-MmIAP from this Guinier fit was 15.4 ± 1.1 Å with an estimated I0 given as 0.580 ± 0.001.
The distance distribution wizard allows systematic changes to be made to parameters of the P(r) fit. The P(r) curve is an indirect Fourier transform of the curve fit to the experimental data. Therefore, it is essential to verify that the fit in the left panel accurately reflects the experimental data. For the fit shown in this example, a Dmax of 46 Å was obtained.
Figure 11 illustrates common errors in Dmax selection. A Dmax that is artificially large often causes P(r) values to become negative as the P(r) function oscillates about the x-axis. A Dmax that is artificially small leads to a truncated P(r) curve with an abrupt transition to Rmax=0.
The first steps in the Primus Shape Wizard are identical to the Guinier and Distance Distribution Wizards. Once this information has been provided to obtain good fits to the experimental data, the ab initio model setup is configured.
Figure 12 illustrates a proper input for the Primus Shape Wizard. Here, experimental SANS data for the IAP was provided with a scale in Angstroms, and the expected file prefix of "SANSEnvelope" was given. A set of 17 initial dummy atom models were requested to be generated using "fast" model mode with no symmetry (P1) applied and no anisometry selected. The set of 17 models will be aligned, averaged, and filtered with DAMAVER, and the average model refined using DAMMIN. Inspection of the prefix-damsel.log text document provides a summary of the selection criterion and any models discarded from the set. The final refined model was written as SANSEnvelope-1.pdb.
The program SUPCOMB is used to perform a superimposition of the SANS envelope model with the high-resolution model, with only the two PDB structure files as input. By default, "r" is appended to the reoriented and superimposed file name.
Once the SANS envelope and high-resolution structure have been superimposed, these models can be visualized using any molecular graphics viewing program. The results from PyMOL are suitable for publication quality images and can be used to emphasize biophysical results relating to the structural investigation. Here, we demonstrate a few basic PyMOL operations used to provide 3D representations and views of the resulting superimposed structures.
Figure 13 provides an overview of the PyMOL visualization process. Once the PDB structure files have been opened in PyMOL, the models should be visible in the PyMOL Viewer window. Change the representations of each model using the 'S' button next to each file name. Use a surface representation for the SANS envelope and a cartoon representation of the protein backbone from the high-resolution model. Select a suitable color scheme from options available under the 'C' button. For protein chains, a "chainbow" coloring provides a color gradient from N- to C- terminus to aid interpretation. Transparency is applied to the surface representation to allow better visualization of the protein structure within the envelope. For publication images, a white background is recommended. Once these visualization queues have been applied, the 3D structures can be examined. Manipulations of the perspective and molecule rotation/translation can be performed by clicking and dragging the structure.
Figure 1. Usage of the MULCh Contrast module to determine contrast parameters for components of dodecyl maltoside (DDM). Input values are shown in the screen on the left with the subsequent output to the right. The Contrast module input page is accessed by clicking 'Contrast' (green circle) from the navigation menu on the left side of each screen. Input areas for the project title, buffer components, and subunits 1 and 2 are labelled as such. Output pages contain important particle information and contrast properties for the described system. Relevant tables and calculated match points have been boxed in orange for clarity. Please click here to view a larger version of this figure.
Figure 2. Usage of the MULCh Contrast module to determine contrast parameters for components of the membrane protein-detergent complex. In this instance, input has been given to describe the overall PDC system in buffered solution. Subunit 1 describes the contrast-matched mixed micelle composed of DDM with 43% by mole as d25-DDM. Subunit 2 describes the membrane protein, with the amino acid sequence for MmIAP input as the formula. The deuteration level (green circle) describes the protein's degree of deuterium substitution, and has a direct impact on the SLD and calculated match-point that will be estimated for the protein. Results (orange box) indicate that match-point for the mixed detergent will occur in 48.5% D2O, while the protein's match-point occurs at ~90% D2O. Please click here to view a larger version of this figure.
Figure 3. Sample preparation – Bioreactor trace. The process values displayed are temperature set point (orange line), dissolved oxygen (DO) set point (blue line), agitation set point (red line), and compressed air flow rate (pink line). Pump 1 (green line) was used for pH control. The DO spike at 18:40 signaled depletion of the intial glycerol and was used to initiate feeding of glycerol solution via pump 2 (black line). X-axis denotes hours. Please click here to view a larger version of this figure.
Figure 4. Sample preparation – FPLC and SDS-PAGE characterization of the sample. Final size exclusion chromatogram for d-MmIAP equilibrated with 20 mM HEPES, pH 7.5, 250 mM NaCl, 48.5% D2O and 0.05% total DDM, of which 44% (w/v) is tail-deuterated d25-DDM. Inset: SDS-PAGE analysis with Coomassie staining. Pooled fractions are labeled A, B, and C. Region "B" was used in the SANS experiment. Annotated molecular weight marker is in kDa. Image reproduced with permission from Naing et al.65 Please click here to view a larger version of this figure.
Figure 5. Data collection – Quartz banjo sample cell and autosampler setup. (A) An empty quartz banjo cell is shown resting against the autosampler block. Cells are filled with sample and inserted into one of the 15 available positions. (B) The sample block is mounted behind an aperture selector (not shown) between the Beam Tube Extender and Silicon window at the entrance to the detector tank. Hoses connecting to a temperature-controlled water bath and channels throughout the sample block provide temperature regulation at the sample positions. The arrow indicates the direction of the neutron beam. Please click here to view a larger version of this figure.
Figure 6. Data Collection – Overview of SPICE operations and table scan. The table scan operations are accessible from the 'Table Scan' button on the far left menu of the SPICE instrument control software. Green arrows denote the active tab at the top of the screen and orange boxes highlight areas in the table for active user inputs. (A) The first tab of the table scan is used to provide information about the sample changer and sample cell positions. Provide labels, sample thicknesses, and sample types for each position used in the sample changer. Checking the 'Do Scan' box will add the corresponding row to the table scan queue. (B) On the second tab, details about the instrument configuration and measurement time for each sample (in seconds) are recorded. Instrument configurations are preconfigured by the Neutron Scattering Scientist and provided to users based on details provided in the beam time and instrument proposal. Additional parameters can be added to the instrument control, such as the ability to define temperature setpoints and hold times, throughout the measurement queue. (C) The final tab provides an overview of the measurements to be made for each row in the table. If no changes are needed, the automated scan is initiated by clicking the 'Execute Scans' button. Please click here to view a larger version of this figure.
Figure 7. Data Reduction – Overview of reduction script and MantidPlot operations. MantidPlot software is accessible on the analysis cluster by typing "mantidplot" in a terminal command prompt at any active directory (the yellow circle and arrow are added for emphasis). Once the software is open, access the script window (toggled by button marked in green) and open the script provided by the Neutron Scattering Scientist. Follow the instructions provided in the script, which should only require the scan numbers for each sample and buffer to be entered in a list for automated reduction, as well as scan numbers for the empty cell for subtraction and empty beam for transmission and beam center determination. Please click here to view a larger version of this figure.
Figure 8. Data Analysis – Plotting of data using MantidPlot. Data are referenced as 'Workspaces' in MantidPlot. The workspace area will be populated with filenames as produced by the user reduction script. Workspace data for 1D data sets can be plotted by right-clicking the workspace and selecting "Plot Spectrum with Errors…". Additional data can be added to the current plot by simply dragging and dropping workspaces. Formatting of the plot window (such as for log-log plots) can be performed by selecting 'Preferences' from the 'View' menu bar, or double-clicking on the axis labels. Please click here to view a larger version of this figure.
Figure 9. Data Analysis – ATSAS software: basic operations and buffer subtraction. The primusqt application (SAS Data Analysis) provides visualization for proper background (buffer) subtraction. The buffer file should be scaled for subtraction such that data overlap at high-Q (where scattering is flat as a result of remaining signal from incoherent background). When this high-Q range of data is selected, the Scale operation will apply a scale factor to the lower data file in the table that produces overlap in the defined region. After scaling, the buffer file can be subtracted to yield the net scattering profile of the particle. Please click here to view a larger version of this figure.
Figure 10. Data Analysis – Guinier and P(r) (distance distribution) determination. (A) The Primus Guinier Wizard is used to perform a Guinier analysis, providing an initial estimate of I0 and Rg. The particle type and range of data to fit are used to define the fit. A plot of the fit and corresponding residuals are shown to the left, while Guinier terms (I0 and Rg), Q*Rg limits, and quality of linear fit are provided as output in the area marked by the orange box. (B) The Primus Distance Distribution Wizard is used to perform the distance distribution analysis, providing the P(r) curve which defines the probability of interatomic distances within the scattering particle and includes the Dmax or maximum interatomic distance. Parameters indicated by the orange box can be systematically investigated to determine a proper distance distribution function. Please click here to view a larger version of this figure.
Figure 11. Improper results from the distance distribution analysis. A properly selected Dmax should produce a P(r) curve that peaks with a gradual decay to zero. Dmax values that are too large will likely produce negative P(r) values or oscillations near the x-axis at high values of r. Dmax values that are artificially small often result in P(r) curves where the upper bound appears truncated. Please click here to view a larger version of this figure.
Figure 12. Modeling – Ab initio model setup using the Primus Shape Wizard. Ab initio modeling parameters are provided in a single input window. A prefix is supplied for output file names with other options for processing available. Annealing procedure can be fast (bigger beads with faster cooling) or slow (smaller beads with slower cooling). Number of repetitions should be of sufficient size to examine reproducibility of model features. Particle symmetry and anisometry can be provided, if known. Angular scale should correspond to the units of the measured data. DAMAVER averaging will align and average all dummy atom models, and then apply a selection criterion which excludes any outlier models. A core of fixed atoms from the averaged model will be further refined using DAMMIN if this option is selected. This refined model should represent the 3D low-resolution envelope of the experimental SANS profile. Please click here to view a larger version of this figure.
Figure 13. Visualization – Experimental SANS envelope and overlay with high-resolution model using PyMOL. After opening the PDB structure files in PyMOL, models appear in the PyMOL Viewer. Active models are listed in the table to the right, along with action buttons which can be used to manipulate the model and its representation. Basic operations are provided for visualizing these models which allow regions of agreement (or mismatch) between the SANS envelope and high-resolution structure to be identified. Please click here to view a larger version of this figure.
Table 1. Physical properties of detergents commonly used in membrane protein investigations. Please click here to download this file.
Structural biology researchers take advantage of complementary structural techniques like solution scattering to obtain biochemical and structural details (such as overall size and shape) from biomolecules in solution. SANS is a particularly attractive technique for determining low resolution structures of membrane proteins, a core focus of modern structural biology and biochemistry. SANS requires quantities of purified proteins comparable to those of crystallographic trials (1 mg/sample). The recently expanding commercial availability of high-purity deuterated detergents relevant to membrane protein studies makes accessible a means to manipulate this hydrogen/deuterium content for SANS experiments of membrane protein-detergent complexes, allowing the protein signal to be recorded directly. When successful, an ab initio model molecular envelope can be calculated from data collected over just several hours. Thus, membrane protein biochemists, biophysicists, and structural biologists can readily take advantage of SANS to obtain coveted initial 3D models of membrane proteins in solution, structures in complex with binding partners or substrates, and models obtained by SANS can be used for phasing diffraction data. Routine usage of SANS to characterize membrane proteins would be transformative for the membrane protein biochemistry field and have ripple effects from basic structure function to drug discovery and development. The added advantage of neutron contrast matching with deuterium substitution makes SANS a valuable technique for studying protein-protein, protein-DNA, or other biomolecular complexes, which can be readily manipulated in their hydrogen/deuterium content.
For additional details about small-angle scattering instrument design and theory, the following reviews are recommended: The Bio-SANS instrument at the High Flux Isotope Reactor of Oak Ridge National Laboratory,79 Small-angle scattering for structural biology – expanding the frontier while avoiding the pitfalls,72 and Small-angle scattering studies of biological macromolecules in solution.80 The Neutron Scattering Scientist can also discuss current instrument configurations and the optimal parameters for a given system. For instance, data at lower Q (which is a function of the scattering angle and neutron wavelength) is generally desired for larger systems. The most common instrument configuration at Bio-SANS provides a Qmin of 0.003 Å-1 and is suitable for larger protein complexes up to hundreds of kDa. A solution containing ~3 mg mL-1 of membrane protein of approximate size of 30 kDa (monomer) in contrast-matched micelles with 48.5% D2O in solution requires a typical measurement time around 8 hours each for sample, buffer, and empty cell (24 hours = 1day total). Instrument measurement times at a given contrast condition can be approximately scaled according to the product of the scattering particle's molecular mass and concentration. However, scattering particles must be measured under non-interacting conditions, including any non-specific protein aggregation, which places a practical upper limit on the concentration of the sample. Hour-long measurements place limits on the stability of certain proteins. Fortunately, SANS measurements can be done routinely at refrigerated temperatures to improve protein life time, and the employed beam of low-energy neutrons does not cause any radiation damage during the measurement.
An overview of this process and determination of many key factors toward the successful recording of the neutron scattering from a sample measured at the contrast match point of the detergent are presented in this manuscript. This includes the critical step toward obtaining a complete match of the aggregate detergent by designing a detergent mixture with a uniform neutron contrast between the detergent head group and alkyl chain components. Measurements at this single detergent match point provide a scattering profile attributed to only the membrane protein of interest and allowed an ab initio envelope representing the membrane protein to be reconstructed from the data. The data analysis and ab initio modeling protocols also demonstrate the potential information obtained from such investigations, which could aim to address overall structure, conformational changes, oligomeric states, among others. One limitation is that to date only very few detergents are commercially available with deuterium substitutions.19
While perdeuteration may not be necessary, the aim in this case should be to achieve a protein with >65% deuterium labeling in the protein. Alternatively, if detergent with selectively-deuterated head groups and tails is available, and the CMP of the detergent micelle occurs near 100% D2O, then sufficient contrast from the protein can be achieved without the need for deuterium labeling. Determine the appropriate deuteration level of the protein to achieve sufficient scattering when measured at the contrast-match point of the detergent.There are numerous challenges associated with membrane protein expression and purification,81 which remain beyond the scope of this article. Unfortunately, high levels of deuteration are currently not (yet) possible for eukaryotic systems. While we realize this is a limitation for some eukaryotic proteins, most membrane proteins have bacterial orthologs for which this method is suitable.
The authors have nothing to disclose.
The Office of Biological and Environmental Research supported research at ORNL's Center for Structural Molecular Biology (CSMB) and Bio-SANS using facilities supported by the Scientific User Facilities Division, Office of Basic Energy Sciences, US Department of Energy. Structural work on membrane proteins in the Lieberman lab has been supported by NIH (DK091357, GM095638) and NSF (0845445).
Amicon Ultra MWCO 50KDa concentrator | EMD Millipore | UFC905096 | labware |
Ammonium citrate dibasic | Fisher Scientific | A663 | medium component |
Ammonium sulfate | EMD Millipore | 2150 | medium component |
Bioflo 310 Bioreactor System | Eppendorf | M1287-2110 | equipment |
Calcium chloride dihydrate | Acros | 423525000 | medium component |
Carbenicillin | IBI Scientific | IB02025 | antibiotic |
Chloramphenicol | EMD Millipore | 3130 | antibiotic |
Cobalt (II) chloride | Acros | AC21413-0050 | medium component |
Copper (II) sulfate | Acros | AC19771-1000 | medium component |
Deuterium oxide | Sigma-Aldrich | 756822 | medium component |
Drierite Gas Purifier | W.A. Hammond Drierite Co. Ltd. | 27068 | |
EDTA, disodium, dihydrate | EMD Millipore | 4010 | medium component |
Emulsiflex-C3 | Avestin | EF-C3 | equipment |
Äkta Purifier UPC100 | GE Healthcare | equipment | |
Glycerol | Sigma-Aldrich | G5516 | medium component |
HEPES | Sigma-Aldrich | H4034 | |
HiPrep 16/60 Sephacryl S-300 HR column | GE Healthcare | 17116701 | |
Imidazole | VWR | 97064-622 | |
IPTG | Teknova | I3325 | |
Iron(III) chloride hexahydrate | MP Biochemicals | ICN19404590 | medium component |
LB Agar Miller | Fisher Scientific | BP1425-2 | |
Magnesium sulfate heptahydrate | VWR | 97062-134 | medium component |
Manganese(II) sulfate monohydrate | Acros | AC20590-5000 | medium component |
MaxQ 6000 Incubated/Refrigerated Shaker | Thermo Scientific | SHKE6000-7 | equipment |
n-Dodecyl-d25-β-D-maltopyranoside | Anatrace | D310T | |
n-Dodecyl-β-D-maltopyranoside | Anatrace | D310A | |
Potassium phosphate monobasic | VWR | 97062-346 | medium component |
RC 6 Plus Centrifuge | Thermo Scientific Sorvall | 46910 | equipment |
SIGMAFAST protease inhibitor cocktail tablets, EDTA-free | Sigma-Aldrich | S8830 | |
Sodium chloride | Sigma-Aldrich | S3014 | |
Sodium hydroxide | Sigma-Aldrich | 795429 | |
Sodium phosphate dibasic | Sigma-Aldrich | S7907 | medium component |
Sterile 25mm syringe filter with 0.2µm PES membrane | VWR | 28145-501 | labware |
Sterile disposable bottle top filter with 0.2µm PES membrane | Thermo Scientific | 596-4520 | labware |
Superdex 200 10/300 GL | GE Healthcare | 17517501 | |
Superose-12 10/300 GL column | GE Healthcare | 17517301 | |
Ultrospec 10 Cell Density Meter | GE Healthcare | 80211630 | equipment |
Zinc sulfate monohydrate | Acros | AC38980-2500 | medium component |