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Exploring the structure of biological macromolecules in solution using Quokka, the small angle neutron scattering instrument, at ANSTO
Nuclear Instruments and Methods in Physics Research A 798 (2015) 44–51
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Nuclear Instruments and Methods in Physics Research A journal homepage: www.elsevier.com/locate/nima
Exploring the structure of biological macromolecules in solution using Quokka, the small angle neutron scattering instrument, at ANSTO Kathleen Wood a,n, Cy M. Jeffries a,1, Robert B. Knott a, Anna Sokolova a, David A. Jacques b, Anthony P. Duff a a b
Australian Nuclear Science and Technology Organisation, New Illawarra Rd, Lucas Heights, NSW 2234, Australia MRC Laboratory of Molecular Biology, Cambridge, United Kingdom
art ic l e i nf o
a b s t r a c t
Article history: Received 29 March 2015 Received in revised form 17 June 2015 Accepted 23 June 2015 Available online 30 June 2015
Small angle neutron scattering (SANS) is widely used to extract structural parameters, shape and other types of information from a vast array of materials. The technique is applied to biological macromolecules and their complexes in solution to reveal information often not accessible by other techniques. SANS measurements on biomolecules present some particular challenges however, one of which is suitable instrumentation. This review details SANS experiments performed on two wellcharacterised globular proteins (lysozyme and glucose isomerase) using Quokka, the recently commissioned SANS instrument at the Australian Nuclear Science and Technology Organisation (ANSTO). The instrument configuration as well as data collection and reduction strategies for biological investigations are discussed and act as a general reference for structural biologists who use the instrument. Both model independent analysis of the two proteins and ab initio modelling illustrate that Quokka-SANS data can be used to successfully model the overall shapes of proteins in solution, providing a benchmark for users. & 2015 Published by Elsevier B.V.
Keywords: Small angle neutron scattering Biological structure Protein Macromolecule
1. Introduction Small angle scattering (SAS) offers unique opportunities to investigate the shapes, conformations and interactions between, and within, higher-order macromolecular complexes in solution and how these vary when altering sample environments. The general principles of SAS are well established and there are a number of excellent reviews and texts available on the subject [1–7]. Small angle X-ray scattering (SAXS) is increasingly used by structural biologists to determine the global shapes and organisation of macromolecules [8] and outstanding results are being achieved (e.g. [9]). Small angle neutron scattering (SANS) can reveal additional information not accessible by SAXS based on the very different way neutrons coherently scatter from ‘light’ hydrogen (H) compared to its ‘heavy’ counterpart deuterium (D). Importantly, by altering the ratio of H:D in either the buffer, or by investigating complexes consisting of components with different average numbers of H per unit volume (e.g., protein–nucleic acid and protein–lipid complexes, or D-labelled protein bound to H-protein) it becomes possible to ‘match-in’ and
Abbreviations: SAS, small angle scattering; SAXS, small angle X-ray scattering; GI, glucose isomerase; SANS, small angle neutron scattering; MW, molecular weight n Corresponding author. Tel.: þ61 6 9717 7100. E-mail address: [email protected] (K. Wood). 1 Present address: European Molecular Biology Laboratory, Hamburg, Germany. http://dx.doi.org/10.1016/j.nima.2015.06.034 0168-9002/& 2015 Published by Elsevier B.V.
‘match-out’ coherent neutron scattering contributions from the data. In other words, when applied to investigating macromolecular complexes consisting of components with different average neutron scattering length densities (ρ), SANS makes it possible to extract distance and shape information from a whole complex and, importantly, the shapes and dispositions of individual components within the complex. For biological macromolecules in solution, the isolation of component scattering functions using SANS is usually achieved by altering the neutron scattering length density of the buffer, i.e., substituting H2O with D2O, which changes the neutron contrast, or Δρ, between the solvent and the target macromolecule in the sample. SANS experiments are also advantageous in terms of radiation damage, which is nonexistent for most biological samples placed in a neutron beam. Neutron fluxes are typically several orders of magnitude less than typical X-ray sources. The relatively low flux is partially compensated for by larger beams, therefore larger samples, longer exposure times and increased sample concentration. Experiments must therefore be performed on an instrument with sufficient flux. Here we provide a framework for biological SANS experiments using the Quokka instrument at ANSTO and demonstrate its suitability for structural biology experiments. A comprehensive study of a novel biological complex using SANS generally involves ‘matching out’ a component as described above. The experiments reported in this review do not include such multi-component experiments but rather demonstrate the capabilities of Quokka applied to single-component
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biological samples. Using two standard globular proteins as test cases that mimic a typical size range investigated using SANS – lysozyme (14 kDa) and glucose isomerase (GI: 170 kDa) [10–12] – we benchmark the instrument, quantity of sample and exposure times necessary to obtain quality data from biological samples.
2. Methods 2.1. Sample preparation Lysozyme powder (USB Corporation) was resuspended in 10 ml of 150 mM NaCl, 40 mM NaCH3COO, pH 3.8 (sodium acetate buffer) in H2O to a final concentration of 8 mg ml 1 and filtered (0.22 μm). Individual 500 μL aliquots were dialysed overnight at room temperature against 100 ml of either 100% v/v H2O sodium acetate buffer (pH 3.8) or sodium acetate buffer made up in 100% v/v D2O, pD 3.8. The final concentration of the H2O and 2D2O lysozyme samples post-dialysis were 7.86 and 7.78 mg ml 1, respectively, as determined at Abs280 nm using an extinction coefficient (E0.1%) of 2.7 L.g 1 cm 1 [13]. Glucose isomerase (GI) (Hampton Research HR7-100) was supplied as a crystalline suspension. The crystals were dissolved in 350 μl of 200 mM Na2SO4, 50 mM K2SO4, 1 mM MgSO4, 50 mM HEPES, pH/ pD 8.0 (HEPES buffer) in either 100% v/v H2O, or 100% v/v D2O and
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dialysed overnight against the respective H2O or D2O HEPES buffer. The final concentrations used for the SANS experiments were 1.85 mg ml 1 and 1.61 mg ml 1 for the H2O–GI and D2O–GI, samples (determined at Abs280 nm using an E0.1% of 1.1 L.g 1 cm 1). A dilution series of GI (from 3 to 0.1 mg mL 1) was also prepared using the matched solvent post-dialysis buffers as diluent. Note that for the described sample preparation conditions, the proteins were known to be monodispersed. For SANS data acquisition, all samples and their respective matched solvents (300 μl each) were mounted in 1 mm pathlength quartz cuvettes (Hellma) and placed into a multi-position sample holder at room temperature located at the Quokka SANS beam line. Temperature variation over the duration of the experiment was less than 72 1C. 2.2. Instrument setup The Quokka-SANS instrument at ANSTO is a 40 m continuous instrument employing 20 m variable collimation, with a 20 m variable sample to detector distance. Typically called a pinhole instrument, a circular aperture at the end of the guides defines the source size and various aperture sizes are selected at the sample position. The maximum accessible Q-range spans three orders of magnitude, from 0.0007 to 0.7 Å 1 (where Q¼4πθ⧸λ, θ is the scattering angle and λ is the wavelength of the incident radiation). The incident neutron wavelength, λ, and wavelength resolution, Δλ/λ are defined by a
Table 1 Details of configurations used for the measurements. A 12.5 mm diameter sample aperture was used for all configurations.
a
Source-sample distance [m] Sample-detector distance [m] Detector offset [m]b Beamstop diameter [mm] Qmin o Qo Qmax [Å 1] H2O buffer count ratec [kHz] D2O buffer count rate [kHz] Empty cell count rate [kHz] Time measured [min]
Low-Q configuration
Medium-Q configuration
High-Q configuration
10 10 0 110 0.0078o Qo 0.08 1 0.2 0.1 100
4 4 0 110 0.023 o Qo 0.2 34 6.6 4.7 20
10 2 0.3 44 0.023 oQ o 0.5 21 3.6 1.7 30
a Due to count rate limitations on the detector, the high Q configuration was measured with a larger source to sample distance than strictly necessary, which allowed the use of a smaller beamstop and hence increased the accessible Q-range. b The 1 m 1 m detector currently used on Quokka can be offset from the central position to enable access to higher Q range. c Count rates are approximate only, but give an estimate of expected rates as an experimental check.
Table 2 Sample characteristics and parameters obtained from I(Q).
Concentration [mg/mL] Neutron contrasta (Δρ) [1010 cm 2] Calculated MW b [kDa] Dry volumec [Å3] Partial specific volumec[mL/g] Envelope diameter (CRYSON) Rg [Å] – Guinier analysis I(0) [cm 1] – Guinier analysis MW estimated from data [kDa] ‘Quality’ of data – AutoRg [25] Dmax – Autoporod [27] [Å] Rg [Å] – p(r) analysis I(0) [cm 1] – p(r) analysis Porod volume [ 10 3 Å3][25]
Lysozyme D2O
Lysozyme H2O
GI D2O
GI H2O
7.78 2.917 14.5 17,570 0.740 51 12.8 7 0.4 (0.2 o QRg o 0.8) 0.089 7 0.001 14.0 96% 43 12.6 7 0.1 (0.013o Q o0.35 Å) 0.0889 7 0.0004 12.9 7 0.7 (0.013o Qo 0.18 Å)
7.86 2.499 14.3 17,570 0.740 51 14.4 7 0.8 (0.2o QRg o1.0) 0.068 7 0.001 15.0 58% 46 14.7 7 0.4 (0.013o Qo 0.30 Å) 0.066 7 0.001 18.6 70.7 (0.013o Qo 0.16 Å)
1.61 3.436 173.8 211,063 0.738 102 31.17 0.2 (0.4 o QRg o 1.3) 0.298 70.001 172.9 84% 104 31.17 0.2 (0.013o Qo 0.23 Å) 0.299 70.001 1877 10 (0.013o Qo 0.07 Å)
1.87 2.458 172.8 211,063 0.738 102 32.9 7 0.9 (0.4 oQRg o 1.3) 0.1877 0.003 172.3 91% 95 31.7 7 0.5 (0.013o Qo 0.20 Å) 0.1847 0.002 220 7 6 (0.013o Qo 0.07 Å)
In all data sets, data points up to Q ¼0.013 Å were excluded from the analysis. a b c
Calculated using MULCh [29]. Calculated from sequence assuming 62% of exchangeable hydrogen atoms actually exchange with buffer for GI, 85% for lysozyme [30], D2O at 98% using MULCh [29]. Calculated from sequence using NucProt calculator [31]. PDB files 193L and 1OAD were used. 2GVE used for calculating exchangeable hydrogen atoms for GI.
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rotating mechanical velocity selector and were set at 5 Å and 10%, respectively to maximise the flux at the sample position. More details on instrument hardware are provided in [14]. Depending on the required Q-range the sample to detector distance and wavelength are varied. The Q-range measured should (i) encompass the maximum intra-particle distance (Dmax) which defines Qmin and (ii) extend to high-Q (Qmax) for accurate background subtraction. Typically, it is necessary to measure samples in several instrument configurations and merge the data from different detector distances to cover the full Q-range. More detail on how to select the desired Q-range is given in Supplementary information S1. For the experiments reported here, the parameters of the different instrument configurations are summarised in Table 1. The GI and lysozyme protein samples were measured at both lowQ (100 min) and high-Q (30 min) configurations. Sample transmissions were determined at 10 m for two minutes per sample using the attenuated direct beam.
– Data were put on an absolute intensity scale using the attenuated empty beam measurement to determine a normalisation factor for one instrument configuration. The data were then radially averaged to produce onedimensional profiles of scattered intensity, I(Q), as a function of Q. Errors were propagated in the reduction process. More details are given in the Supplementary information S2. As can be seen in Table 2, there is significant overlap in Q between low- and high-Q configurations and data form the two detector positions were merged into a single data set using the ‘sort’ option in the Igor Pro macros. Merging involved deleting the data points with large statistical errors at high-Q and the points covered by the beam stop. The high-Q configuration was scaled to the low-Q configuration. The resulting instrument independent 1D scattering profiles from the protein and buffer were then subtracted as described in Supplementary information S2.
3. Results and discussion 2.3. Data processing 3.1. Dilution series Data were reduced using macros for Igor Pro (WaveMetrics Inc) developed at NIST with Quokka specific modifications [15]. Details of the reduction procedures can be found in the associated help file and on the NIST website [16]. For the data presented here, the isotropic two-dimensional data were corrected for instrument effects in the following steps: – Empty cell scattering was subtracted from both buffer and sample. – The detector sensitivity was corrected by normalising the data to a uniformly flat scatterer (a sheet of Plexiglass). Data from the flat scatterer is generally only recorded once per reactor cycle, to ensure that no changes to the detector response are observed.
The inherent low flux of neutron sources is compensated for in several ways, including larger beam sizes (therefore larger samples) and increased macromolecular concentrations. If samples remain monodisperse, without interparticle interference effects, and sample availability is not an issue, SANS experiments are often performed at relatively high sample concentrations (5– 10 mg mL 1). In order to give users some idea of the limits in terms of concentrations for biological experiments on Quokka, a dilution series was performed on GI in H2O and D2O. The protein solutions were measured at a single medium-Q configuration only for 20 min (see Table 1) and the reduced data presented in Fig. 1. Note that all samples were measured in 1 mm pathlength cells, which in the case of D2O samples may be further increased to
Fig. 1. Glucose isomerase at various concentrations at sample to detector distance of 4 m. Sample, buffer and empty cell were all measured for 20 min each. 300 μL of solution are loaded in each case. Traces are offset for clarity.
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Fig. 2. Lysozyme in D2O (blue in all panels) and H2O (green in all panels). (A) Experimental data with fits to PDB model structure 193L [19] using CRYSON [21] and fit from which p(r) is calculated. (B) Guinier plot from which radius of gyration and molecular weight are calculated (see Table 2). (C) Atom-pair distance distribution in the scattering particle calculated from I(Q) [25]. (D) Kratky plot, showing the folded nature of the protein in both D2O and H2O (tending to zero at high Q). Traces offset for clarity in (A) and (B) (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).
2 mm if required. As can be seen in Fig. 1, data remain a reasonable quality down to low protein concentrations in D2O. Clearly, the necessary data quality depends on the information to be extracted from the scattering, and the data shown in the figure give the reader an estimation of necessary concentration and count times. For a protein of high molecular weight like GI, a reliable Rg can be obtained from the data shown in Fig. 1 at concentrations down to 0.5 mg/mL in both D2O and H2O, while full modelling may require higher concentration solutions. Dividing the highest concentrated data set by the medium one reveals a flat line, as would be expected from the dilution. When the data from the weakest concentration sample is divided by the others however, this is not the case. Even if the data were measured a much longer time to improve the error on the counting statistics, it would appear that other sources of error are visible at 0.1 mg/mL, possibly due to background subtraction from the weak scatterer. 3.2. Experimental data and model-independent analysis Lysozyme and GI data measured on Quokka across the full (i.e., merged) Q-range and associated plots are presented in Figs. 2 and 3, respectively. In our initial analysis, the guidelines for evaluation and presentation of small angle scattering data set out in [18] were adopted. The theoretical background to the model independent analysis of data is set out in Supplementary information S3.
In panel A of Figs. 2 and 3, the measured data is plotted as the measured neutron intensity I(Q) versus Q. The increased variance and errors in the scattering data for the proteins measured in H2O compared to D2O are caused by a combination of a lower absolute magnitude in the coherent neutron scattering contrast in the H2O buffer (Table 2), and, more significantly, an increase in incoherent scattering from H when H2O is used as a buffer. Scattering profiles were calculated from high-resolution structures determined by Xray crystallography [19,20] using CRYSON [21] and are presented in panel A of Figs. 2 and 3. As can be seen, the calculated scattering profiles agree well with data measured from both D2O and H2O samples for GI and lysozyme, indicating that the shape of the proteins in solution is the same as their structure in single crystals – as has been observed previously [22,23]. In panel B of Figs. 2 and 3, Guinier representation of SANS data (lnI(Q) versus Q2) are presented [24]. Guinier plots, when displayed at very low Q, give a first qualitative indication of sample quality [18,22]. Often deviations from linearity in the Gunier region indicate complex formation, aggregation or interparticle interference [22]. All four protein samples show a linear Guinier region indicating sample monodispersity. The radii of gyration (Rg) of the scattering particles, proportional to the slope at low Q in the Guinier plots, are presented in Table 2. The Guinier fits were performed using PRIMUS [25], and the upper limit of the Q-range (QmaxRg o1.3) chosen manually. An automatic Guinier fit was also
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Fig. 3. Glucose isomerase in D2O (blue in all panels) and H2O (green in all panels). (A) Experimental data with fits to PDB model structure 1OAD [20] using CRYSON [21] and fit from which p(r) is calculated. (B) Guinier plot from which radius of gyration and molecular weight are calculated (see Table 2). (C) Atom-pair distance distribution in the scattering particle calculated from I(Q) [25]. (D) Kratky plot, showing the folded nature of the protein in both D2O and H2O (tending to zero at high Q). Traces offset for clarity in (A) and (B) (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).
performed using AutoRg [25] from the ATSAS package and found not to differ substantially from that determined manually. For both proteins, the determined Rg in D2O is approximately 1.5 Å smaller than that in H2O buffer. The difference can be explained by differences in the scattering length density of the hydration layer around the proteins [21]. The lysozyme Rg reported here agree within error to previously published values [21], where values of 12.4 70.2 Å and 13.8 7 0.2 Å were reported for lysozyme in D2O and H2O, respectively. AutoRg also gives a quality assessment of the data, which is reported in Table 2. AutoRg determined the data quality to be above 80% for three of the samples, with a significantly lower quality for lysozyme in H2O. Lower data quality for lysozyme in H2O is also apparent by inspection (Fig. 2), which is due to three sample parameters: small size of the particle, high incoherent background from the H2O buffer and lower contrast between protein and buffer. The intercept of the Guinier plot extrapolated to zero-angle (I (0)) can be used to calculate the molecular weight (MW) of the scattering particle if the concentration of the solution, contrast and partial specific volume of the particle are all known [26] (see S3 for method). The calculation of MW is an important check of sample quality. Parameters used to calculate MW from the Guinier analysis as well as the extracted MW are reported in Table 2. All
MW obtained from the experimental data agree within 5% with the calculated values determined from the amino acid sequences of the proteins. The MW calculated from I(0) have several sources of error: the absolute intensity from the instrument is determined to within 2%, the error on the concentration determination is estimated to be 10%. In addition the calculated value is strongly dependent on the calculated neutron contrast, and in the current study correctly taking into account exchangeable hydrogen atoms was found to be crucial to correctly assessing MW. The molecular weight estimates from I(0) and concentration confirm that, as expected, lysozyme is monomeric in solution, while GI is a monodisperse tetramer, in agreement with the standard molecular weight values for these proteins. The atom-pair distance distribution (p(r)) in real space can be calculated via an indirect Fourier transformation of the I(Q) data [7]. The p(r) represents the distribution of distances between scattering centres within the particle and are plotted for all for samples in panels C of Figs. 2 and 3, (calculated using GNOM [17]). The maximum particle dimension (Dmax) is an imposed constraint in the calculation of the p(r), and was estimated using the AutoPorod [27] package, which evaluates the p(r) for several Dmax and selects the most suitable Dmax based on the shape of the resulting p(r) as described in [27]. The p(r) plotted in Fig. 2 for GI indicates a spherical particle with a distribution of distances symmetric around the maximum value at Dmax/2. The p(r)
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Fig. 4. Lysozyme data modelled using MONSA [32], panel A in D2O, panel B in H2O. 10 models were generated for each data set and a typical fit is plotted. The structures on the left under panel A represent three views of models constructed from D2O data, on the right under panel B corresponding views of models constructed from H2O data. The 10 models were aligned to PDB structure 193L [19], compared using DAMAVER and a probability map calculated, represented as a grey surface. DAMAVER was also used to calculate a filtered structure, represented as an orange mesh. The PDB is given as a grey cartoon representation for comparison. DAMAVER gives an estimate of agreement between the different models as a ‘normalised spatial discrepancy’ ( o NSD4), equal to 0.462 7 0.019 and 0.523 7 0.018 for D2O and H2O respectively, showing that all the models are close in overall shape (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).
for lysozyme shows a slight asymmetry, indicating a slightly more elongated particle. Satisfactory form factor fits to the data could be obtained with an ellipsoidal model using SASHA [28], where the three axes of the ellipsoid are 43 Å, 25 Å and 25 Å. Rg and I(0) can also be calculated from p(r), and are therefore effectively calculated from the entire scattering profile. The values are reported in Table 2, and agree within error to those obtained from the Guinier analysis, giving a good indication of sample and data quality. For a particle of well-defined (smooth) surface, the volume of the scattering particle can be calculated from the scattering profile using Porod's law. The calculated volumes for GI and lysozyme are also presented in Table 2. For the proteins in H2O, particle volume agrees to within 5% of the dry volume calculated from the sequence. In the case of D2O, the volume is smaller than that calculated from the sequence, which as discussed above has been explained by scattering from the protein hydration layer [21].
3.3. Data modelling The initial analysis presented in the previous section shows the collected data is of sufficient quality to proceed to shape reconstruction of the particles. The four complete data sets, GI and lysozyme in D2O and H2O, were modelled using MONSA [32] from the ATSAS package. MONSA constructs bead models based on small angle scattering data, and is well suited to SANS data as it is designed to simultaneously fit multiple curves from macromolecules measured in buffers of differing contrasts. Here MONSA was used at a single contrast only, and for each sample, was run 10 times to construct 10 models. Contrasts used in the input files were obtained from MULCh [29] as in Table 2. Initial spherical search volumes were calculated using DAMESV, with radii of 23 Å for lysozyme and 52 Å for GI. Default values were used in MONSA, except for the number of annealing steps, which was extended to 200 in the case of GI in H2O.
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Fig. 5. Glucose isomerase data modelled using MONSA [32], panel A in D2O, panel B in H2O. 10 models were generated for each data set and a typical fit is plotted. The structures on the left under panel A represent three views of models constructed from D2O data, on the right under panel B corresponding views of models constructed from H2O data. The 10 models were aligned to PDB structure 1OAD [20], compared using DAMAVER and a probability map calculated, represented here as a grey surface. DAMAVER was also used to calculate a filtered structure, represented as an orange mesh. The PDB is given as a grey cartoon representation for comparison. The estimate of agreement between the different models given by DAMAVER ‘normalised spatial discrepancy’ ( o NSD4), was found to be 0.4987 0.013 and 0.4747 0.018 for D2O and H2O, respectively. The o NSD4 values indicate a reliable reconstruction [35] (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).
Instrument resolution was taken into account and corresponding file given in the Supplementary material. Figs. 4 and 5 show SANS data and results of the bead modelling for lysozyme and GI, respectively. In all cases, all 10 models were found to fit the data well, and only one is represented in the upper panels. As discussed previously the data from samples measured in H2O have a lower signal to noise ratio for both GI and lysozyme, the data was modelled out to a higher Q for the D2O samples compared to H2O. The models were aligned to the published crystal structures using SUPCOMB [33] and inspected in PyMOL to ensure they were all globular proteins of the same overall shape as the crystal structure [34]. All lysozyme models were ellipsoids of similar size to the crystallographic structure. The GI models calculated from the data measured in D2O, were all approximately spherical, but all
contained a small pocket of water in the centre of the structure. Increasing the number of iterations and changing the fitted Q range, did not eliminate the water cavity. When the GI data in H2O was initially modelled with 100 annealing steps, several beads of solvent were found in the protein structure, but no water pocket in the centre was found. The internal solvent beads disappeared on increasing the minimum number of annealing steps to 200. The example shows that careful consideration of Monsa input parameters and extracted models is required. The 10 different models generated with MONSA for each data set were then compared using DAMAVER [35], which compares the structures, finds the most probable and any outliers. In all cases, no outliers as defined by DAMAVER were found. DAMAVER [35] was then used to align the models, calculate a probability map (represented as a grey surface in Figs. 4 and 5) and a filtered
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average model (plotted as orange mesh). In Figs. 4 and 5 the probability maps and averaged models are compared to the backbone of the crystallographic structures. Three different views represent the 3D structure. As can be seen in the figures, a reasonable agreement is observed between the crystallographic structures and models determined using SANS data. Agreement between the shape of models obtained from the data and the crystal structures again indicates data measured on Quokka are of sufficient quality to enable modelling of biomolecules. 4. Conclusion The data presented clearly demonstrate that the SANS instrument Quokka is well suited for structural biology experiments, with data being collected in a reasonable time (100 min per sample). The data are of comparable quality to other leading reactor based SANS instruments at the Institut Laue Langevin and the National Institute of Standards and Technology. Structural parameters in line with previously published data were calculated and models generated using MONSA also show reasonable agreement with other structural work. For completeness, all reduced data sets and MONSA input files and models are included in the supplementary material, and raw data are available on request. Acknowledgements The authors thank Andrew Whitten for fruitful discussions. Appendix A. Supporting information Supplementary data associated with this article can be found in the online version at http://dx.doi.org/10.1016/j.nima.2015.06.034. References [1] M.H. Koch, P. Vachette, D.I. Svergun, Quarterly Reviews of Biophysics 36 (2) (2003) 147. [2] D.I. Svergun, M.H.J. Koch, Reports on Progress in Physics 66 (10) (2003) 1735.
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[3] S.J. Perkins, et al., Biochemical Society Transactions 30 (Pt 6) (2002) 996. [4] C. Neylon, European Biophysics Journal with Biophysics Letters 37 (5) (2008) 531. [5] J.K. Krueger, G.D. Wignall, Small-angle neutroon scattering from biological molecules, in: J. Fitter, T. Gutberlet, J. Katsaras (Eds.), Neutron Scattering in Biology Techniques and Applications, Springer-Verlag, Berlin Heidelberg, 2006. [6] C.D. Putnam, et al., Quarterly Reviews of Biophysics 40 (3) (2007) 191. [7] D. Svergun, et al., Small Angle X-Ray and Neutron Scattering from Solutions of Biological Macromolecules, Oxford University Press, Oxford, 2013. [8] M.A. Graewert, D.I. Svergun, Current Opinion in Structural Biology 23 (5) (2013) 748. [9] S.S. Pang, et al., Nature 467 (7317) (2010) 844. [10] E. Mylonas, D.I. Svergun, Journal of Applied Crystallography 40 (2007) S245. [11] M. Kozak, Journal of Applied Crystallography 38 (2005) 555. [12] W.R. Krigbaum, F.R. Kugler, Biochemistry 9 (5) (1970) 1216. [13] E. Gasteiger, et al., Protein identification and analysis tools on the ExPASy server, in: J.M. Walker (Ed.), The Proteomics Protocols Handbook, Humana Press, New Jersey, 2005, pp. 571–607. [14] E.P. Gilbert, J.C. Schulz, T.J. Noakes, Physica B-Condensed Matter 385-86 (2006) 1180. [15] S.R. Kline, Journal of Applied Crystallography 39 (2006) 895. [16] 〈http://www.ncnr.nist.gov/programs/sans/data/red_anal.html〉. NIST SANS data reduction and analysis page. Available from: 〈http://www.ncnr.nist.gov/ programs/sans/data/red_anal.html〉. [17] D.I. Svergun, Journal of Applied Crystallography 25 (1992) 495. [18] D.A. Jacques, et al., Acta Crystallographica Section D: Biological Crystallography Pt 6 (2012) 62068 Pt 6 (2012) 620. [19] M.C. Vaney, et al., Acta Crystallographica Section D: Biological Crystallography 52 (1996) 505. [20] U.A. Ramagopal, M. Dauter, Z. Dauter, Acta Crystallographica Section D: Biological Crystallography Pt 5 (2003) 86859 Pt 5 (2003) 868. [21] D.I. Svergun, et al., Proceedings of the National Academy of Sciences of the United States of America 95 (5) (1998) 2267. [22] D.A. Jacques, J. Trewhella, Protein science: a Publication of the Protein Society 19 (4) (2010) 642. [23] E. Banachowicz, et al., Journal of Applied Crystallography 42 (2009) 461. [24] A. Guinier, G. Fournet, Small Angle Scattering of X-rays, John Wiley and Sons, New York, 1955. [25] P.V. Konarev, et al., Journal of Applied Crystallography 36 (2003) 1277. [26] I.N. Serdyuk, N.R. Zaccai, G. Zaccai, Methods in Molecular Biophysics, Cambridge University Press, Cambridge, 2007. [27] M.V. Petoukhov, et al., Journal of Applied Crystallography 40 (2007) S223. [28] D.I. Svergun, et al., Acta Crystallographica Section A 52 (1996) 419. [29] A.E. Whitten, S.Z. Cai, J. Trewhella, Journal of Applied Crystallography 41 (2008) 222. [30] R.B. Gregory, R. Lumry, Biopolymers 24 (2) (1985) 301. [31] N.R. Voss, M. Gerstein, Journal of molecular biology 346 (2) (2005) 477. [32] D.I. Svergun, Biophysical Journal 76 (6) (1999) 2879. [33] M.B. Kozin, D.I. Svergun, Journal of Applied Crystallography 34 (2001) 33. [34] Schrödinger, L., The PyMOL Molecular Graphics System. [35] V.V. Volkov, D.I. Svergun, Journal of Applied Crystallography 36 (2003) 860.
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Nuclear Instruments and Methods in Physics Research A journal homepage: www.elsevier.com/locate/nima
Exploring the structure of biological macromolecules in solution using Quokka, the small angle neutron scattering instrument, at ANSTO Kathleen Wood a,n, Cy M. Jeffries a,1, Robert B. Knott a, Anna Sokolova a, David A. Jacques b, Anthony P. Duff a a b
Australian Nuclear Science and Technology Organisation, New Illawarra Rd, Lucas Heights, NSW 2234, Australia MRC Laboratory of Molecular Biology, Cambridge, United Kingdom
art ic l e i nf o
a b s t r a c t
Article history: Received 29 March 2015 Received in revised form 17 June 2015 Accepted 23 June 2015 Available online 30 June 2015
Small angle neutron scattering (SANS) is widely used to extract structural parameters, shape and other types of information from a vast array of materials. The technique is applied to biological macromolecules and their complexes in solution to reveal information often not accessible by other techniques. SANS measurements on biomolecules present some particular challenges however, one of which is suitable instrumentation. This review details SANS experiments performed on two wellcharacterised globular proteins (lysozyme and glucose isomerase) using Quokka, the recently commissioned SANS instrument at the Australian Nuclear Science and Technology Organisation (ANSTO). The instrument configuration as well as data collection and reduction strategies for biological investigations are discussed and act as a general reference for structural biologists who use the instrument. Both model independent analysis of the two proteins and ab initio modelling illustrate that Quokka-SANS data can be used to successfully model the overall shapes of proteins in solution, providing a benchmark for users. & 2015 Published by Elsevier B.V.
Keywords: Small angle neutron scattering Biological structure Protein Macromolecule
1. Introduction Small angle scattering (SAS) offers unique opportunities to investigate the shapes, conformations and interactions between, and within, higher-order macromolecular complexes in solution and how these vary when altering sample environments. The general principles of SAS are well established and there are a number of excellent reviews and texts available on the subject [1–7]. Small angle X-ray scattering (SAXS) is increasingly used by structural biologists to determine the global shapes and organisation of macromolecules [8] and outstanding results are being achieved (e.g. [9]). Small angle neutron scattering (SANS) can reveal additional information not accessible by SAXS based on the very different way neutrons coherently scatter from ‘light’ hydrogen (H) compared to its ‘heavy’ counterpart deuterium (D). Importantly, by altering the ratio of H:D in either the buffer, or by investigating complexes consisting of components with different average numbers of H per unit volume (e.g., protein–nucleic acid and protein–lipid complexes, or D-labelled protein bound to H-protein) it becomes possible to ‘match-in’ and
Abbreviations: SAS, small angle scattering; SAXS, small angle X-ray scattering; GI, glucose isomerase; SANS, small angle neutron scattering; MW, molecular weight n Corresponding author. Tel.: þ61 6 9717 7100. E-mail address: [email protected] (K. Wood). 1 Present address: European Molecular Biology Laboratory, Hamburg, Germany. http://dx.doi.org/10.1016/j.nima.2015.06.034 0168-9002/& 2015 Published by Elsevier B.V.
‘match-out’ coherent neutron scattering contributions from the data. In other words, when applied to investigating macromolecular complexes consisting of components with different average neutron scattering length densities (ρ), SANS makes it possible to extract distance and shape information from a whole complex and, importantly, the shapes and dispositions of individual components within the complex. For biological macromolecules in solution, the isolation of component scattering functions using SANS is usually achieved by altering the neutron scattering length density of the buffer, i.e., substituting H2O with D2O, which changes the neutron contrast, or Δρ, between the solvent and the target macromolecule in the sample. SANS experiments are also advantageous in terms of radiation damage, which is nonexistent for most biological samples placed in a neutron beam. Neutron fluxes are typically several orders of magnitude less than typical X-ray sources. The relatively low flux is partially compensated for by larger beams, therefore larger samples, longer exposure times and increased sample concentration. Experiments must therefore be performed on an instrument with sufficient flux. Here we provide a framework for biological SANS experiments using the Quokka instrument at ANSTO and demonstrate its suitability for structural biology experiments. A comprehensive study of a novel biological complex using SANS generally involves ‘matching out’ a component as described above. The experiments reported in this review do not include such multi-component experiments but rather demonstrate the capabilities of Quokka applied to single-component
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biological samples. Using two standard globular proteins as test cases that mimic a typical size range investigated using SANS – lysozyme (14 kDa) and glucose isomerase (GI: 170 kDa) [10–12] – we benchmark the instrument, quantity of sample and exposure times necessary to obtain quality data from biological samples.
2. Methods 2.1. Sample preparation Lysozyme powder (USB Corporation) was resuspended in 10 ml of 150 mM NaCl, 40 mM NaCH3COO, pH 3.8 (sodium acetate buffer) in H2O to a final concentration of 8 mg ml 1 and filtered (0.22 μm). Individual 500 μL aliquots were dialysed overnight at room temperature against 100 ml of either 100% v/v H2O sodium acetate buffer (pH 3.8) or sodium acetate buffer made up in 100% v/v D2O, pD 3.8. The final concentration of the H2O and 2D2O lysozyme samples post-dialysis were 7.86 and 7.78 mg ml 1, respectively, as determined at Abs280 nm using an extinction coefficient (E0.1%) of 2.7 L.g 1 cm 1 [13]. Glucose isomerase (GI) (Hampton Research HR7-100) was supplied as a crystalline suspension. The crystals were dissolved in 350 μl of 200 mM Na2SO4, 50 mM K2SO4, 1 mM MgSO4, 50 mM HEPES, pH/ pD 8.0 (HEPES buffer) in either 100% v/v H2O, or 100% v/v D2O and
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dialysed overnight against the respective H2O or D2O HEPES buffer. The final concentrations used for the SANS experiments were 1.85 mg ml 1 and 1.61 mg ml 1 for the H2O–GI and D2O–GI, samples (determined at Abs280 nm using an E0.1% of 1.1 L.g 1 cm 1). A dilution series of GI (from 3 to 0.1 mg mL 1) was also prepared using the matched solvent post-dialysis buffers as diluent. Note that for the described sample preparation conditions, the proteins were known to be monodispersed. For SANS data acquisition, all samples and their respective matched solvents (300 μl each) were mounted in 1 mm pathlength quartz cuvettes (Hellma) and placed into a multi-position sample holder at room temperature located at the Quokka SANS beam line. Temperature variation over the duration of the experiment was less than 72 1C. 2.2. Instrument setup The Quokka-SANS instrument at ANSTO is a 40 m continuous instrument employing 20 m variable collimation, with a 20 m variable sample to detector distance. Typically called a pinhole instrument, a circular aperture at the end of the guides defines the source size and various aperture sizes are selected at the sample position. The maximum accessible Q-range spans three orders of magnitude, from 0.0007 to 0.7 Å 1 (where Q¼4πθ⧸λ, θ is the scattering angle and λ is the wavelength of the incident radiation). The incident neutron wavelength, λ, and wavelength resolution, Δλ/λ are defined by a
Table 1 Details of configurations used for the measurements. A 12.5 mm diameter sample aperture was used for all configurations.
a
Source-sample distance [m] Sample-detector distance [m] Detector offset [m]b Beamstop diameter [mm] Qmin o Qo Qmax [Å 1] H2O buffer count ratec [kHz] D2O buffer count rate [kHz] Empty cell count rate [kHz] Time measured [min]
Low-Q configuration
Medium-Q configuration
High-Q configuration
10 10 0 110 0.0078o Qo 0.08 1 0.2 0.1 100
4 4 0 110 0.023 o Qo 0.2 34 6.6 4.7 20
10 2 0.3 44 0.023 oQ o 0.5 21 3.6 1.7 30
a Due to count rate limitations on the detector, the high Q configuration was measured with a larger source to sample distance than strictly necessary, which allowed the use of a smaller beamstop and hence increased the accessible Q-range. b The 1 m 1 m detector currently used on Quokka can be offset from the central position to enable access to higher Q range. c Count rates are approximate only, but give an estimate of expected rates as an experimental check.
Table 2 Sample characteristics and parameters obtained from I(Q).
Concentration [mg/mL] Neutron contrasta (Δρ) [1010 cm 2] Calculated MW b [kDa] Dry volumec [Å3] Partial specific volumec[mL/g] Envelope diameter (CRYSON) Rg [Å] – Guinier analysis I(0) [cm 1] – Guinier analysis MW estimated from data [kDa] ‘Quality’ of data – AutoRg [25] Dmax – Autoporod [27] [Å] Rg [Å] – p(r) analysis I(0) [cm 1] – p(r) analysis Porod volume [ 10 3 Å3][25]
Lysozyme D2O
Lysozyme H2O
GI D2O
GI H2O
7.78 2.917 14.5 17,570 0.740 51 12.8 7 0.4 (0.2 o QRg o 0.8) 0.089 7 0.001 14.0 96% 43 12.6 7 0.1 (0.013o Q o0.35 Å) 0.0889 7 0.0004 12.9 7 0.7 (0.013o Qo 0.18 Å)
7.86 2.499 14.3 17,570 0.740 51 14.4 7 0.8 (0.2o QRg o1.0) 0.068 7 0.001 15.0 58% 46 14.7 7 0.4 (0.013o Qo 0.30 Å) 0.066 7 0.001 18.6 70.7 (0.013o Qo 0.16 Å)
1.61 3.436 173.8 211,063 0.738 102 31.17 0.2 (0.4 o QRg o 1.3) 0.298 70.001 172.9 84% 104 31.17 0.2 (0.013o Qo 0.23 Å) 0.299 70.001 1877 10 (0.013o Qo 0.07 Å)
1.87 2.458 172.8 211,063 0.738 102 32.9 7 0.9 (0.4 oQRg o 1.3) 0.1877 0.003 172.3 91% 95 31.7 7 0.5 (0.013o Qo 0.20 Å) 0.1847 0.002 220 7 6 (0.013o Qo 0.07 Å)
In all data sets, data points up to Q ¼0.013 Å were excluded from the analysis. a b c
Calculated using MULCh [29]. Calculated from sequence assuming 62% of exchangeable hydrogen atoms actually exchange with buffer for GI, 85% for lysozyme [30], D2O at 98% using MULCh [29]. Calculated from sequence using NucProt calculator [31]. PDB files 193L and 1OAD were used. 2GVE used for calculating exchangeable hydrogen atoms for GI.
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rotating mechanical velocity selector and were set at 5 Å and 10%, respectively to maximise the flux at the sample position. More details on instrument hardware are provided in [14]. Depending on the required Q-range the sample to detector distance and wavelength are varied. The Q-range measured should (i) encompass the maximum intra-particle distance (Dmax) which defines Qmin and (ii) extend to high-Q (Qmax) for accurate background subtraction. Typically, it is necessary to measure samples in several instrument configurations and merge the data from different detector distances to cover the full Q-range. More detail on how to select the desired Q-range is given in Supplementary information S1. For the experiments reported here, the parameters of the different instrument configurations are summarised in Table 1. The GI and lysozyme protein samples were measured at both lowQ (100 min) and high-Q (30 min) configurations. Sample transmissions were determined at 10 m for two minutes per sample using the attenuated direct beam.
– Data were put on an absolute intensity scale using the attenuated empty beam measurement to determine a normalisation factor for one instrument configuration. The data were then radially averaged to produce onedimensional profiles of scattered intensity, I(Q), as a function of Q. Errors were propagated in the reduction process. More details are given in the Supplementary information S2. As can be seen in Table 2, there is significant overlap in Q between low- and high-Q configurations and data form the two detector positions were merged into a single data set using the ‘sort’ option in the Igor Pro macros. Merging involved deleting the data points with large statistical errors at high-Q and the points covered by the beam stop. The high-Q configuration was scaled to the low-Q configuration. The resulting instrument independent 1D scattering profiles from the protein and buffer were then subtracted as described in Supplementary information S2.
3. Results and discussion 2.3. Data processing 3.1. Dilution series Data were reduced using macros for Igor Pro (WaveMetrics Inc) developed at NIST with Quokka specific modifications [15]. Details of the reduction procedures can be found in the associated help file and on the NIST website [16]. For the data presented here, the isotropic two-dimensional data were corrected for instrument effects in the following steps: – Empty cell scattering was subtracted from both buffer and sample. – The detector sensitivity was corrected by normalising the data to a uniformly flat scatterer (a sheet of Plexiglass). Data from the flat scatterer is generally only recorded once per reactor cycle, to ensure that no changes to the detector response are observed.
The inherent low flux of neutron sources is compensated for in several ways, including larger beam sizes (therefore larger samples) and increased macromolecular concentrations. If samples remain monodisperse, without interparticle interference effects, and sample availability is not an issue, SANS experiments are often performed at relatively high sample concentrations (5– 10 mg mL 1). In order to give users some idea of the limits in terms of concentrations for biological experiments on Quokka, a dilution series was performed on GI in H2O and D2O. The protein solutions were measured at a single medium-Q configuration only for 20 min (see Table 1) and the reduced data presented in Fig. 1. Note that all samples were measured in 1 mm pathlength cells, which in the case of D2O samples may be further increased to
Fig. 1. Glucose isomerase at various concentrations at sample to detector distance of 4 m. Sample, buffer and empty cell were all measured for 20 min each. 300 μL of solution are loaded in each case. Traces are offset for clarity.
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Fig. 2. Lysozyme in D2O (blue in all panels) and H2O (green in all panels). (A) Experimental data with fits to PDB model structure 193L [19] using CRYSON [21] and fit from which p(r) is calculated. (B) Guinier plot from which radius of gyration and molecular weight are calculated (see Table 2). (C) Atom-pair distance distribution in the scattering particle calculated from I(Q) [25]. (D) Kratky plot, showing the folded nature of the protein in both D2O and H2O (tending to zero at high Q). Traces offset for clarity in (A) and (B) (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).
2 mm if required. As can be seen in Fig. 1, data remain a reasonable quality down to low protein concentrations in D2O. Clearly, the necessary data quality depends on the information to be extracted from the scattering, and the data shown in the figure give the reader an estimation of necessary concentration and count times. For a protein of high molecular weight like GI, a reliable Rg can be obtained from the data shown in Fig. 1 at concentrations down to 0.5 mg/mL in both D2O and H2O, while full modelling may require higher concentration solutions. Dividing the highest concentrated data set by the medium one reveals a flat line, as would be expected from the dilution. When the data from the weakest concentration sample is divided by the others however, this is not the case. Even if the data were measured a much longer time to improve the error on the counting statistics, it would appear that other sources of error are visible at 0.1 mg/mL, possibly due to background subtraction from the weak scatterer. 3.2. Experimental data and model-independent analysis Lysozyme and GI data measured on Quokka across the full (i.e., merged) Q-range and associated plots are presented in Figs. 2 and 3, respectively. In our initial analysis, the guidelines for evaluation and presentation of small angle scattering data set out in [18] were adopted. The theoretical background to the model independent analysis of data is set out in Supplementary information S3.
In panel A of Figs. 2 and 3, the measured data is plotted as the measured neutron intensity I(Q) versus Q. The increased variance and errors in the scattering data for the proteins measured in H2O compared to D2O are caused by a combination of a lower absolute magnitude in the coherent neutron scattering contrast in the H2O buffer (Table 2), and, more significantly, an increase in incoherent scattering from H when H2O is used as a buffer. Scattering profiles were calculated from high-resolution structures determined by Xray crystallography [19,20] using CRYSON [21] and are presented in panel A of Figs. 2 and 3. As can be seen, the calculated scattering profiles agree well with data measured from both D2O and H2O samples for GI and lysozyme, indicating that the shape of the proteins in solution is the same as their structure in single crystals – as has been observed previously [22,23]. In panel B of Figs. 2 and 3, Guinier representation of SANS data (lnI(Q) versus Q2) are presented [24]. Guinier plots, when displayed at very low Q, give a first qualitative indication of sample quality [18,22]. Often deviations from linearity in the Gunier region indicate complex formation, aggregation or interparticle interference [22]. All four protein samples show a linear Guinier region indicating sample monodispersity. The radii of gyration (Rg) of the scattering particles, proportional to the slope at low Q in the Guinier plots, are presented in Table 2. The Guinier fits were performed using PRIMUS [25], and the upper limit of the Q-range (QmaxRg o1.3) chosen manually. An automatic Guinier fit was also
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Fig. 3. Glucose isomerase in D2O (blue in all panels) and H2O (green in all panels). (A) Experimental data with fits to PDB model structure 1OAD [20] using CRYSON [21] and fit from which p(r) is calculated. (B) Guinier plot from which radius of gyration and molecular weight are calculated (see Table 2). (C) Atom-pair distance distribution in the scattering particle calculated from I(Q) [25]. (D) Kratky plot, showing the folded nature of the protein in both D2O and H2O (tending to zero at high Q). Traces offset for clarity in (A) and (B) (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).
performed using AutoRg [25] from the ATSAS package and found not to differ substantially from that determined manually. For both proteins, the determined Rg in D2O is approximately 1.5 Å smaller than that in H2O buffer. The difference can be explained by differences in the scattering length density of the hydration layer around the proteins [21]. The lysozyme Rg reported here agree within error to previously published values [21], where values of 12.4 70.2 Å and 13.8 7 0.2 Å were reported for lysozyme in D2O and H2O, respectively. AutoRg also gives a quality assessment of the data, which is reported in Table 2. AutoRg determined the data quality to be above 80% for three of the samples, with a significantly lower quality for lysozyme in H2O. Lower data quality for lysozyme in H2O is also apparent by inspection (Fig. 2), which is due to three sample parameters: small size of the particle, high incoherent background from the H2O buffer and lower contrast between protein and buffer. The intercept of the Guinier plot extrapolated to zero-angle (I (0)) can be used to calculate the molecular weight (MW) of the scattering particle if the concentration of the solution, contrast and partial specific volume of the particle are all known [26] (see S3 for method). The calculation of MW is an important check of sample quality. Parameters used to calculate MW from the Guinier analysis as well as the extracted MW are reported in Table 2. All
MW obtained from the experimental data agree within 5% with the calculated values determined from the amino acid sequences of the proteins. The MW calculated from I(0) have several sources of error: the absolute intensity from the instrument is determined to within 2%, the error on the concentration determination is estimated to be 10%. In addition the calculated value is strongly dependent on the calculated neutron contrast, and in the current study correctly taking into account exchangeable hydrogen atoms was found to be crucial to correctly assessing MW. The molecular weight estimates from I(0) and concentration confirm that, as expected, lysozyme is monomeric in solution, while GI is a monodisperse tetramer, in agreement with the standard molecular weight values for these proteins. The atom-pair distance distribution (p(r)) in real space can be calculated via an indirect Fourier transformation of the I(Q) data [7]. The p(r) represents the distribution of distances between scattering centres within the particle and are plotted for all for samples in panels C of Figs. 2 and 3, (calculated using GNOM [17]). The maximum particle dimension (Dmax) is an imposed constraint in the calculation of the p(r), and was estimated using the AutoPorod [27] package, which evaluates the p(r) for several Dmax and selects the most suitable Dmax based on the shape of the resulting p(r) as described in [27]. The p(r) plotted in Fig. 2 for GI indicates a spherical particle with a distribution of distances symmetric around the maximum value at Dmax/2. The p(r)
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Fig. 4. Lysozyme data modelled using MONSA [32], panel A in D2O, panel B in H2O. 10 models were generated for each data set and a typical fit is plotted. The structures on the left under panel A represent three views of models constructed from D2O data, on the right under panel B corresponding views of models constructed from H2O data. The 10 models were aligned to PDB structure 193L [19], compared using DAMAVER and a probability map calculated, represented as a grey surface. DAMAVER was also used to calculate a filtered structure, represented as an orange mesh. The PDB is given as a grey cartoon representation for comparison. DAMAVER gives an estimate of agreement between the different models as a ‘normalised spatial discrepancy’ ( o NSD4), equal to 0.462 7 0.019 and 0.523 7 0.018 for D2O and H2O respectively, showing that all the models are close in overall shape (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).
for lysozyme shows a slight asymmetry, indicating a slightly more elongated particle. Satisfactory form factor fits to the data could be obtained with an ellipsoidal model using SASHA [28], where the three axes of the ellipsoid are 43 Å, 25 Å and 25 Å. Rg and I(0) can also be calculated from p(r), and are therefore effectively calculated from the entire scattering profile. The values are reported in Table 2, and agree within error to those obtained from the Guinier analysis, giving a good indication of sample and data quality. For a particle of well-defined (smooth) surface, the volume of the scattering particle can be calculated from the scattering profile using Porod's law. The calculated volumes for GI and lysozyme are also presented in Table 2. For the proteins in H2O, particle volume agrees to within 5% of the dry volume calculated from the sequence. In the case of D2O, the volume is smaller than that calculated from the sequence, which as discussed above has been explained by scattering from the protein hydration layer [21].
3.3. Data modelling The initial analysis presented in the previous section shows the collected data is of sufficient quality to proceed to shape reconstruction of the particles. The four complete data sets, GI and lysozyme in D2O and H2O, were modelled using MONSA [32] from the ATSAS package. MONSA constructs bead models based on small angle scattering data, and is well suited to SANS data as it is designed to simultaneously fit multiple curves from macromolecules measured in buffers of differing contrasts. Here MONSA was used at a single contrast only, and for each sample, was run 10 times to construct 10 models. Contrasts used in the input files were obtained from MULCh [29] as in Table 2. Initial spherical search volumes were calculated using DAMESV, with radii of 23 Å for lysozyme and 52 Å for GI. Default values were used in MONSA, except for the number of annealing steps, which was extended to 200 in the case of GI in H2O.
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Fig. 5. Glucose isomerase data modelled using MONSA [32], panel A in D2O, panel B in H2O. 10 models were generated for each data set and a typical fit is plotted. The structures on the left under panel A represent three views of models constructed from D2O data, on the right under panel B corresponding views of models constructed from H2O data. The 10 models were aligned to PDB structure 1OAD [20], compared using DAMAVER and a probability map calculated, represented here as a grey surface. DAMAVER was also used to calculate a filtered structure, represented as an orange mesh. The PDB is given as a grey cartoon representation for comparison. The estimate of agreement between the different models given by DAMAVER ‘normalised spatial discrepancy’ ( o NSD4), was found to be 0.4987 0.013 and 0.4747 0.018 for D2O and H2O, respectively. The o NSD4 values indicate a reliable reconstruction [35] (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).
Instrument resolution was taken into account and corresponding file given in the Supplementary material. Figs. 4 and 5 show SANS data and results of the bead modelling for lysozyme and GI, respectively. In all cases, all 10 models were found to fit the data well, and only one is represented in the upper panels. As discussed previously the data from samples measured in H2O have a lower signal to noise ratio for both GI and lysozyme, the data was modelled out to a higher Q for the D2O samples compared to H2O. The models were aligned to the published crystal structures using SUPCOMB [33] and inspected in PyMOL to ensure they were all globular proteins of the same overall shape as the crystal structure [34]. All lysozyme models were ellipsoids of similar size to the crystallographic structure. The GI models calculated from the data measured in D2O, were all approximately spherical, but all
contained a small pocket of water in the centre of the structure. Increasing the number of iterations and changing the fitted Q range, did not eliminate the water cavity. When the GI data in H2O was initially modelled with 100 annealing steps, several beads of solvent were found in the protein structure, but no water pocket in the centre was found. The internal solvent beads disappeared on increasing the minimum number of annealing steps to 200. The example shows that careful consideration of Monsa input parameters and extracted models is required. The 10 different models generated with MONSA for each data set were then compared using DAMAVER [35], which compares the structures, finds the most probable and any outliers. In all cases, no outliers as defined by DAMAVER were found. DAMAVER [35] was then used to align the models, calculate a probability map (represented as a grey surface in Figs. 4 and 5) and a filtered
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average model (plotted as orange mesh). In Figs. 4 and 5 the probability maps and averaged models are compared to the backbone of the crystallographic structures. Three different views represent the 3D structure. As can be seen in the figures, a reasonable agreement is observed between the crystallographic structures and models determined using SANS data. Agreement between the shape of models obtained from the data and the crystal structures again indicates data measured on Quokka are of sufficient quality to enable modelling of biomolecules. 4. Conclusion The data presented clearly demonstrate that the SANS instrument Quokka is well suited for structural biology experiments, with data being collected in a reasonable time (100 min per sample). The data are of comparable quality to other leading reactor based SANS instruments at the Institut Laue Langevin and the National Institute of Standards and Technology. Structural parameters in line with previously published data were calculated and models generated using MONSA also show reasonable agreement with other structural work. For completeness, all reduced data sets and MONSA input files and models are included in the supplementary material, and raw data are available on request. Acknowledgements The authors thank Andrew Whitten for fruitful discussions. Appendix A. Supporting information Supplementary data associated with this article can be found in the online version at http://dx.doi.org/10.1016/j.nima.2015.06.034. References [1] M.H. Koch, P. Vachette, D.I. Svergun, Quarterly Reviews of Biophysics 36 (2) (2003) 147. [2] D.I. Svergun, M.H.J. Koch, Reports on Progress in Physics 66 (10) (2003) 1735.
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