research papers\(\def\hfill{\hskip 5em}\def\hfil{\hskip 3em}\def\eqno#1{\hfil {#1}}\)

IUCrJ
Volume 5| Part 2| March 2018| Pages 211-222
ISSN: 2052-2525

Intermolecular correlations are necessary to explain diffuse scattering from protein crystals

aDepartment of Biochemistry, Stanford University, Stanford, CA 94305, USA, bDepartment of Structural Biology, Stanford University, Stanford, CA 94305, USA, cStanford PULSE Institute, SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA, and dBioscience Division and Linac Coherent Light Source, SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA
*Correspondence e-mail: apeck@stanford.edu, tjlane@slac.stanford.edu

Edited by K. Moffat, University of Chicago, USA (Received 3 November 2017; accepted 17 January 2018; online 21 February 2018)

Conformational changes drive protein function, including catalysis, allostery and signaling. X-ray diffuse scattering from protein crystals has frequently been cited as a probe of these correlated motions, with significant potential to advance our understanding of biological dynamics. However, recent work has challenged this prevailing view, suggesting instead that diffuse scattering primarily originates from rigid-body motions and could therefore be applied to improve structure determination. To investigate the nature of the disorder giving rise to diffuse scattering, and thus the potential applications of this signal, a diverse repertoire of disorder models was assessed for its ability to reproduce the diffuse signal reconstructed from three protein crystals. This comparison revealed that multiple models of intramolecular conformational dynamics, including ensemble models inferred from the Bragg data, could not explain the signal. Models of rigid-body or short-range liquid-like motions, in which dynamics are confined to the biological unit, showed modest agreement with the diffuse maps, but were unable to reproduce experimental features indicative of long-range correlations. Extending a model of liquid-like motions to include disorder across neighboring proteins in the crystal significantly improved agreement with all three systems and highlighted the contribution of intermolecular correlations to the observed signal. These findings anticipate a need to account for intermolecular disorder in order to advance the interpretation of diffuse scattering to either extract biological motions or aid structural inference.

1. Introduction

X-ray diffraction images from macromolecular crystals frequently exhibit a diffuse background between and beneath the Bragg peaks (Wall, Adams et al., 2014[Wall, M. E., Adams, P. D., Fraser, J. S. & Sauter, N. K. (2014). Structure, 22, 182-184.]; Welberry & Weber, 2016[Welberry, T. R. & Weber, T. (2016). Crystallogr. Rev. 22, 2-78.]). In contrast to the Bragg reflections, which arise from coherent diffraction across the crystal, this diffuse signal results from disorder-induced incoherent diffraction. The uncorrelated disorder of solvent and macromolecular atoms yields a trivial diffuse scattering pattern that is radially symmetric (Moore, 2009[Moore, P. B. (2009). Structure, 17, 1307-1315.]). On the other hand, correlated disorder produces anisotropic diffuse scattering features whose spacing and intensity in reciprocal space are respectively determined by the length scale and amplitudes of the correlated atomic displacements involved (Benoit & Doucet, 1995[Benoit, J.-P. & Doucet, J. (1995). Q. Rev. Biophys. 28, 131-169.]).

Correlated displacements in macromolecules underlie many biological functions, such as allostery, signaling and enzyme catalysis. However, methods for directly measuring such motions with high spatial resolution are rare. Diffuse scattering has routinely been cited as one such method that could provide unique insights into the collective motions responsible for biological functions (Wall, Adams et al., 2014[Wall, M. E., Adams, P. D., Fraser, J. S. & Sauter, N. K. (2014). Structure, 22, 182-184.]; Benoit & Doucet, 1995[Benoit, J.-P. & Doucet, J. (1995). Q. Rev. Biophys. 28, 131-169.]). Indeed, this has motivated the majority of studies of diffuse scattering from macromolecular crystals (Wall et al., 1997[Wall, M. E., Clarage, J. B. & Phillips, G. N. (1997). Structure, 5, 1599-1612.]; Héry et al., 1998[Héry, S., Genest, D. & Smith, J. C. (1998). J. Mol. Biol. 279, 303-319.]; Kolatkar et al., 1994[Kolatkar, A. R., Clarage, J. B. & Phillips, G. N. (1994). Acta Cryst. D50, 210-218.]; Chacko & Phillips, 1992[Chacko, S. & Phillips, G. N. (1992). Biophys. J. 61, 1256-1266.]; Meinhold & Smith, 2005a[Meinhold, L. & Smith, J. C. (2005a). Phys. Rev. Lett. 95, 218103.],b[Meinhold, L. & Smith, J. C. (2005b). Biophys. J. 88, 2554-2563.], 2007[Meinhold, L. & Smith, J. C. (2007). Proteins, 66, 941-953.]; Wall, Van Benschoten et al., 2014[Wall, M. E., Van Benschoten, A. H., Sauter, N. K., Adams, P. D., Fraser, J. S. & Terwilliger, T. C. (2014). Proc. Natl Acad. Sci. USA, 111, 17887-17892.]; Van Benschoten et al., 2016[Van Benschoten, A. H., Liu, L., Gonzalez, A., Brewster, A. S., Sauter, N. K., Fraser, J. S. & Wall, M. E. (2016). Proc. Natl Acad. Sci. USA, 113, 4069-4074.]), despite the technical challenges of measuring and the computational cost of modeling this signal (Wall, Adams et al., 2014[Wall, M. E., Adams, P. D., Fraser, J. S. & Sauter, N. K. (2014). Structure, 22, 182-184.]).

Recently, the application of diffuse scattering to complement and improve (static) structure inference from crystal diffraction has also been proposed. Chapman and colleagues have suggested that in cases where measurable diffuse scattering extends to higher resolution than the Bragg data, it may be employed for structural inference at that higher resolution (Ayyer et al., 2016[Ayyer, K. et al. (2016). Nature (London), 530, 202-206.]). Further, the ability to oversample the diffraction pattern by measuring the continuous diffuse signal raises the possibility of solving the phase problem directly, without resorting to anomalous or isomorphic methods. This approach, however, assumes that diffuse scattering primarily originates from specific types of disorder, such as rigid-body motions, which are unlikely to inform on biological function.

Identifying the physical origins of diffuse scattering, and thus its potential for probing biological motions or advancing methods, remains a challenge for the field. Many types of disorder involve small motions that can be conveniently described by a matrix, the elements of which give the covariation between the displacements of any two atoms from their mean positions. Such a covariance matrix can directly, but not uniquely, predict diffuse scattering. If the covariance matrix could be inferred directly from experiment, the diffuse signal could be analyzed to determine which regions of the macromolecule move together. There is a fundamental problem with direct inference, however: while the number of observed independent variables is quite large (say V voxels for a given unit-cell volume, assuming that the maximum resolution of diffraction is fixed), the number of unknowns is even larger (of the order of V2 matrix elements, one for each atom pair, assuming that the number of atoms scales linearly with volume). Thus, to infer a covariance matrix one must make simplifying assumptions about the nature of protein motions: a parsimonious model for the protein physics is required.

In this work, we analyze the parsimonious models that have been suggested previously (reviewed in Meisburger et al., 2017[Meisburger, S. P., Thomas, W. C., Watkins, M. B. & Ando, N. (2017). Chem. Rev. 117, 7615-7672.]) and variations of these models to critically examine the types of disorder underlying the diffuse scattering observed in a range of systems. The three protein systems analyzed here represent both diverse crystalline properties and biological functions: cyclophilin A (CypA), a monomeric proline isomerase (Van Benschoten et al., 2016[Van Benschoten, A. H., Liu, L., Gonzalez, A., Brewster, A. S., Sauter, N. K., Fraser, J. S. & Wall, M. E. (2016). Proc. Natl Acad. Sci. USA, 113, 4069-4074.]); WrpA, a flavodoxin-like protein (Peck et al., 2016[Peck, A., Sunden, F., Andrews, L. D., Pande, V. S. & Herschlag, D. (2016). J. Mol. Biol. 428, 2758-2768.]); and a dimeric enzyme, alkaline phosphatase (AP), bound to its transition-state analog (Herrou et al., 2016[Herrou, J., Czyż, D. M., Willett, J. W., Kim, H. S., Chhor, G., Babnigg, G., Kim, Y. & Crosson, S. (2016). J. Bacteriol. 198, 1281-1293.]). Despite this diversity, we found that multiple models of intramolecular conformational dynamics were unable to explain the observed diffuse scattering in all three systems. By contrast, models of simpler dynamics, including rigid-body and liquid-like motions, consistently showed modest correlation with the experimental signal when correlations were confined to the biological unit. The conventional form of the liquid-like motions model, in which correlations span neighboring protein molecules in the crystal, significantly improved the agreement both quantitatively and qualitatively, but still did not fully account for the observed signal. This comprehensive comparison both reconciles opposing viewpoints about the principal physical origins of diffuse scattering and specifically identifies intermolecular correlations as a critical component of the underlying disorder. Further, these findings anticipate that deconvolving the intermolecular contribution from the signal will be required to enable the future application of diffuse scattering to probe biological motions or to improve static structure inference.

2. Methods

2.1. Reconstruction of three-dimensional diffuse scattering maps

For each experimental data set, the Bragg reflections were indexed by XDS (Kabsch, 2010b[Kabsch, W. (2010b). Acta Cryst. D66, 125-132.]). Refined parameters from XDS were then used to determine the reciprocal-space co­ordinates of each pixel in every diffraction image. Measured intensities were corrected for polarization of the X-ray beam (Hura et al., 2000[Hura, G., Sorenson, J. M., Glaeser, R. M. & Head-Gordon, T. (2000). J. Chem. Phys. 113, 9140.]) and the difference in solid angle subtended by pixels at different scattering angles (Wall, 2009[Wall, M. E. (2009). Methods Mol. Biol. 544, 269-279.]). Per-image scale factors from XDS were applied to correct for differences in overall intensity across the rotation range (Kabsch, 2010b[Kabsch, W. (2010b). Acta Cryst. D66, 125-132.]). For data sets collected on a PILATUS detector, parallax broadening was also accounted for, as implemented in DIALS (Waterman et al., 2016[Waterman, D. G., Winter, G., Gildea, R. J., Parkhurst, J. M., Brewster, A. S., Sauter, N. K. & Evans, G. (2016). Acta Cryst. D72, 558-575.]; Winter et al., 2018[Winter, G., Parkhurst, J., Fuentes-Montero, L., Hattne, J., Brewster, A., Sauter, N. & Evans, G. (2018). Acta Cryst. D74, 85-97.]). No Lorentz correction was applied, as pixel intensities were averaged rather than integrated, and the diffuse features were observed to vary gradually in reciprocal space. Under these conditions, the volume of reciprocal space integrated by each pixel only needs to be corrected for the solid angle subtended, and not the arc length that the pixel traverses owing to rotation of the crystal (Boysen & Adlhart, 1987[Boysen, H. & Adlhart, W. (1987). J. Appl. Cryst. 20, 200-209.]).

After correcting for geometrical distortions, Bragg peaks were removed by implementing the spot-prediction algorithm described in Kabsch (2010a[Kabsch, W. (2010a). Acta Cryst. D66, 133-144.]). Pixels predicted to be spanned by a Bragg reflection were masked if their intensity exceeded three standard deviations above the mean intensity of neighboring pixels outside the predicted reflection region in a 30 × 30 pixel window centered on the reflection. Prior studies have replaced masked pixel intensities by the intensities from adjoining pixels (Polikanov & Moore, 2015[Polikanov, Y. S. & Moore, P. B. (2015). Acta Cryst. D71, 2021-2031.]) or pre-filtered images (Ayyer et al., 2016[Ayyer, K. et al. (2016). Nature (London), 530, 202-206.]), but the strategy of masking without replacement was found to maintain an adequate signal-to-noise ratio for map voxels that coincided with Miller indices (Supplementary Fig. S1, solid and dashed lines). To ensure the complete removal of Bragg contaminants, an additional step of masking was performed to eliminate pixels whose intensities exceeded the median radial intensity by more than five times the median absolute deviation of that resolution shell. In the case of WrpA, pixels were additionally masked if they fell within a region contaminated by scattering from the cryo-loop and exceeded the median radial intensity by more than 2.5 times the median absolute deviation of the resolution shell. Supplementary Fig. S2 shows the results of this Bragg masking procedure for a representative image from CypA.

The radial intensity profiles of the images were then compared for uniformity to ensure that independent measurements of equivalent positions in reciprocal space were on the same scale prior to merging. As noted above, variation in the total protein scattering across the rotation range, owing to fluctuations in the illuminated crystal volume and incident beam intensity for instance, was corrected by applying XDS scale factors used to similarly normalize the Bragg intensities. We assumed that large, radially symmetric changes in the measured scattering as the crystal is rotated originate from sources of noncrystalline scattering instead, such as solvent or coating oil. Based on this assumption, the largest variance components between the radial intensity profiles were determined and removed from each image. This approach was validated by comparing symmetry-equivalent positions in the corrected maps, which were found to be consistent. This observation supports our initial assumption that large-variance components were indeed caused by scattering from sources other than the protein crystal.

In the case of CypA, a radially symmetric peak at |q| = 1.3 Å−1 was variably observed, consistent with scattering from the Paratone oil in which the crystal was coated prior to data collection. In images without visible Paratone contamination, the experimental radial intensity profiles approximately followed a second-degree polynomial in the neighboring region of 0.63 < |q| < 1.88 Å−1. Based on this observation, the radial intensity profile for each image was fitted in this region of |q| to the sum of a second-degree polynomial and a scaled Paratone profile: a|q|2 + b|q| + c + mIref,Paratone(|q| − q0). The reference Paratone profile, Iref,Paratone(|q|), was derived from the computed structure factors for noncrystalline Paratone N (Holton, 2016[Holton, J. (2016). nonBragg. http://bl831.als.lbl.gov/~jamesh/nonBragg.]). Fitted parameters associated with the Paratone profile were a multiplicative scale factor, m, and an offset in |q|, q0. The latter parameter accounts for anisotropy in the distribution of the Paratone coating the crystal; optimized values of q0 were small, with a mean of q0 = 0.007 Å−1 across the 360 images in the CypA data set. The resulting Paratone profile was then subtracted from each image. Principal component analysis was performed to remove residual radial variance from the corrected images, and a background profile for each image was generated from the sum of the first two principal components, which were scaled by their associated eigenvalues and projected onto the radial intensity profile of the image. The intensity correction for each pixel was then estimated by linear interpolation from these background profiles and subtracted from each image. In the case of WrpA, the first principal component was similarly used to remove variance in the radial intensity distributions across the rotation range. No further corrections were applied to the AP diffraction images.

Diffuse scattering maps were constructed as three-dimensional grids in reciprocal space whose nodes oversample Miller indices by a factor of three along each lattice direction. Corrected intensities were binned into voxels centered on these nodes, and the mean pixel intensity of each voxel was used to estimate the intensity at each node. The signal-to-noise ratio was estimated as the mean divided by the standard deviation of the intensities binned into each voxel and is shown across resolution shells in Supplementary Fig. S1. Maps were symmetrized by averaging the intensities of Laue- and Friedel-equivalent voxels, followed by subtraction of the interpolated radial intensity. If symmetrization is justified, this order of operations will provide a better estimate of the intensities used to compute the radial average profile; if not, it will introduce bias. However, reversing the symmetrization and radial average subtraction steps was not observed to affect the results, with all correlation coefficients (CCs) observed to be within 0.01 of their prior values. A constant value was then added uniformly to each symmetrized, radial average-subtracted map to ensure that all intensities were positive. The code used to generate the maps is available at https://github.com/apeck12/diffuse.

2.2. Bragg data processing

Refined structural models from the analysis of the Bragg component of these data sets have previously been reported (Van Benschoten et al., 2016[Van Benschoten, A. H., Liu, L., Gonzalez, A., Brewster, A. S., Sauter, N. K., Fraser, J. S. & Wall, M. E. (2016). Proc. Natl Acad. Sci. USA, 113, 4069-4074.]; Peck et al., 2016[Peck, A., Sunden, F., Andrews, L. D., Pande, V. S. & Herschlag, D. (2016). J. Mol. Biol. 428, 2758-2768.]; Herrou et al., 2016[Herrou, J., Czyż, D. M., Willett, J. W., Kim, H. S., Chhor, G., Babnigg, G., Kim, Y. & Crosson, S. (2016). J. Bacteriol. 198, 1281-1293.]). However, because the diffuse maps were generated using refined parameters and a modified version of the spot-prediction algorithm from XDS, the Bragg data were reprocessed to maximize consistency between the treatment of the Bragg and diffuse signals. The Bragg data were indexed, integrated and scaled with XDS (Kabsch, 2010b[Kabsch, W. (2010b). Acta Cryst. D66, 125-132.]); statistics are shown in Table 1[link]. For CypA, molecular replacement was performed with Phaser (McCoy et al., 2007[McCoy, A. J., Grosse-Kunstleve, R. W., Adams, P. D., Winn, M. D., Storoni, L. C. & Read, R. J. (2007). J. Appl. Cryst. 40, 658-674.]) using PDB entry 2cpl (Ke, 1992[Ke, H. (1992). J. Mol. Biol. 228, 539-550.]) as a search model. This was followed by five macrocycles of refinement in PHENIX (Adams et al., 2010[Adams, P. D. et al. (2010). Acta Cryst. D66, 213-221.]) as previously described (Van Benschoten et al., 2016[Van Benschoten, A. H., Liu, L., Gonzalez, A., Brewster, A. S., Sauter, N. K., Fraser, J. S. & Wall, M. E. (2016). Proc. Natl Acad. Sci. USA, 113, 4069-4074.]). For AP, molecular replacement was performed with Phaser using wild-type AP (PDB entry 3tg0; Bobyr et al., 2012[Bobyr, E., Lassila, J. K., Wiersma-Koch, H. I., Fenn, T. D., Lee, J. J., Nikolic-Hughes, I., Hodgson, K. O., Rees, D. C., Hedman, B. & Herschlag, D. (2012). J. Mol. Biol. 415, 102-117.]) stripped of non­protein atoms as the search model. As in Peck et al. (2016[Peck, A., Sunden, F., Andrews, L. D., Pande, V. S. & Herschlag, D. (2016). J. Mol. Biol. 428, 2758-2768.]), zinc ions at full occupancy and a tungstate ion and water molecules at partial occupancy were manually modeled into the residual electron density in each active site. Automated refinement was performed using REFMAC5 (Murshudov et al., 2011[Murshudov, G. N., Skubák, P., Lebedev, A. A., Pannu, N. S., Steiner, R. A., Nicholls, R. A., Winn, M. D., Long, F. & Vagin, A. A. (2011). Acta Cryst. D67, 355-367.]). For WrpA, molecular replacement was performed with Phaser using PDB entry 5f51 as a search model (Herrou et al., 2016[Herrou, J., Czyż, D. M., Willett, J. W., Kim, H. S., Chhor, G., Babnigg, G., Kim, Y. & Crosson, S. (2016). J. Bacteriol. 198, 1281-1293.]). This was followed by alternating rounds of manual refinement in Coot (Emsley et al., 2010[Emsley, P., Lohkamp, B., Scott, W. G. & Cowtan, K. (2010). Acta Cryst. D66, 486-501.]) to model a sulfate ion and water molecules, and automated refinement in Phenix. The Rwork/Rfree values of the final refined models were similar to those deposited previously (Van Benschoten et al., 2016[Van Benschoten, A. H., Liu, L., Gonzalez, A., Brewster, A. S., Sauter, N. K., Fraser, J. S. & Wall, M. E. (2016). Proc. Natl Acad. Sci. USA, 113, 4069-4074.]; Peck et al., 2016[Peck, A., Sunden, F., Andrews, L. D., Pande, V. S. & Herschlag, D. (2016). J. Mol. Biol. 428, 2758-2768.]; Herrou et al., 2016[Herrou, J., Czyż, D. M., Willett, J. W., Kim, H. S., Chhor, G., Babnigg, G., Kim, Y. & Crosson, S. (2016). J. Bacteriol. 198, 1281-1293.]). Diffraction images for the CypA, AP and WrpA data sets are available in the SBGrid Data Bank, with accession Nos. 68 (Fraser, 2015[Fraser, J. S. (2015). X-ray Diffraction Data for Cyclophilin A. PDB Code 4YUO. SBGrid Data Bank v.1. https://doi.org/10.15785/SBGRID/68.]), 456 (Peck et al., 2017[Peck, A., Ressl, S. & Herschlag, D. (2017). X-ray Diffraction Data for E. coli Alkaline Phosphatase in Complex with Tungstate. PDB Code 5C66. SBGrid Data Bank v.1. https://doi.org/10.15785/SBGRID/456.]) and 203 (Herrou & Crosson, 2015[Herrou, J. & Crosson, S. (2015). X-ray Diffraction Data for Structure of B. abortus WrbA-related Protein A (apo-WrpA). PDB Code 5F51. SBGrid Data Bank v.1. https://doi.org/10.15785/SBGRID/203.]), respectively.

Table 1
Data collection, model and map statistics

  CypA AP WrpA
Data-collection and Bragg statistics
 Space group P212121 P6322 P4222
 Unit-cell parameters
  a, b, c (Å) 42.9, 52.4, 89.1 161.3, 161.3, 139.4 61.3, 61.3, 128.7
  α, β, γ (°) 90.0, 90.0, 90.0 90.0, 90.0, 120.0 90.0, 90.0, 90.0
 Wavelength (Å) 0.9795 0.9795 0.9787
 Oscillation range (°) 0.5 0.15 1.0
 Data-collection temperature (K) 273 100 100
 Beam divergence SD (°) 0.0417 0.0285 0.0339
 Mosaicity (°) 0.106 0.136 0.197
 Resolution range (Å) 44.57–1.20 (1.24–1.20) 49.36–2.00 (2.07–2.00) 44.38–2.51 (2.60–2.51)
 Multiplicity 5.8 (4.8) 6.3 (6.5) 12.7 (12.7)
 Completeness (%) 93.6 (85.8) 94.0 (92.7) 99.6 (98.5)
 〈I/σ(I)〉 11.3 (3.9) 26.2 (3.6) 25.5 (4.6)
Rmerge 0.113 (0.504) 0.045 (0.348) 0.061 (0.520)
Rmeas 0.123 (0.564) 0.049 (0.375) 0.063 (0.542)
 CC1/2 0.989 (0.872) 1.000 (0.972) 0.999 (0.962)
 Wilson B2) 16.1 31.4 64.7
 Detector PILATUS 6M PILATUS 6M MAR Mosaic 300
 SBGrid ID 68 456 203
Structure-refinement statistics
 Resolution range (Å) 44.57–1.20 49.36–2.00 44.37–2.50
 No. of non-H atoms 1419 6731 1237
 Average B factor (Å2) 21.4 46.2 77.0
 Solvent content (%) 56 62 52
Rwork/Rfree 0.169/0.177 0.192/0.231 0.235/0.277
Diffuse scattering statistics
 Resolution range (Å) 93.4–1.5 75.2–2.5 69.3–2.1
 CCFriedel pairs 0.979 (0.623) 0.954 (0.695) 0.988 (0.689)
 CCunsym, Friedel-sym§ 0.996 (0.915) 0.994 (0.957) 0.997 (0.930)
 CCunsym, Laue-sym§ 0.993 (0.862) 0.984 (0.887) 0.994 (0.827)
 Completeness (%) 98.7 96.1 100.0
†Values in parentheses are for the highest resolution shell.
‡For values in parentheses, the average radial intensity was subtracted prior to applying symmetry operations.
§Correlation coefficient between the unsymmetrized map and the map after applying the indicated symmetry operations.

2.3. Diffuse scattering predictions from real-space models of disorder

Experimental diffuse scattering maps were compared with the following disorder models.

  • (i) A Gaussian elastic network model, a commonly used normal-mode decomposition of the protein motions based on the structure. Normal modes were computed using a uniform spring constant for all atom pairs within a given distance (Bray et al., 2011[Bray, J. K., Weiss, D. R. & Levitt, M. (2011). Biophys. J. 101, 2966-2969.]), and the predicted interatomic correlations were renormalized by the B factors from the refined models of the Bragg data.

  • (ii) Conformational ensemble models, which model configurational disorder as a discrete set of probability-weighted states (Guinier, 1963[Guinier, A. (1963). X-ray Diffraction in Crystals, Imperfect Crystals, and Amorphous Bodies. San Francisco: W. H. Freeman & Co.]). Conformational states were inferred by analyzing the crystal electron density from the Bragg data.

  • (iii) Rigid-body rotations, in which the atoms in an asymmetric unit rotate as a unit around a random, isotropically oriented axis with a normally distributed rotation angle (Moore, 2009[Moore, P. B. (2009). Structure, 17, 1307-1315.]).

  • (iv) Rigid-body translations, in which the atoms in an asymmetric unit translate as a unit. Translations sample an isotropic Gaussian distribution (Moore, 2009[Moore, P. B. (2009). Structure, 17, 1307-1315.]; Ayyer et al., 2016[Ayyer, K. et al. (2016). Nature (London), 530, 202-206.]).

  • (v) Liquid-like motions, in which correlations between atoms decay exponentially as a function of interatomic distance (Wall et al., 1997[Wall, M. E., Clarage, J. B. & Phillips, G. N. (1997). Structure, 5, 1599-1612.]; Clarage et al., 1992[Clarage, J. B., Clarage, M. S., Phillips, W. C., Sweet, R. M. & Caspar, D. L. (1992). Proteins, 12, 145-157.]). Two forms of this model were considered: a variant in which correlations were confined within the boundaries of the asymmetric unit and the conventional form of this model, in which correlations extend between neighboring protein molecules, thereby crossing asymmetric unit and unit-cell boundaries.

For all models except for the conventional liquid-like motions model, correlations were assumed to be confined within the boundaries of the asymmetric unit, with no coherence between neighboring molecules in the crystal lattice. For the systems considered here, the chosen asymmetric unit contained a single copy of the biological unit.

Diffuse scattering maps were simulated using Thor (Lane, 2017[Lane, T. J. (2017). Thor. https://github.com/erikhuda/thor.]), a software package for simulating and analyzing X-ray scattering experiments. For consistency with the experimental maps, the average radial intensity was subtracted from the predicted maps. Best-fit parameters for each model were determined by scanning over the disorder parameter(s) to maximize the CC with the experimental map. Agreement was assessed by the CC between the predicted and experimental maps, with each voxel downweighted by its multiplicity. For visual comparisons, a multiplicative scale factor and constant platform were applied to place the predicted maps on the same intensity scale as the experimental maps, unless otherwise noted. Disorder models are described in more mathematical detail in the Supporting Information.

3. Results

3.1. The experimental maps exhibit Laue symmetry and significant anisotropic features

We analyzed three crystallographic data sets collected by the rotation method for which diffuse scattering was visible in the raw diffraction images (Fig. 1[link], left). The Bragg data were separated and processed using standard protocols, yielding refined structural models similar to those published previously (Van Benschoten et al., 2016[Van Benschoten, A. H., Liu, L., Gonzalez, A., Brewster, A. S., Sauter, N. K., Fraser, J. S. & Wall, M. E. (2016). Proc. Natl Acad. Sci. USA, 113, 4069-4074.]; Peck et al., 2016[Peck, A., Sunden, F., Andrews, L. D., Pande, V. S. & Herschlag, D. (2016). J. Mol. Biol. 428, 2758-2768.]; Herrou et al., 2016[Herrou, J., Czyż, D. M., Willett, J. W., Kim, H. S., Chhor, G., Babnigg, G., Kim, Y. & Crosson, S. (2016). J. Bacteriol. 198, 1281-1293.]). The diffuse scattering was isolated and processed to generate three-dimensional maps in reciprocal space (Fig. 1[link], upper right panels). The maps oversample the diffuse scattering signal along each lattice direction by a factor of three relative to the Miller indices, which enables these maps to resolve correlations that extend across multiple unit cells.

[Figure 1]
Figure 1
Reciprocal-space maps from experimental diffuse scattering. The diffuse scattering in diffraction images collected for (a) CypA, (b) AP and (c) WrpA was reconstructed into a reciprocal-space map for each system. These maps take the form of three-dimensional grids that are oversampled by a factor of three relative to the Miller indices along each lattice axis. Left: an example diffraction image from each data set. Right: central slices through reciprocal space are visualized for each unsymmetrized map in the top panels. The lower panels show these slices after symmetrization of Friedel- and Laue-equivalent voxels, followed by subtraction of the average radial intensity profile to highlight anisotropic features. For the symmetrized maps, the color scales do not span the entire range of voxel intensities; this saturates a subset of voxels but improves the overall contrast.

Overall statistics for the diffuse scattering maps are shown in Table 1[link] and statistics by resolution shell are shown in Supplementary Fig. S1. The intensities of voxels related by Friedel's law and Laue symmetry showed significant correlation in all cases, supporting symmetrization of the maps by averaging the intensities of these symmetry-equivalent voxels. Currently there is no established convention for determining when the symmetrization of diffuse maps is justified, so we considered the CC between symmetry-related voxels to be significant based on the threshold value of CC1/2 used to determine resolution cutoffs for the Bragg data (Karplus & Diederichs, 2012[Karplus, P. A. & Diederichs, K. (2012). Science, 336, 1030-1033.]). Although these cases are not precisely analogous, this enhances the consistency between Bragg and diffuse data-processing techniques. To remove the intensity contributions from uncorrelated disorder, which includes solvent and air scattering in addition to uncorrelated protein disorder, the average radial intensity was subtracted from each map. The resulting maps were characterized by significant anisotropic features indicative of correlated disorder (Fig. 1[link], lower right panels).

3.2. Models of conformational dynamics do not correlate with the experimental maps

Models of intramolecular disorder that predict idiosyncratic configurational dynamics, the type of motions that are most likely to be related to biological function, were assessed for their ability to reproduce the experimental signal. A general class of these models assumes that interatomic displacements are small and sample a Gaussian distribution, and can thus be described by a covariance matrix. Here, covariance matrices were predicted from normal-mode analysis of each protein structure in torsion-angle space, using a standard form of the elastic network model that has been validated against Bragg-derived crystal structures (Bray et al., 2011[Bray, J. K., Weiss, D. R. & Levitt, M. (2011). Biophys. J. 101, 2966-2969.]). Interatomic covariances were renormalized by the refined B factors such that the predicted amplitudes of motion were consistent with the structural models of the Bragg data. These elastic network models predicted distributions of strongly covarying atom pairs that were non-uniform and often spatially localized in the protein (Fig. 2[link], left, and Supplementary Fig. S3). For all three systems, the diffuse scattering predicted by these network models was unable to reproduce the observed signal, which was apparent both in the low CC and by visual comparison of the predicted and experimental 0kl planes (Fig. 2[link]).

[Figure 2]
Figure 2
Elastic network models of Gaussian disorder. The highest and lowest magnitude entries in the covariance matrix are overlaid as blue and red cylinders, respectively, on the refined atomic coordinates for (a) CypA, (b) AP and (c) WrpA. The 0kl slices of the predicted diffuse scattering maps are shown on the right, with the overall CC noted in black. The color scales differ from Fig. 1[link] to enhance visualization of the features.

Non-Gaussian ensemble models inferred from the Bragg data were also evaluated to determine whether such conformational heterogeneity contributes measurably to the diffuse signal. In the case of CypA, multiconformer modeling of the electron-density map revealed a minor population of alternative rotamers for a series of residues that radiate from the active site (Fig. 3[link]a, left; van den Bedem et al., 2009[Bedem, H. van den, Dhanik, A., Latombe, J.-C. & Deacon, A. M. (2009). Acta Cryst. D65, 1107-1117.]). This observation of a correlated rotameric switch is consistent with prior analysis of CypA crystal structures (van den Bedem et al., 2009[Bedem, H. van den, Dhanik, A., Latombe, J.-C. & Deacon, A. M. (2009). Acta Cryst. D65, 1107-1117.]; Fraser et al., 2009[Fraser, J. S., Clarkson, M. W., Degnan, S. C., Erion, R., Kern, D. & Alber, T. (2009). Nature (London), 462, 669-673.], 2011[Fraser, J. S., van den Bedem, H., Samelson, A. J., Lang, P. T., Holton, J. M., Echols, N. & Alber, T. (2011). Proc. Natl Acad. Sci. USA, 108, 16247-16252.]; Keedy et al., 2015[Keedy, D. A. et al. (2015). Elife, 4, e07574.]). Another ensemble was generated from the loop conformations populated by residues 79–83 in CypA crystals from which data were collected at or below 180 K (Fig. 3[link]b, left; Keedy et al., 2015[Keedy, D. A. et al. (2015). Elife, 4, e07574.]). Although only one of these conformations is populated in the data set analyzed here (which was collected at 273 K), this model offers a distinct example of a type of configurational disorder that is prevalent in proteins. A third ensemble model was suggested by the occupancy disorder observed in AP, for which the Bragg coordinates were refined with a half-occupied tungstate ion in each active site. However, the Bragg data cannot distinguish between this model of partial occupancy and a model of correlated occupancy in which only one AP monomer is tungstate-bound at a given time (Fig. 3[link]c, left). Whereas partial occupancy contributes to radially symmetric diffuse scattering, correlated occupancy yields anisotropic features.

[Figure 3]
Figure 3
Ensemble models inferred from the Bragg data. (a) Multi-conformer modeling of the CypA electron-density map predicts a minor population of alternate conformers (purple) that radiate from the active site. (b) CypA data sets collected at 180 K and below show two loop conformations between residues 79 and 83; the visualized loop conformations are those modeled in the 100 K data set. Above 180 K, the conformation shown in white is not populated. (c) Model of AP assuming that one active site is tungstate-bound (left) while the other is occupied by water molecules that coordinate the active-site metal ions (right). The 0kl plane of the predicted map from each ensemble model is shown on the right, with a different color scale from Fig. 1[link] to enhance visual contrast. The overall CC between the experimental map and each predicted map is noted in black.

Diffuse scattering maps were predicted for each two-state model by Guinier's equation (Guinier, 1963[Guinier, A. (1963). X-ray Diffraction in Crystals, Imperfect Crystals, and Amorphous Bodies. San Francisco: W. H. Freeman & Co.]); the 0kl planes are shown in Fig. 3[link]. The predicted maps from the CypA ensemble models are distinct from one another, but both exhibit features spread over much broader regions of reciprocal space (owing to the short length scale of the disorder in real space) than observed experimentally (Fig. 1[link]a versus Figs. 3[link]a and 3[link]b). The map predicted by the correlated occupancy model shows a unique checkered pattern (Fig. 3[link]c), but these regular features are similarly larger than the features observed in the experimental map for AP (Fig. 1[link]c). Although diffuse scattering has been suggested as a route for validating conformational heterogeneity modeled during Bragg refinement (Moore, 2009[Moore, P. B. (2009). Structure, 17, 1307-1315.]; Wilson, 2013[Wilson, M. A. (2013). Nat. Methods, 10, 835-837.]), these ensemble models do not appreciably account for the diffuse signal in these data sets (Table 2[link]).

Table 2
Correlation coefficients between predicted and experimental maps

  Model
  Rigid-body rotations Rigid-body translations Liquid-like motions Elastic network Ensemble
CypA 0.46 0.44 0.48 | 0.71 0.17 −0.02, −0.04
AP 0.26 0.32 0.32 | 0.67 0.09 0.00§
WrpA 0.44 0.41 0.48 | 0.67 0.21
†Values before and after the vertical bar indicate the asymmetric unit-confined and `with neighbors' models, respectively.
‡Rotamer switch and disordered loop models, respectively.
§Correlated occupancy model.

3.3. Short-range rigid-body and liquid-like motions models show modest agreement with the observed signal

The inability of these elastic network and ensemble models to reproduce the experimental maps prompted us to evaluate other disorder models which predict simpler dynamics of rigid-body or liquid-like motions. As in the previous section, the models described below assume that correlations do not extend beyond the boundaries of individual asymmetric units. Additionally, the models evaluated in this section share the symmetrized molecular transform – specifically, the Fourier transform of the individual protein molecule, incoherently summed over its orientations in the unit cell – as their basis, which, as discussed below, raises the possibility of using the diffuse scattering signal for static structural inference.

The diffuse scattering predicted by rigid-body rotational disorder is related to the variance of an ensemble of rotated structure factors. Visually, this type of disorder has the effect of blurring features of the molecular transform in concentric shells of reciprocal space. An isotropic version of this model showed modest correlation with the CypA and WrpA maps (Fig. 4[link]). For both maps, the best-fit values for the standard deviation of the angle of rotation were of the order of 2–3° (Table 3[link]), consistent with a blurring effect that spans a few voxels of these reciprocal-space maps. The best-fit value for the AP map was smaller (0.9°), yielding minimal radial blurring that could be resolved by the coarseness of the map's voxels, which along with the modest correlation suggested that rotational disorder was inconsistent with the observed signal. Relative to CypA and WrpA, AP has more crystal contacts that may inhibit this type of disorder. It is also possible that the finer slicing during the collection of the AP data minimized blurring, but radial blurring owing to data collection versus as a result of rotational disorder in the crystal cannot be distinguished by the isotropic model considered here.

Table 3
Refined model parameters

  Model
  Bragg Wilson B Rigid-body translations Liquid-like motions Rigid-body rotations
  σ (Å) σ (Å) σ (Å) γ§ (Å) σ (°)
CypA 0.45 0.68 0.36 | 0.39 18 | 18 2.9
AP 0.63 0.63 0.40 | 0.48 118 | 53 0.9
WrpA 0.90 1.05 0.54 | 0.61 15 | 18 3.4
†The Bragg σ was computed from the Wilson B factor.
‡The values to the left and right of the vertical bar indicate the asymmetric unit-confined model and conventional model `with neighbors', respectively.
§The correlation length affects the volume in reciprocal space across which intensities of the crystal transform are blurred. Consequently, this parameter may be sensitive to how finely the diffuse signal is sampled. However, fitting the LLM to experimental maps constructed with oversampling by a factor of five relative to the Miller indices yielded similar values of γ, suggesting that the experimental features are sufficiently resolved with oversampling by a factor of three.
¶Prior analysis of this data set found best-fit parameters of σ = 0.38 Å and γ = 7.1 Å for the model with neighbors (Van Benschoten et al., 2016[Van Benschoten, A. H., Liu, L., Gonzalez, A., Brewster, A. S., Sauter, N. K., Fraser, J. S. & Wall, M. E. (2016). Proc. Natl Acad. Sci. USA, 113, 4069-4074.]). However, in that study the diffuse signal was sampled at integral Miller indices and diffuse halos around Bragg peaks were suppressed.
[Figure 4]
Figure 4
Comparison of models of rigid-body and liquid-like motions. For the indicated model, parameters that tune the disorder were fitted to maximize overall correlation with the experimental map. The predicted 0kl planes for the best-fit maps are shown, with the experimental 0kl planes displayed in the rightmost column for comparison. The overall CC between the experimental map and each predicted map is noted in black. As with Fig. 1, the color scales span a partial range of the voxel intensities to improve visual contrast.

By contrast, the diffuse scattering produced by rigid-body translational disorder is the molecular transform scaled by the Debye–Waller factor (Moore, 2009[Moore, P. B. (2009). Structure, 17, 1307-1315.]; Ayyer et al., 2016[Ayyer, K. et al. (2016). Nature (London), 530, 202-206.]). For all three maps, this disorder model showed nontrivial correlation with the experimental maps. Further, the best-fit values of the isotropic displacement parameter σ, which reports on the scale of displacement, were within twofold of the value predicted by the Bragg Wilson B factor (Fig. 4[link], Table 3[link]), suggesting that the diffuse signal is consistent with scattered intensity missing in the Bragg data owing to disorder. The fit was modestly improved by imposing exponential decay on interatomic covariances, thereby switching from a rigid-body to a liquid-like description of correlated dynamics (Fig. 4[link]). This model of asymmetric unit-confined liquid-like motions predicted similar though consistently smaller values for the isotropic displacement parameter and, in the case of CypA and WrpA, a correlation length roughly one third to one half the dimensions of the protein molecule (Table 3[link]). In the case of AP, the best-fit correlation length spanned the longest dimension of the protein, consistent with the lack of improvement in CC: in the regime of correlation lengths longer than the protein unit, the diffuse scattering predictions of the liquid-like motions and rigid-body translational disorder models converge.

3.4. Speckles indicate long-range correlated disorder that crosses unit-cell boundaries

Of the models considered above, in no case does the correlation coefficient between the predicted and experimental map exceed 0.5 (Table 2[link], Supplementary Fig. S4). Visual inspection suggests that a feature that these models systematically fail to reproduce is the observed `speckles': periodic spikes in intensity that appear superimposed on diffuse scattering features that span larger volumes in recip­rocal space (Figs. 5a[link] and 5b[link], insets). Such speckles arise from enhanced scattering at reciprocal-lattice positions, i.e. the estimated diffuse intensity underneath Bragg peaks, and have previously been noted in studies that analyzed the diffuse scattering signal at fractional Miller indices (Glover et al., 1991[Glover, I. D., Harris, G. W., Helliwell, J. R. & Moss, D. S. (1991). Acta Cryst. B47, 960-968.]; Polikanov & Moore, 2015[Polikanov, Y. S. & Moore, P. B. (2015). Acta Cryst. D71, 2021-2031.]; Meinhold et al., 2007[Meinhold, L., Merzel, F. & Smith, J. C. (2007). Phys. Rev. Lett. 99, 138101.]). Because the length scale of disorder in real space determines the spacing of diffuse features in reciprocal space, the need to oversample the diffuse signal relative to integral Miller indices to observe these speckles indicates that they arise from correlations that extend beyond the boundaries of a single unit cell. The models examined in prior sections assumed correlated disorder confined within asymmetric units and so were unable to generate this type of signal.

[Figure 5]
Figure 5
Speckled features of the experimental maps. The insets highlight the characteristic speckles for the 0kl planes of the (a) CypA and (b) AP maps. In (c)–(f), maps were reconstructed or computed with oversampling by a factor of five along each lattice direction relative to integral Miller indices. Data were binned into 20 resolution shells, and the median profile for the intensity as a function of δq, the distance from reciprocal-lattice sites, is shown for each shell. Intensity profiles were normalized such that the intensity value for voxels that coincided with reciprocal-lattice sites (δq = 0) was unity. For each experimental map, curves with a 1/δq2 (dashed blue) or a 8πγ3/(1 + γ2δq2)2 (dashed red) dependence, as predicted by the phonon and liquid-like motions models, respectively, were fitted to the intensity profile in the lowest resolution shell (red points). For comparison with models whose basis is the molecular transform, intensity profiles for the CypA molecular-transform map are shown in (c).

We therefore considered two models of disorder in which correlations extend across unit-cell boundaries to determine whether accounting for intermolecular correlations improved predictions of the total diffuse signal, including these speckled features. The first of these models is the traditional liquid-like motions model, in which the basis is the crystal rather than the molecular transform. For each system, this model of long-range liquid-like motions showed considerable agreement with the experimental signal, qualitatively reproducing the speckled features and quantitatively yielding the highest CC of the models considered here (Fig. 4[link], Table 2[link]). This improved correlation is observed not just at the reciprocal-lattice sites where the speckles are centered, but also at the map voxels farthest from integral Miller indices (Supplementary Fig. S4b). The refined model parameters for the two liquid-like motions models were similar in most cases (Table 3[link]), indicating that the consistent increase in CC between the short-range and long-range models resulted almost exclusively from taking into account correlations across neighbors in the crystal, which contribute to the diffuse signal throughout reciprocal space. Further support for this liquid-like motions model comes from a comparison of the predicted and experimental autocorrelation functions, from which correlation lengths in real space can be inferred. Peaks consistent with the unit-cell dimensions, and thus indicative of correlations extending across unit-cell boundaries, were observed in the autocorrelation function of each experimental map and the long-range liquid-like motions model. By contrast, models in which correlations were confined within the boundaries of the asymmetric unit did not reproduce these characteristic peaks (Supplementary Fig. S5).

An alternative model proposed to account for speckled features invokes acoustic lattice vibrations from phonon-induced inelastic scattering (Glover et al., 1991[Glover, I. D., Harris, G. W., Helliwell, J. R. & Moss, D. S. (1991). Acta Cryst. B47, 960-968.]; Polikanov & Moore, 2015[Polikanov, Y. S. & Moore, P. B. (2015). Acta Cryst. D71, 2021-2031.]; Meinhold et al., 2007[Meinhold, L., Merzel, F. & Smith, J. C. (2007). Phys. Rev. Lett. 99, 138101.]). One prediction of this model is that the diffuse intensity will decrease proportional to the square of the distance from reciprocal-lattice positions, δq. Such a trend is absent from the molecular transform and its derivative models (Fig. 5[link]c), in which disorder is confined within the boundaries of asymmetric units. Although qualitatively the phonon model accounts for the observed halos around Bragg peaks, quantitatively the experimental fall-off in intensity differs from the 1/δq2 dependence predicted for single-phonon interactions and is better fitted by the dependence predicted by the liquid-like motions kernel (Figs. 5[link]d, 5[link]e and 5[link]f, dashed blue versus red; Glover et al., 1991[Glover, I. D., Harris, G. W., Helliwell, J. R. & Moss, D. S. (1991). Acta Cryst. B47, 960-968.]; Meinhold et al., 2007[Meinhold, L., Merzel, F. & Smith, J. C. (2007). Phys. Rev. Lett. 99, 138101.]). More complex phonon models, either from extending the spectrum to include optical modes or from accounting for multiple-phonon effects, are predicted to cause the diffuse intensity to vary more slowly with distance from reciprocal-lattice sites (Glover et al., 1991[Glover, I. D., Harris, G. W., Helliwell, J. R. & Moss, D. S. (1991). Acta Cryst. B47, 960-968.]). However, there is currently no robust method for predicting the diffuse scattering produced by phonons in macromolecular crystals. In the absence of such a method and an established procedure for simulating competing acoustic modes, let alone optical modes or the effects of multiple-phonon interactions, we cannot fully assess agreement with the phonon model versus other types of long-range disorder.

4. Discussion

Here, we present a unified framework of the principal disorder models that have previously been used to interpret diffuse scattering, and compare their ability to reproduce the signal observed in three experimental data sets. Consistent with previous work, the above analysis finds that rigid-body and liquid-like motions models exhibit modest correlation with the experimental maps when correlated disorder is confined to the asymmetric unit (Ayyer et al., 2016[Ayyer, K. et al. (2016). Nature (London), 530, 202-206.]). Multiple models that predict more complex intramolecular dynamics were also considered, but showed minimal agreement with experiment (Table 2[link]). Experimentally observed speckles did not fit the profile for phonon-induced lattice dynamics but, in agreement with prior results, could largely be reproduced by the conventional form of the liquid-like motions model, in which disorder extends across neighboring asymmetric units and thus unit-cell boundaries (Wall et al., 1997[Wall, M. E., Clarage, J. B. & Phillips, G. N. (1997). Structure, 5, 1599-1612.]; Doucet & Benoit, 1987[Doucet, J. & Benoit, J.-P. (1987). Nature (London), 325, 643-646.]; Van Benschoten et al., 2016[Van Benschoten, A. H., Liu, L., Gonzalez, A., Brewster, A. S., Sauter, N. K., Fraser, J. S. & Wall, M. E. (2016). Proc. Natl Acad. Sci. USA, 113, 4069-4074.]). None of the models assessed here fully explained the experimental signal in these data sets, which represent a range of crystallographic properties and biological functions. However, all three protein systems were globular proteins, and it is possible that distinct types of disorder underlie the diffuse scattering from crystals of membrane and fibrous proteins.

Past interest in diffuse scattering has primarily stemmed from the premise that these data probe dynamics related to biological function (Wall, Adams et al., 2014[Wall, M. E., Adams, P. D., Fraser, J. S. & Sauter, N. K. (2014). Structure, 22, 182-184.]; Héry et al., 1998[Héry, S., Genest, D. & Smith, J. C. (1998). J. Mol. Biol. 279, 303-319.]; Kolatkar et al., 1994[Kolatkar, A. R., Clarage, J. B. & Phillips, G. N. (1994). Acta Cryst. D50, 210-218.]; Chacko & Phillips, 1992[Chacko, S. & Phillips, G. N. (1992). Biophys. J. 61, 1256-1266.]; Meinhold & Smith, 2005a[Meinhold, L. & Smith, J. C. (2005a). Phys. Rev. Lett. 95, 218103.],b[Meinhold, L. & Smith, J. C. (2005b). Biophys. J. 88, 2554-2563.], 2007[Meinhold, L. & Smith, J. C. (2007). Proteins, 66, 941-953.]; Wall, Van Benschoten et al., 2014[Wall, M. E., Van Benschoten, A. H., Sauter, N. K., Adams, P. D., Fraser, J. S. & Terwilliger, T. C. (2014). Proc. Natl Acad. Sci. USA, 111, 17887-17892.]; Van Benschoten et al., 2016[Van Benschoten, A. H., Liu, L., Gonzalez, A., Brewster, A. S., Sauter, N. K., Fraser, J. S. & Wall, M. E. (2016). Proc. Natl Acad. Sci. USA, 113, 4069-4074.]). However, the experimental maps showed minimal correlation with the elastic network and ensemble models assessed here. These specific models represent a limited subspace of possible models of conformational dynamics that are consistent with the Bragg data, and it is likely that refining the parameters of the models could improve agreement with the diffuse signal. However, the observation of experimental features indicative of correlations that span neighboring molecules in the crystal cautions against the assumption that the dominant signal originates from the same protein motions that occur under physiological conditions, which these models attempt to capture. Models of disorder that account for both intermolecular and intramolecular correlations will thus be needed to resolve the contributions of each to the observed signal, a prerequisite in determining whether diffuse scattering is a useful method for studying dynamics associated with biological function.

On the other hand, the ability of rigid-body and liquid-like motions models to reproduce many experimental features suggests that diffuse scattering data could in some cases be useful for resolution extension or phase retrieval. These models share the molecular or crystal transform as their basis, and thus yield a scaled or blurred image of this transform in the diffuse scattering map. The general observation that the diffuse scattering does not directly reflect the molecular or crystal transform, but at the very least the convolution of the transform with some blurring function, must be better understood and taken into account.

Extracting the molecular-transform signal will be particularly challenging for maps that exhibit enhanced scattering at reciprocal-lattice sites, as observed here. The conventional liquid-like motions model, in which correlations are not confined to the asymmetric unit but rather extend between neighboring units in the crystal, best accounted for this feature. However, in the linear approximation, this model is a convolution of the disorder-free diffraction with a kernel that is the Fourier representation of the disorder. Thus, this model reports on the crystal transform, which is nonzero only at integral Miller indices. It does not contain information about the value of the molecular transform at fractional Miller indices. Iterative phase-retrieval algorithms, such as those recently used by Ayyer et al. (2016[Ayyer, K. et al. (2016). Nature (London), 530, 202-206.]), require these non-integral oversampled measurements to uniquely determine un­measured phases (Sayre, 1952[Sayre, D. (1952). Acta Cryst. 5, 843.]). Thus, the information present in the liquid-like motions model could in principle be employed for resolution extension, but not phase retrieval. However, the liquid-like motions model is approximate, and a more rigorous treatment of crystalline disorder may enable measurement of an oversampled molecular transform from the diffuse scattering. Despite this possibility, our results call into question the practice of directly using a diffuse scattering map for either resolution extension or iterative phasing in cases that exhibit enhanced scattering at reciprocal-lattice positions, a feature observed in all three systems we studied and one that we have no physical or theoretical grounds to mask or model separately from the remainder of the diffuse signal. Precisely how to deconvolve useful signals from such maps remains an open area of investigation.

The above analysis highlights the need for new models of diffuse scattering, either to interpret biologically relevant disorder or improve structure determination. If different sources of disorder are largely uncoupled, their contributions to the diffuse scattering will be approximately additive. However, the absence of coupling between distinct types of motions is not guaranteed, particularly in the context of a crystal lattice. Thus, challenges lie ahead both in jointly modeling distinct sources of disorder and in deconvolving weak signals from dominant features. The search space for such models is intractably large, so the number of acceptable free parameters and constraints will require careful treatment. Ideally, it would be possible to assert a model that is sophisticated enough to report interesting and idiosyncratic disorder in different systems (such as functional motions), but simple enough (i.e. with few independent parameters) to infer directly from the observed data.

An alternative route is detailed forward modeling, such as molecular dynamics, which has previously been used to analyze diffuse scattering (Faure et al., 1994[Faure, P., Micu, A., Pérahia, D., Doucet, J., Smith, J. C. & Benoit, J.-P. (1994). Nat. Struct. Biol. 1, 124-128.]; Héry et al., 1998[Héry, S., Genest, D. & Smith, J. C. (1998). J. Mol. Biol. 279, 303-319.]; Wall, Van Benschoten et al., 2014[Wall, M. E., Van Benschoten, A. H., Sauter, N. K., Adams, P. D., Fraser, J. S. & Terwilliger, T. C. (2014). Proc. Natl Acad. Sci. USA, 111, 17887-17892.]). Molecular dynamics concurrently simulates multiple types of disorder, but this method does not lend itself to refining the contributions of different kinds of disorder to fit experimental data. In the common case where such simulations do not satisfactorily reproduce experimental observations, it is challenging to modify them in a principled manner so that they do. Combined with the computational expense of these methods, it seems prudent to seek simple explanations and models for analyzing diffuse scattering before comparing with atomic simulation. The incisive test of any model will come from its predictive power: confirming that a specific physical perturbation of a crystal system results in the predicted change to the diffuse signal.

5. Conclusions

Here, we investigated the physical origins of the diffuse scattering observed from three protein crystals. A comprehensive comparison of previously proposed models critically addressed the nature and length scale of the disorder underlying this signal. Multiple models of intramolecular conformational dynamics, including ensemble models inferred from the Bragg data, were unable to explain the observed diffuse scattering. While models of rigid-body and liquid-like motions of individual proteins consistently showed modest agreement with experiment, a model of extended liquid-like motions across the crystal achieved high correlations with the three data sets analyzed (CC ≃ 0.7). This analysis indicates that accounting for the intermolecular component of the disorder will be critical to successfully model this signal, which in turn is necessary to interpret diffuse scattering in order to either probe conformational dynamics or enhance static structure inference.

Supporting information


Acknowledgements

We thank Vijay Pande, Soichi Wakatsuki and Peter Moore for instructive discussions, and Dan Herschlag for the alkaline phosphatase data set. We also thank Dan Herschlag, Nozomi Ando, Michael Wall, James Fraser, Henry Chapman and Kartik Ayyer for comments on the manuscript. Michael Levitt is gratefully acknowledged for providing his normal-mode analysis code. The Stanford Research Computing Center provided the Sherlock cluster and other computational resources that facilitated this work.

Funding information

AP was funded by a National Science Foundation Graduate Research Fellowship and supported by Vijay Pande. FP acknowledges support from the National Institutes of Health (NIH), grant No. R35GM122543 (PI: Michael Levitt). TJL was supported by the LCLS directorate, SLAC National Accelerator Laboratory via the US Department of Energy Office of Basic Energy Sciences Contract No. DE-AC02-76SF00515.

References

First citationAdams, P. D. et al. (2010). Acta Cryst. D66, 213–221.  Web of Science CrossRef CAS IUCr Journals Google Scholar
First citationAyyer, K. et al. (2016). Nature (London), 530, 202–206.  Web of Science CrossRef CAS PubMed Google Scholar
First citationBedem, H. van den, Dhanik, A., Latombe, J.-C. & Deacon, A. M. (2009). Acta Cryst. D65, 1107–1117.  Web of Science CrossRef IUCr Journals Google Scholar
First citationBenoit, J.-P. & Doucet, J. (1995). Q. Rev. Biophys. 28, 131–169.  CrossRef CAS PubMed Web of Science Google Scholar
First citationBobyr, E., Lassila, J. K., Wiersma-Koch, H. I., Fenn, T. D., Lee, J. J., Nikolic-Hughes, I., Hodgson, K. O., Rees, D. C., Hedman, B. & Herschlag, D. (2012). J. Mol. Biol. 415, 102–117.  CrossRef CAS Google Scholar
First citationBoysen, H. & Adlhart, W. (1987). J. Appl. Cryst. 20, 200–209.  CrossRef CAS Web of Science IUCr Journals Google Scholar
First citationBray, J. K., Weiss, D. R. & Levitt, M. (2011). Biophys. J. 101, 2966–2969.  CrossRef CAS Google Scholar
First citationChacko, S. & Phillips, G. N. (1992). Biophys. J. 61, 1256–1266.  CrossRef PubMed CAS Web of Science Google Scholar
First citationClarage, J. B., Clarage, M. S., Phillips, W. C., Sweet, R. M. & Caspar, D. L. (1992). Proteins, 12, 145–157.  CrossRef PubMed CAS Web of Science Google Scholar
First citationDoucet, J. & Benoit, J.-P. (1987). Nature (London), 325, 643–646.  CrossRef CAS PubMed Web of Science Google Scholar
First citationEmsley, P., Lohkamp, B., Scott, W. G. & Cowtan, K. (2010). Acta Cryst. D66, 486–501.  Web of Science CrossRef CAS IUCr Journals Google Scholar
First citationFaure, P., Micu, A., Pérahia, D., Doucet, J., Smith, J. C. & Benoit, J.-P. (1994). Nat. Struct. Biol. 1, 124–128.  CrossRef CAS Web of Science Google Scholar
First citationFraser, J. S. (2015). X-ray Diffraction Data for Cyclophilin A. PDB Code 4YUO. SBGrid Data Bank v.1. https://doi.org/10.15785/SBGRID/68Google Scholar
First citationFraser, J. S., Clarkson, M. W., Degnan, S. C., Erion, R., Kern, D. & Alber, T. (2009). Nature (London), 462, 669–673.  Web of Science CrossRef PubMed CAS Google Scholar
First citationFraser, J. S., van den Bedem, H., Samelson, A. J., Lang, P. T., Holton, J. M., Echols, N. & Alber, T. (2011). Proc. Natl Acad. Sci. USA, 108, 16247–16252.  Web of Science CrossRef CAS PubMed Google Scholar
First citationGlover, I. D., Harris, G. W., Helliwell, J. R. & Moss, D. S. (1991). Acta Cryst. B47, 960–968.  CrossRef CAS Web of Science IUCr Journals Google Scholar
First citationGuinier, A. (1963). X-ray Diffraction in Crystals, Imperfect Crystals, and Amorphous Bodies. San Francisco: W. H. Freeman & Co.  Google Scholar
First citationHerrou, J. & Crosson, S. (2015). X-ray Diffraction Data for Structure of B. abortus WrbA-related Protein A (apo-WrpA). PDB Code 5F51. SBGrid Data Bank v.1. https://doi.org/10.15785/SBGRID/203Google Scholar
First citationHerrou, J., Czyż, D. M., Willett, J. W., Kim, H. S., Chhor, G., Babnigg, G., Kim, Y. & Crosson, S. (2016). J. Bacteriol. 198, 1281–1293.  CrossRef CAS Google Scholar
First citationHéry, S., Genest, D. & Smith, J. C. (1998). J. Mol. Biol. 279, 303–319.  Web of Science CrossRef PubMed Google Scholar
First citationHolton, J. (2016). nonBragg. http://bl831.als.lbl.gov/~jamesh/nonBraggGoogle Scholar
First citationHura, G., Sorenson, J. M., Glaeser, R. M. & Head-Gordon, T. (2000). J. Chem. Phys. 113, 9140.  CrossRef Google Scholar
First citationKabsch, W. (2010a). Acta Cryst. D66, 133–144.  Web of Science CrossRef CAS IUCr Journals Google Scholar
First citationKabsch, W. (2010b). Acta Cryst. D66, 125–132.  Web of Science CrossRef CAS IUCr Journals Google Scholar
First citationKarplus, P. A. & Diederichs, K. (2012). Science, 336, 1030–1033.  Web of Science CrossRef CAS PubMed Google Scholar
First citationKe, H. (1992). J. Mol. Biol. 228, 539–550.  CrossRef PubMed CAS Web of Science Google Scholar
First citationKeedy, D. A. et al. (2015). Elife, 4, e07574.  Web of Science CrossRef PubMed Google Scholar
First citationKolatkar, A. R., Clarage, J. B. & Phillips, G. N. (1994). Acta Cryst. D50, 210–218.  CrossRef CAS Web of Science IUCr Journals Google Scholar
First citationLane, T. J. (2017). Thor. https://github.com/erikhuda/thorGoogle Scholar
First citationMcCoy, A. J., Grosse-Kunstleve, R. W., Adams, P. D., Winn, M. D., Storoni, L. C. & Read, R. J. (2007). J. Appl. Cryst. 40, 658–674.  Web of Science CrossRef CAS IUCr Journals Google Scholar
First citationMeinhold, L., Merzel, F. & Smith, J. C. (2007). Phys. Rev. Lett. 99, 138101.  Web of Science CrossRef PubMed Google Scholar
First citationMeinhold, L. & Smith, J. C. (2005a). Phys. Rev. Lett. 95, 218103.  CrossRef Google Scholar
First citationMeinhold, L. & Smith, J. C. (2005b). Biophys. J. 88, 2554–2563.  CrossRef CAS Google Scholar
First citationMeinhold, L. & Smith, J. C. (2007). Proteins, 66, 941–953.  Web of Science CrossRef PubMed CAS Google Scholar
First citationMeisburger, S. P., Thomas, W. C., Watkins, M. B. & Ando, N. (2017). Chem. Rev. 117, 7615–7672.  Web of Science CrossRef CAS PubMed Google Scholar
First citationMoore, P. B. (2009). Structure, 17, 1307–1315.  Web of Science CrossRef PubMed CAS Google Scholar
First citationMurshudov, G. N., Skubák, P., Lebedev, A. A., Pannu, N. S., Steiner, R. A., Nicholls, R. A., Winn, M. D., Long, F. & Vagin, A. A. (2011). Acta Cryst. D67, 355–367.  Web of Science CrossRef CAS IUCr Journals Google Scholar
First citationPeck, A., Ressl, S. & Herschlag, D. (2017). X-ray Diffraction Data for E. coli Alkaline Phosphatase in Complex with Tungstate. PDB Code 5C66. SBGrid Data Bank v.1. https://doi.org/10.15785/SBGRID/456Google Scholar
First citationPeck, A., Sunden, F., Andrews, L. D., Pande, V. S. & Herschlag, D. (2016). J. Mol. Biol. 428, 2758–2768.  CrossRef CAS Google Scholar
First citationPolikanov, Y. S. & Moore, P. B. (2015). Acta Cryst. D71, 2021–2031.  Web of Science CrossRef IUCr Journals Google Scholar
First citationSayre, D. (1952). Acta Cryst. 5, 843.  CrossRef IUCr Journals Web of Science Google Scholar
First citationVan Benschoten, A. H., Liu, L., Gonzalez, A., Brewster, A. S., Sauter, N. K., Fraser, J. S. & Wall, M. E. (2016). Proc. Natl Acad. Sci. USA, 113, 4069–4074.  Web of Science CrossRef CAS PubMed Google Scholar
First citationWall, M. E. (2009). Methods Mol. Biol. 544, 269–279.  CrossRef PubMed CAS Google Scholar
First citationWall, M. E., Adams, P. D., Fraser, J. S. & Sauter, N. K. (2014). Structure, 22, 182–184.  Web of Science CrossRef CAS PubMed Google Scholar
First citationWall, M. E., Clarage, J. B. & Phillips, G. N. (1997). Structure, 5, 1599–1612.  Web of Science CrossRef CAS PubMed Google Scholar
First citationWall, M. E., Van Benschoten, A. H., Sauter, N. K., Adams, P. D., Fraser, J. S. & Terwilliger, T. C. (2014). Proc. Natl Acad. Sci. USA, 111, 17887–17892.  Web of Science CrossRef CAS PubMed Google Scholar
First citationWaterman, D. G., Winter, G., Gildea, R. J., Parkhurst, J. M., Brewster, A. S., Sauter, N. K. & Evans, G. (2016). Acta Cryst. D72, 558–575.  Web of Science CrossRef IUCr Journals Google Scholar
First citationWelberry, T. R. & Weber, T. (2016). Crystallogr. Rev. 22, 2–78.  Web of Science CrossRef CAS Google Scholar
First citationWilson, M. A. (2013). Nat. Methods, 10, 835–837.  CrossRef CAS Google Scholar
First citationWinter, G., Parkhurst, J., Fuentes-Montero, L., Hattne, J., Brewster, A., Sauter, N. & Evans, G. (2018). Acta Cryst. D74, 85–97.  CrossRef IUCr Journals Google Scholar

This is an open-access article distributed under the terms of the Creative Commons Attribution (CC-BY) Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited.

IUCrJ
Volume 5| Part 2| March 2018| Pages 211-222
ISSN: 2052-2525