Article

SAXS/SANS on Supercharged Proteins Reveals Residue-Specific Modifications of the Hydration Shell Henry S. Kim,1,2,3 Anne Martel,4 Eric Girard,1,2,3 Martine Moulin,4 Michael Ha¨rtlein,4 Dominique Madern,1,2,3,4 Martin Blackledge,1,2,3 Bruno Franzetti,1,2,3,4 and Frank Gabel1,2,3,4,* 1

University Grenoble Alpes, 2CNRS, and 3CEA, IBS, Grenoble, France; and 4Institut Laue-Langevin, Grenoble, France

ABSTRACT Water molecules in the immediate vicinity of biomacromolecules, including proteins, constitute a hydration layer characterized by physicochemical properties different from those of bulk water and play a vital role in the activity and stability of these structures, as well as in intermolecular interactions. Previous studies using solution scattering, crystallography, and molecular dynamics simulations have provided valuable information about the properties of these hydration shells, including modifications in density and ionic concentration. Small-angle scattering of x-rays (SAXS) and neutrons (SANS) are particularly useful and complementary techniques to study biomacromolecular hydration shells due to their sensitivity to electronic and nuclear scattering-length density fluctuations, respectively. Although several sophisticated SAXS/SANS programs have been developed recently, the impact of physicochemical surface properties on the hydration layer remains controversial, and systematic experimental data from individual biomacromolecular systems are scarce. Here, we address the impact of physicochemical surface properties on the hydration shell by a systematic SAXS/SANS study using three mutants of a single protein, green fluorescent protein (GFP), with highly variable net charge (þ36, 6, and 29). The combined analysis of our data shows that the hydration shell is locally denser in the vicinity of acidic surface residues, whereas basic and hydrophilic/hydrophobic residues only mildly modify its density. Moreover, the data demonstrate that the density modifications result from the combined effect of residue-specific recruitment of ions from the bulk in combination with water structural rearrangements in their vicinity. Finally, we find that the specific surface-charge distributions of the different GFP mutants modulate the conformational space of flexible parts of the protein.

INTRODUCTION Hydration water is an integral part of biomacromolecules and plays a crucial role in their activity and stability, as well as in intermolecular interactions (1–3). Despite its importance, gaining insight into the molecular mechanisms and structures of hydration water has been challenging and has led to conflicting descriptions, mainly due to its dynamic nature, which can be affected by various factors in the surrounding environment, including physicochemical properties of the solute molecules, as well as solvent composition, in particular, the presence of ions (4–6). High-resolution structural studies by x-ray crystallography or NMR, molecular dynamics (MD) simulations, solution scattering, and other biophysical studies have provided information on water-macromolecule interactions in specific systems

Submitted December 21, 2015, and accepted for publication April 8, 2016. *Correspondence: [email protected] Editor: James Cole. http://dx.doi.org/10.1016/j.bpj.2016.04.013

(7–10). NMR and neutron spectroscopy have revealed altered mobility of water molecules on protein surfaces with respect to the bulk (11–15), and time-resolved vibrational spectroscopy has revealed distinctive dynamics of solvation near the active sites of enzymes during catalysis with respect to bulk solvent (16). Increased water density in the first hydration shell (HS), as well as surface-specific differences in the organization of the hydration water, have been reported by MD simulations (17–20) and have been described thermodynamically in terms of electrorestriction (21). MD studies involving nucleic acids (7,22,23) or proteins (24,25), as well as anomalous x-ray solution studies on proteins and nucleic acids (26,27), have revealed the specific recruitment of ions into their HSs. Small-angle scattering of x-rays (SAXS) and neutrons (SANS) are particularly suited to probe the structural properties of hydration layers around biomacromolecules in solution (28–33). SAXS and SANS are sensitive to the electronic and nuclear scattering-length density (SLD)

Ó 2016 Biophysical Society.

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fluctuations, Dr, respectively, between solutes (i.e., biomacromolecules) and the bulk solvent (29). The scattered intensity, I(Q), from randomly oriented, solubilized particles can be calculated by using the equation * Z  2+   ~ IðQÞ ¼  Dr eiQ:~r d~ r  ; V

where Q ¼ ð4p=lÞsin q is the modulus of the scattering vec~ l is the x-ray or neutron wavelength, and 2q is the tor Q, scattering angle. The angled brackets indicate rotational averaging over all particle orientations. Importantly, the integral needs to run over the whole particle volume, V, including regions in its vicinity if they have different average SLDs from that of the bulk solvent. Mathematically, Dr can be split into a contribution from the biomacromolecule and from the hydration layer. It has been shown experimentally by SAXS/SANS that the solvent in the vicin˚ ngstroms) has an average SLD ity of proteins (within a few A different from that of the bulk, usually slightly denser, that needs to be taken into account to accurately fit protein structures in aqueous solutions (19,30). SAXS and SANS have the advantage that the respective contribution of the hydration layer (or shell) to the overall scattering from the macromolecules varies both in magnitude and sign (34). The HS contribution has been implemented in a number of programs to back-calculate SAXS (and, more rarely, SANS) curves from atomic structures, either as a homogeneous shell of a specific thickness (30–32,35), as grid elements (36,37), as dummy atoms (38,39), as explicit water molecules (40–45), as a density map (46,47), or by voxelization (48). Accurate description of the HS is essential for the growing field of quasiatomic protein-structure modeling from solution data (34). Surprisingly, as more and more programs are being developed (38), new original experimental small-angle scattering (SAS) data describing HSs are rather scarce and are limited to a small number of protein systems. In particular, systematic studies using both SAXS and SANS to probe the HS as a function of physicochemical surface properties from a single protein system have, to our knowledge, not been carried out to date. Here, we present a SAXS/SANS study of a series of highly charged mutants of green fluorescent protein (GFP; Fig. S1 in the Supporting Material) to address the influence of the surface charge on the properties of the HS. GFP (49) is perfectly suited for this approach for two major reasons: 1) it is relatively small (~27 kDa), implying a large surface/volume ratio and therefore a large relative contribution of the HS to the overall scattered signal; and 2) it can be solubilized at high concentrations (~50 mg/mL) in a stable and monodisperse state to provide a SAXS/SANS signal with a very good signal/noise ratio. To probe the impact of protein surface charge on structural properties of HSs, we recorded

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a set of SAXS and SANS curves from natural abundance and deuterium-labeled GFP mutants (net charges þ36, 6, and 29) at variable H2O/D2O ratios in solution. Care was taken to ensure sample quality in solution, especially with respect to monodispersity and ideality, which are paramount aspects of accurate SAXS/SANS data interpretation in terms of form factors of individual particles (50,51). Full-length GFP models including two flexible tails of a total of 14 residues (10 at the C- and 4 at the N-terminus (52)) were generated using the statistical coil algorithm flexible-meccano (53), and SAXS data were subsequently analyzed by several programs: CRYSOL (35), FOXS (39), and GENFIT (32). CRYSON (30) and GENFIT (32) were used to perform the SANS data analyses. Our model-free data analysis provides clear evidence that the hydrationshell density depends strongly on the amount and sign of the surface charges, with a strong local increase around negatively charged residues but only a moderate increase or decrease in density around positive and neutral residues. The results of an integrative analysis revealed the nature of the density modification as a combined effect of local ion accumulation and structural rearrangement of water molecules. Finally, our data reveal a specific modification of the conformational space of the flexible regions as a function of the surface charges. MATERIALS AND METHODS Plasmid construction and protein purification The three GFP constructs (Fig. S1) were designed based on super-charged GFPs (54). All three were synthesized and cloned into pET30a(þ) vectors (GeneCust), expressed as His-GFP fusion proteins in Escherichia coli BL21(DE3), and purified according to previously established protocols (52,54), with minor modifications. Full details are provided in the Supporting Material.

Preparation of deuterated GFPs Expression of partially deuterated (‘‘match-out labeled’’) GFPs (dGFPs) for neutron scattering was carried out by the Institut Laue-Langevin (ILL) Deuteration Laboratory (D-Lab), Grenoble, France, according to previously established protocols (55). Full details are provided in the Supporting Material.

Biophysical sample characterization The purity and monodispersity of each protein were assessed by N-terminal protein sequence determination, sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE), and analytical ultracentrifugation in velocity sedimentation mode (Fig. 1). Full details are provided in the Supporting Material.

SAXS/SANS experiments and data reduction The measurements were performed at the European Synchrotron Radiation Facility BioSAXS beamline BM29 (Grenoble, France), using a 2D Pilatus ˚ . Data processing and detector at an x-ray wavelength of l ¼ 1.008 A

Protein Hydration Shell by SAXS/SANS

A

kDa Std. GFP(-6) GFP(-29) GFP(+36)

Integrative SAXS/SANS data analysis

130 100 70

To correlate the physicochemical surface properties of the three GFP mutants with the densities of their respective HSs provided by different SAXS/SANS programs from fits of the experimental data (Table 1; Table S3), we grouped the surface amino acid residues into three classes: 1) acidic residues (Asp and Glu), 2) basic residues (Lys and Arg), and 3) neutral residues (hydrophobic/hydrophilic). Next, we postulated that each of the three surface amino acid classes affects the HS density in its ˚ ) in a specific way that can be described by a factor fj ( j ¼ vicinity (~3 A acidic, basic, or neutral). This factor is defined as 1 for bulk solvent; fj < 1 signifies a local reduction of the HS density, whereas fj > 1 is equivalent to a density increase. The respective data sets from the three GFP mutants then yield a system of three linear equations with three unknown factors:

55 40 35 25 26.9 kDa 26.9 kDa 27.6 kDa

15

GFPð6Þ

10

SDS-PAGE (15%) B

GFPð6Þ

GFPð6Þ

GFPð6Þ

Ntotal ¼ facidic Nacidic þ fbasic Nbasic þ fneutral Nneutral GFPð29Þ GFPð29Þ GFPð29Þ GFPð29Þ Ntotal ¼ facidic Nacidic þ fbasic Nbasic þ fneutral Nneutral ; GFPðþ36Þ GFPðþ36Þ GFPðþ36Þ GFPðþ36Þ ¼ facidic Nacidic þ fbasic Nbasic þ fneutral Nneutral Ntotal (1) GFPðiÞ

where Ntotal (i ¼ 6, 29, or þ36) are the total number of electrons (or ˚ HS around the proteins. They can be neutron scattering lengths) in a 3 A calculated from the fits of the experimental data with SAXS/SANS programs (which provide relative changes of the HS densities, Dr; Table 1), according to the equation GFPðiÞ

Ntotal

¼ ð1 þ Dr=rbulk ÞSrbulk 3  A:

(2)

Here, S is the total protein surface of each protein mutant (Table S2), and rbulk are the bulk solvent (150 mM NaCl) densities for x-rays ˚ 3) or neutrons (6.39  1010 cm2 in 100% D2O), calculated (0.3357 e/A GFPðiÞ from the literature (60,61). Nj ( j ¼ acidic, basic, or neutral) are the

FIGURE 1 Characterization of GFP proteins used in the SAS experiments. (A) SDS-PAGE (15%) gel. Std., protein standard, with molecular masses (kDa) indicated. (B) Sedimentation coefficient, c(s). To see this figure in color, go online.

reduction were performed using an automated standard European Synchrotron Radiation Facility beamline software (BSxCuBE) (56) and PRIMUS (57). To eliminate interparticle effects, data from different concentrations were merged (see example in Fig. S2). SANS experiments were carried out on the small-angle diffractometer D22 at the ILL (Grenoble, France) at 1.4 m/1.4 m and 4 m/4 m setups (collimation length/sample-detector ˚ . SANS data reduction distances) with a fixed neutron wavelength (l) of 6 A was performed in analogy to SAXS data, using the ILL software GRASP, version 3.4 (http://www.ill.eu/instruments-support/instruments-groups/ groups/lss/grasp/home/) and PRIMUS (57). Full details are provided in the Supporting Material.

TABLE 1 SAXS and SANS Parameters of the GFP Mutants from Fitting with CRYSOL/CRYSON

Molecular mass (kDa) (theoretical)

GFP(6)

GFP(29)

GFP(þ36)

26.9

26.9

27.6

23.8–31.7

23.3–31.0

23.0–30.6

20.4 5 0.1 1.96 5.00 5 0.57

19.3 5 0.1 1.20 8.80 5 0.62

19.1 5 0.1 0.78 4.13 5 0.31

18.4 5 0.2 1.63 0.00 5 0.00

16.9 5 0.1 1.18 2.65 5 0.60

18.4 5 0.1 2.63 0.27 5 0.47

19.8 5 0.6 1.15 (0.00 5 0.00)

19.5 5 3.1 0.48 (0.00 5 0.00)

19.4 5 0.7 0.65 (0.00 5 0.00)

20.9 5 4.0 0.53 0.95 5 0.35

N.D. N.D. N.D.

23.4 5 2.8 0.98 0.00 5 0.00

SAXS Molecular mass (kDa) (experimental) ˚) RG (Guinier) (A c HS Dr=rbulk (%) SANS (100%) ˚) RG (Guinier) (A c HS Dr=rbulk (%) SANS (8%)

Generation of GFP models and ensemble structures Superfolder GFP (sfGFP) (52) was chosen as the starting model as it shares >98% sequence similarity with GFP(6) and served to construct all GFP structures with MODELER (58). This procedure assumes, in the absence of available crystallographic structures of the highly charged mutants, an identical, rigid core structure (b-barrel fold) for all models. A pool of 10,000 conformers with flexible tail conformations were generated for each of the three GFP mutants using an ensemble generation algorithm flexiblemeccano (53,59). Full details are provided in the Supporting Material.

˚) Rg (Guinier) (A c HS Dr=rbulk (%) d-SANS (100%) ˚) Rg (Guinier) (A c HS Dr=rbulk (%)

RG and HS Dr=rbulk values are expressed as the mean 5 SE. HS density errors are from the top 10 structures.

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Kim et al. ˚ thick layer assonumber of electrons (or neutron scattering lengths) in a 3 A ciated with the relative surfaces, Sj, of each class of surface amino acids, assuming bulk density: GFPðiÞ

Nj

¼ Sj rbulk 3  A:

(3)

All surface areas were calculated from the atomic models with the program PyMOL (62) using the ‘‘get_area’’ command (http://www.pymolwiki.org/ index.php/Get_Area) and are reported in Table S2. ˚ hyIn summary, Eq. 1 attributes a specific ‘‘weighing’’ factor to the 3 A dration layer on top of each specific class of surface amino acid residues to describe three measured SAXS (or SANS) data sets simultaneously. χ

RESULTS Biophysical characterization and sample quality The accuracy and validity of analyses from SAS data rely significantly on the quality of the scattering curves but also on well-defined sample states, in particular on their monodispersity and ideality (50). To assure the highest sample quality, SDS-PAGE, N-terminal sequencing, analytical ultracentrifugation, as well as SAS experiments at different concentrations, were performed. Single bands in all gels, single, sharp peaks in the sedimentation coefficient profiles, and a unique five-residue N-terminal sequence in all cases demonstrated that our final protein samples were pure, monodisperse, and in a monomeric state (Fig. 1). We combined scattering curves from samples of low concentration at small angles with those of high concentration at wide angles (Table S1), which yielded interparticle-free curves with very good signal/noise ratios that represent form factors of individual proteins in solution (Fig. S2) and at the same time are very discriminative between structural models. Conformational ensemble analysis of the SAXS data All GFP protein constructs used in this study contained flexible regions of 14 residues (10 at the C-termini and 4 at the N-termini) that are not resolved in the homologous GFP crystal structure (52). We therefore adopted an ensemble generation algorithm, flexible-meccano (53,59), to account for them by creating a pool of conformers with different tail conformations that were fitted to the experimental SAXS data using CRYSOL (35). Variability of the tail conformers was significant in terms of c2 values, which increased by up to 10-fold between the best and the worst fits. Fig. 2 shows the 10 best ensemble structures, from which the final, best GFP structural models were chosen (Fig. 3). Importantly, in the case of GFP(þ36), the conformational space of the tail was very ample and a multitude of structures with varied conformations were in very good agreement with the experimental SAXS data. The spread of the tail conformations of the GFP(6) and GFP(29) constructs, on the other hand, was more restricted and only extended tail conformations with maximum distance

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χ χ

FIGURE 2 Top 30 GFP structures with c < 2.2 (CRYSOL). The top 10 GFP conformers for each of the three mutants are superposed. The structured region of all three proteins is shown in gray, with the chromophore shown as sticks in green. The unstructured flexible tail region is shown in gray, red, and blue for GFP(6), GFP(29), and GFP(þ36), respectively. The image was produced using PyMOL (62). To see this figure in color, go online.

to the protein surface yielded acceptable fits with the experimental SAXS data. Individual and combined SAXS and SANS analyses of HSs The three natural-abundance (hydrogenated) GFP mutants were measured in H2O solvent yielding interparticlefree and good signal/noise-ratio SAXS data sets (Fig. 3; Fig. S2). Molecular mass estimates were in very good agreement with theoretical values of a monomeric state in each case (Table 1). The RG values for the three proteins were very similar, consistent with their close structural similarities. The theoretical scattering curves of the best conformers of the ensemble were in very good agreement with the experimental SAXS data (Figs. 2 and 3). The quality of the c2 fits, as well as the optimal density changes of the HSs, of the respective proteins with respect to the bulk varied slightly depending on the type of program used to fit them (Fig. S4; Table S3). Interestingly, values of the HS changes for the highly negatively charged GFP(29) were found to lie between 9% and 15% (Table S3), which is significantly higher than those of the near-neutral GFP(6) (4–7%) and of the highly positively charged GFP (þ36) (3–5%), indicating that the electronic density of the HS increases with respect to the bulk as the protein surface acquires a more acidic character. To estimate the impact of the flexible N- and C-termini on the fit parameters, we cut them away in silico and fitted the artificially truncated models against the SAXS data (Fig. S5). The results indicate that the flexible parts are needed to yield a

Protein Hydration Shell by SAXS/SANS

A

χ

χ

χ

χ B

χ

FIGURE 3 Fitted SAXS and SANS curves (intensity versus scattering vector, Q) together with surface-charge representations (red, negatively charged residues; gray, neutral; blue, positively charged residues) for the three constructs: (A) GFP(6), (B) GFP(þ36), and (C) GFP(29). For the purpose of presentation, I(Q) was scaled for GFP(6) SAXS (0.05), GFP(6) d-SANS (0.2), GFP(29) SANS (8% D2O) (50), GFP(þ36) SANS (8% D2O) (20), and d-SANS (2). To see this figure in color, go online.

χ

χ χ C

χ

χ χ

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satisfactory fit but that the HS densities are not sensitive to their presence. Finally, the HS densities did not vary a lot between the top 10 structures of each construct (Table 1) nor between the structures with poorer fits, which is a strong argument that the HS shell values are stable and independent from the specific terminus conformations. SANS data for the three hydrogenated GFP mutants were measured under different solvent contrast conditions of 8% D2O (coherent water SLD ¼ 0, i.e., equivalent to measuring proteins in vacuo (63)) and 100% D2O, where the influence of HS density on scattering curves is much more pronounced and of opposite contrast with respect to x-rays (34). Partially deuterated GFP(6) and GFP(þ36) were also measured in 100% D2O (d-SANS), their theoretical match point (i.e., where GFP and solvent have the same average SLD), with the advantage of minimal incoherent solvent background. The RG values of all three GFPs in 100% D2O were smaller than their counterparts determined ˚ . This is consistent by SAXS or SANS in 8% D2O by 1–3 A with a denser HS, in agreement with previous findings in other systems (30,34). The quality of the fits was very good and was similar among the three GFPs (Fig. 3). As expected (63), the optimal density variation of the HS was 0 in 8% D2O (in vacuo) with respect to bulk (Table 1). In 100% D2O (SANS data sets with the best signal/noise ratio), the HS of the three GFP mutants followed the pattern observed in SAXS. Importantly, all SAXS and SANS data sets recorded could be fitted in a satisfactory manner with CRYSOL/CRYSON, which demonstrates that the model of a homogeneous HS is a good first approximation. The d-SANS data in 100% D2O differ qualitatively, and the more concentrated GFP(6) sample displays an early minimum at low angles that is reminiscent of the scattering pattern of a hollow sphere. It can be qualitatively explained by a strong contribution of the HS with respect to a contrastmatched protein. The absence of this minimum in the GFP(þ36) d-SANS data might be due either to its lower signal/noise ratio or to a slightly different degree of deuteration (~2–3%) in the latter sample resulting in imperfect matching conditions. Interestingly, the overall comparison between all data reveals that the absolute values of the SANS (100% D2O) HS densities were reduced by 4–6 percentage points with respect to the SAXS values (Table 1). In a combined analysis of the three SAXS (SANS) data sets, we assigned a specific, modified local density (with respect to the bulk) to each of the three classes of surface residues (acidic, basic, and neutral (i.e., hydrophobic and hydrophilic)). As a result, the combined analysis from SAXS (SANS) data from all three mutants yielded a system of three linear equations that describes the relationships between the residue-specific HS densities and the globally measured HS densities from the SAXS or SANS data (Eqs. 1–3 and Materials and Methods). Using the numerical values for the surfaces corresponding to the three classes of residues (Table S2) and the global electronic density varia-

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tions determined with CRYSOL from the SAXS data (Table 1), Eq. 1 yielded fbasic ¼ 1.07 (1.05–1.11), facidic ¼ 1.33 (1.24–1.43), and fneutral ¼ 0.94 (0.88–1.00), i.e., average electronic density increases of ~7% and 33% for basic and acidic residues, respectively, whereas neutral residues displayed a 6% decrease (the values in parentheses indicate error intervals from a statistical analysis described in detail in Table S4). An analogous calculation using the CRYSON fits of the SANS data in 100% D2O (Table 1) yielded fbasic ¼ 1.04 (1.02–1.05), facidic ¼ 1.20 (1.16– 1.24), and fneutral ¼ 0.91 (0.89–0.92), i.e., the neutron scattering length densities increased by ~4% and 20% around basic and acidic residues, respectively, but decreased by 9% around neutral residues. DISCUSSION Flexible regions are specifically modulated by the protein surface charge Scoring of 10,000 conformers against our SAXS data with CRYSOL indicated that the best structures cluster and display stretched, elongated C-terminus conformations pointing away from the GFP surface in the case of the GFP(6) and GFP(29) constructs, whereas the respective conformations are much more varied and spread in different directions in the case of GFP(þ36) (Fig. 2). Such a differential behavior can be understood qualitatively by the charge distributions of the respective C-termini with respect to the charge distributions of the GFP surface patches in their vicinity (Fig. 3). The GFP(þ36) C-terminus, on the one hand, is partly negatively charged, whereas the surface in its vicinity is positively charged. The GFP(29) C-terminus and the surrounding surface residues, on the other hand, are all mainly negatively charged. Therefore, in the case of GFP(þ36), one observes attraction of the C-terminus toward the surface, whereas in the case of GFP(29), an overall electrostatic repulsion prevails. In the case of GFP(6), the situation is more complex, with a mixture of both positive and negative charges on the C-terminus and on the GFP surface in proximity to it. In particular, the conformations of the C-termini of GFP(6) are the most distal ones with respect to the protein center and explain its slightly larger radius of gyration with respect to the two other constructs (Table 1). In conclusion, our results suggest that the conformational space of flexible parts is influenced by surface-charge distributions, a mechanism that has also been recently observed and quantified in flexible multidomain proteins and related to functionally important conformations (64). Protein HS density and ionic composition depend on the surface amino acid composition The structural properties of protein HSs are complex in nature, and density variations have been attributed to amino

Protein Hydration Shell by SAXS/SANS

acid composition (65). Previous SAXS/SANS studies demonstrated that HSs of increased density with respect to the bulk solvent needed to be accounted for to reconcile calculated SAS curves from atomic Protein Data Bank models with experimentally measured scattering curves (30). These observations have been addressed by successive MD simulations on lysozyme (19) that attributed two-thirds of the increase to geometrical effects and one-third to perturbations of the average water structure (in equal parts due to shortening of the O-O distances and an increase in the coordination number). The simulations predicted an increased density around surface residues with net charges, even though the overall effect was small due to their scarcity (5% of surface residues) in the lysozyme system studied. To disentangle the relative contributions from geometrical/topological and physicochemical (in particular, charge) properties of protein surfaces, we recorded SAXS and SANS data from three different mutants of the same protein (GFP) with near-neutral, highly positive, and highly negative surfaces. Since SAXS and SANS measure electronic and neutron scattering length densities (SLDs), respectively (29), our combined approach allowed us to distinguish contributions that are due to structural rearrangements of water molecules from those that are due to variations in ion concentrations in the HS. The most striking result is the correlation of the HS densities with the surfacecharge properties of the mutants; the highly negatively charged (acidic) mutant displays the strongest increase, and the highly positively charged (basic) mutant displays the smallest increase (Table 1). Interestingly, the same tendency was observed for both x-rays and neutrons, even though the SANS neutron SLDs (in 100% D2O) were smaller by 4–6% than the SAXS electronic SLDs. The systematic deviation between SAXS and SANS data, observed for all mutants, excludes the possibility that the effect is due exclusively to structural properties of water molecules in the vicinity of protein surfaces. If this were the case, the relative HS density increases would have been identical for SAXS and SANS for a given mutant, since the electronic and neutron SLDs are both directly proportional to the physical density of water molecules. The systematic decrease of the SANS values can, however, be explained both qualitatively and quantitatively by an increased local concentration of ions in the HS with respect to the bulk: Naþ and Cl ions contain the same number or more electrons than a water molecule and therefore contribute strongly to SAXS, whereas they contribute only weakly (with respect to a water molecule) to SANS in 100% D2O (Fig. S3). Indeed, the relative decrease of neutrons with respect to x-ray SLDs can be used to estimate the average increase of ion concentrations in the HS with respect to the bulk: from tabulated data of aqueous NaCl solutions (60), an increase from 150 mM to 1 M (2 M) NaCl results in an increase of 2.8% (5.7%) for the electronic

SLD but in a 0.5% (1.3%) decrease of the neutron SLD in 100% D2O. The observed shift of 4–6% between the SAXS and the 100% D2O SANS data is therefore quantitatively equivalent to an average ion concentration of ~1.5 M NaCl in the HS. Interestingly, this 10-fold increase is not far from the sixfold increase of chloride ions predicted by MD ˚ -thick layer (V ~ 40,000 A ˚ 3 for simulations (66). For a 3-A GFP mutants studied here; Table S2), these values correspond to ~35–40 Naþ and Cl ions in the HS, which matches roughly one sodium and one chloride ion per acidic and basic surface residue (Fig. S1). Even though it is convenient to estimate an average ion concentration, the systematic shift of HS SLDs between the SAXS and SANS data does not necessarily originate from an exact equimolar number of cations and anions in the HS, nor are ions likely to be the only reason for an increased density. Indeed, our SAXS data indicate that negatively charged surface residues increase the electronic SLD locally much more than do positively charged or neutral ones. Since sodium ions contain fewer electrons than chloride ions, our SAXS results suggest that water molecules are ‘‘contracting’’ locally more around acidic than around basic surface residues and that protein HS properties are a combined result of a residue-specific ionic interaction and structural changes of water molecules in their vicinity. Indeed, an important factor not taken into account by most SAXS/ SANS programs is the contribution of solvent ions in the vicinity of proteins, which may display concentrations that vary significantly from those in the bulk as a function of the local physicochemical protein surface properties (24,25). Recent MD simulations suggest that ions may either bind specifically and locally to oppositely charged protein surface residues (67,68) or accumulate at nonpolar surface patches (66) as a function of type (i.e., anions/cations) and size, an observation supported by SANS experiments with varying salt concentrations and type (5,33). Moreover, both MD simulations and neutron liquid diffraction show that different ions in solution affect the local structure of water molecules (e.g., coordination numbers and orientations) specifically (69,70) so that the overall effect near protein surfaces is most probably a combination of variable local charge concentrations and, concomitantly, density variations of the water molecules with respect to the bulk solvent as a function of ion type and size (71,72). The combined SAXS/SANS data presented here quantify, in a model-free way, the local variations in electronic and neutron SLDs as a function of the character and number of amino acid residues on a protein surface. Together, they indicate that basic and neutral (hydrophilic/hydrophobic) surface residues have only moderate effects on local HS densities, whereas acidic residues recruit hydrated Naþ ions specifically and, concomitantly, modify water structural networks in their vicinity, a phenomenon supported by recent crystallography results from acidic halophilic proteins (73).

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CONCLUSIONS Our results indicate a complex modification of protein HSs, consisting of a combined effect of specific modifications of local ion concentrations and water structural modifications that depend strongly on the protein surface-charge distribution. Although all SAXS/SANS programs applied here used a homogeneous HS for fitting and produced the same qualitative results (i.e., increasing density of the HS with increasing acidity of the protein surface), we observed minor quantitative differences in the respective c values and curve fits (in particular at higher angles), as well as in the exact numerical values of the HS densities (Table S3; Fig. S4). Together, our data indicate that a more detailed and heterogeneous description of the HS may be needed to accurately describe high-quality SAS data, in agreement with recent theoretical considerations (74). Interestingly, the importance of ions, revealed by our comparative SAXS/SANS analysis, suggests a strategy to improve existing programs invoking explicit solvent (40–45,75,76) by incorporating specific interactions of ions with charged surface residues. Further developments of SAXS/SANS programs are particularly crucial for biological macromolecular systems with complex and/or polyelectrolyte surfaces such as RNA/DNA molecules (27,77–79), intrinsically disordered proteins (59,80–82), and membrane protein/detergent complexes (83–85) that all rely strongly on an accurate description of the HS.

SUPPORTING MATERIAL Supporting Materials and Methods, five figures, and four tables are available at http://www.biophysj.org/biophysj/supplemental/S0006-3495(16) 30176-X.

AUTHOR CONTRIBUTIONS H.S.K. performed research, analyzed data, and wrote the article, A.M., E.G., M.M., M.H., D.M., M.B., and B.F. performed and supervised research, and F.G. designed and performed research, analyzed data, and wrote the article.

ACKNOWLEDGMENTS We thank local contacts Dr. A. Round and Dr. P. Pernot at the European Synchrotron Radiation Facility BioSAXS beamline BM29 for assistance during the experiments and the European Synchrotron Radiation Facility for MX BAG beamtime. We thank Dr. Liu and his group at Harvard University for generously providing plasmids of the GFP constructs used as starting points for the final modified constructs used in this work. We thank J.-P. Andrieu, from the Institut de Biologie Structurale (IBS) platform of the Partnership for Structural Biology (PSB) and the IBS in Grenoble (PSB/IBS), for assistance and for access to the Protein Sequencing Facility. We thank A. Le Roy and Dr. C. Ebel for assistance and for access to the Analytical Ultracentrifugation platform. Dr. G. Zaccai is acknowledged for critical comments on the manuscript.

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This work also used the platforms of the Grenoble Instruct Centre (ISBG; UMS 3518 CNRS-CEA-UJF-EMBL) with support from FRISBI (ANR10-INSB-05-02) and GRAL (ANR-10-LABX-49-01) within the Grenoble PSB. M.M. and M.H. acknowledge support from the Engineering and Physical Sciences Research Centre (EP/C015452/1) to V. T. Forsyth (Keele University, Staffordshire, UK) for the creation of the Deuteration Laboratory within ILL’s Life Sciences Group and from the European Union under contract RII3-CT-2003-505925. Finally, the authors acknowledge financial support from the French Agence Nationale de la Recherche via grant ANR11-JSV5-003-01 HYDROSAS.

REFERENCES 1. Makarov, V., B. M. Pettitt, and M. Feig. 2002. Solvation and hydration of proteins and nucleic acids: a theoretical view of simulation and experiment. Acc. Chem. Res. 35:376–384. 2. Raschke, T. M. 2006. Water structure and interactions with protein surfaces. Curr. Opin. Struct. Biol. 16:152–159. 3. Smith, J. C., F. Merzel, ., S. Fischer. 2004. Structure, dynamics and reactions of protein hydration water. Philos. Trans. R. Soc. Lond. B Biol. Sci. 359:1181–1189, discussion 1189–1190. 4. Gokarn, Y. R., R. M. Fesinmeyer, ., D. N. Brems. 2011. Effective charge measurements reveal selective and preferential accumulation of anions, but not cations, at the protein surface in dilute salt solutions. Protein Sci. 20:580–587. 5. Zaccai, G. 2013. Hydration shells with a pinch of salt. Biopolymers. 99:233–238. 6. Zhang, Y., and P. S. Cremer. 2006. Interactions between macromolecules and ions: the Hofmeister series. Curr. Opin. Chem. Biol. 10:658–663. 7. Auffinger, P., and Y. Hashem. 2007. Nucleic acid solvation: from outside to insight. Curr. Opin. Struct. Biol. 17:325–333. 8. Halle, B. 2004. Protein hydration dynamics in solution: a critical survey. Philos. Trans. R. Soc. Lond. B Biol. Sci. 359:1207–1223, discussion 1223–1224, 1323–1328. 9. Nakasako, M. 2004. Water-protein interactions from high-resolution protein crystallography. Philos. Trans. R. Soc. Lond. B Biol. Sci. 359:1191–1204, discussion 1204–1206. 10. Perkins, S. J. 2001. X-ray and neutron scattering analyses of hydration shells: a molecular interpretation based on sequence predictions and modelling fits. Biophys. Chem. 93:129–139. 11. Denisov, V. P., and B. Halle. 1995. Protein hydration dynamics in aqueous solution: a comparison of bovine pancreatic trypsin inhibitor and ubiquitin by oxygen-17 spin relaxation dispersion. J. Mol. Biol. 245:682–697. 12. Modig, K., E. Liepinsh, ., B. Halle. 2004. Dynamics of protein and peptide hydration. J. Am. Chem. Soc. 126:102–114. 13. Song, J., J. Franck, ., S. Han. 2014. Specific ions modulate diffusion dynamics of hydration water on lipid membrane surfaces. J. Am. Chem. Soc. 136:2642–2649. 14. Fichou, Y., G. Schiro`, ., M. Weik. 2015. Hydration water mobility is enhanced around tau amyloid fibers. Proc. Natl. Acad. Sci. USA. 112:6365–6370. 15. Schiro`, G., Y. Fichou, ., M. Weik. 2015. Translational diffusion of hydration water correlates with functional motions in folded and intrinsically disordered proteins. Nat. Commun. 6:6490. 16. Jha, S. K., M. Ji, ., S. G. Boxer. 2012. Site-specific measurement of water dynamics in the substrate pocket of ketosteroid isomerase using time-resolved vibrational spectroscopy. J. Phys. Chem. B. 116:11414– 11421. 17. Head-Gordon, T., J. M. Sorenson, ., R. M. Glaeser. 1997. Differences in hydration structure near hydrophobic and hydrophilic amino acids. Biophys. J. 73:2106–2115.

Protein Hydration Shell by SAXS/SANS 18. Kovacs, H., A. E. Mark, and W. F. van Gunsteren. 1997. Solvent structure at a hydrophobic protein surface. Proteins. 27:395–404. 19. Merzel, F., and J. C. Smith. 2002. Is the first hydration shell of lysozyme of higher density than bulk water? Proc. Natl. Acad. Sci. USA. 99:5378–5383. 20. Merzel, F., and J. C. Smith. 2005. High-density hydration layer of lysozymes: molecular dynamics decomposition of solution scattering data. J. Chem. Inf. Model. 45:1593–1599. 21. Danielewicz-Ferchmin, I., and A. R. Ferchmin. 2010. Link between the hydration enthalpy of lysozyme and the density of its hydration water: electrostriction. Phys. Chem. Chem. Phys. PCCP. 12:11299–11307. 22. Auffinger, P., and E. Westhof. 2000. Water and ion binding around RNA and DNA (C,G) oligomers. J. Mol. Biol. 300:1113–1131. 23. Feig, M., and B. M. Pettitt. 1999. Sodium and chlorine ions as part of the DNA solvation shell. Biophys. J. 77:1769–1781. 24. Jungwirth, P., and B. Winter. 2008. Ions at aqueous interfaces: from water surface to hydrated proteins. Annu. Rev. Phys. Chem. 59:343–366. 25. Collins, K. D. 1997. Charge density-dependent strength of hydration and biological structure. Biophys. J. 72:65–76. 26. Stuhrmann, H. B. 1981. Anomalous small angle scattering. Q. Rev. Biophys. 14:433–460. 27. Pollack, L. 2011. SAXS studies of ion-nucleic acid interactions. Annu. Rev. Biophys. 40:225–242. 28. Zhang, F., F. Roosen-Runge, ., F. Schreiber. 2012. Hydration and interactions in protein solutions containing concentrated electrolytes studied by small-angle scattering. Phys. Chem. Chem. Phys. 14:2483–2493. 29. Svergun, D. I., M. H. J. Koch, ., R. P. May. 2013. Small Angle X-Ray and Neutron Scattering from Solutions of Biological Macromolecules. Oxford University Press, Oxford, United Kingdom.

41. Yang, S., S. Park, ., B. Roux. 2009. A rapid coarse residue-based computational method for x-ray solution scattering characterization of protein folds and multiple conformational states of large protein complexes. Biophys. J. 96:4449–4463. 42. Park, S., J. P. Bardhan, ., L. Makowski. 2009. Simulated x-ray scattering of protein solutions using explicit-solvent models. J. Chem. Phys. 130:134114. 43. Merzel, F., and J. C. Smith. 2002. SASSIM: a method for calculating small-angle X-ray and neutron scattering and the associated molecular envelope from explicit-atom models of solvated proteins. Acta Crystallogr. D Biol. Crystallogr. 58:242–249. 44. Chen, P. C., and J. S. Hub. 2015. Interpretation of solution x-ray scattering by explicit-solvent molecular dynamics. Biophys. J. 108:2573– 2584. 45. Ravikumar, K. M., W. Huang, and S. Yang. 2013. Fast-SAXS-pro: A unified approach to computing SAXS profiles of DNA, RNA, protein, and their complexes. J. Chem. Phys. 138:024112. 46. Poitevin, F., H. Orland, ., M. Delarue. 2011. AquaSAXS: A web server for computation and fitting of SAXS profiles with a non-uniform hydration layer. Nucleic Acids Res. 39:W184–W189. 47. Virtanen, J. J., L. Makowski, ., K. F. Freed. 2011. Modeling the hydration layer around proteins: applications to small- and wide-angle x-ray scattering. Biophys. J. 101:2061–2069. 48. Liu, H., A. Hexemer, and P. H. Zwart. 2012. The Small Angle Scattering ToolBox (SASTBX): an open-source software for biomolecular small-angle scattering. J. Appl. Crystallogr. 45:587–593. 49. Tsien, R. Y. 1998. The green fluorescent protein. Annu. Rev. Biochem. 67:509–544. 50. Jacques, D. A., and J. Trewhella. 2010. Small-angle scattering for structural biology—expanding the frontier while avoiding the pitfalls. Protein Sci. 19:642–657.

30. Svergun, D. I., S. Richard, ., G. Zaccai. 1998. Protein hydration in solution: experimental observation by x-ray and neutron scattering. Proc. Natl. Acad. Sci. USA. 95:2267–2272.

51. Jacques, D. A., J. M. Guss, ., J. Trewhella. 2012. Publication guidelines for structural modelling of small-angle scattering data from biomolecules in solution. Acta Crystallogr. D Biol. Crystallogr. 68:620–626.

31. Sinibaldi, R., M. G. Ortore, ., P. Mariani. 2008. SANS/SAXS study of the BSA solvation properties in aqueous urea solutions via a global fit approach. Eur. Biophys. J. 37:673–681.

52. Pe´delacq, J.-D., S. Cabantous, ., G. S. Waldo. 2006. Engineering and characterization of a superfolder green fluorescent protein. Nat. Biotechnol. 24:79–88.

32. Spinozzi, F., C. Ferrero, ., P. Mariani. 2014. GENFIT: software for the analysis of small-angle x-ray and neutron scattering data of macromolecules in solution. J. Appl. Crystallogr. 47:1132–1139.

53. Ozenne, V., F. Bauer, ., M. Blackledge. 2012. Flexible-meccano: a tool for the generation of explicit ensemble descriptions of intrinsically disordered proteins and their associated experimental observables. Bioinformatics. 28:1463–1470.

33. Ebel, C., L. Costenaro, ., G. Zaccai. 2002. Solvent interactions of halophilic malate dehydrogenase. Biochemistry. 41:13234–13244. 34. Kim, H. S., and F. Gabel. 2015. Uniqueness of models from smallangle scattering data: the impact of a hydration shell and complementary NMR restraints. Acta Crystallogr. D Biol. Crystallogr. 71:57–66.

54. Lawrence, M. S., K. J. Phillips, and D. R. Liu. 2007. Supercharging proteins can impart unusual resilience. J. Am. Chem. Soc. 129:10110–10112.

35. Svergun, D., C. Barberato, and M. H. Koch. 1995. CRYSOL—a program to evaluate x-ray solution scattering of biological macromolecules from atomic coordinates. J. Appl. Crystallogr. 28:768–773.

55. Artero, J. B., M. Ha¨rtlein, ., P. Timmins. 2005. A comparison of refined x-ray structures of hydrogenated and perdeuterated rat gE-crystallin in H2O and D2O. Acta Crystallogr. D Biol. Crystallogr. 61:1541–1549.

36. Bardhan, J., S. Park, and L. Makowski. 2009. SoftWAXS: a computational tool for modeling wide-angle x-ray solution scattering from biomolecules. J. Appl. Crystallogr. 42:932–943.

56. Pernot, P., A. Round, ., S. McSweeney. 2013. Upgraded ESRF BM29 beamline for SAXS on macromolecules in solution. J. Synchrotron Radiat. 20:660–664.

37. Tjioe, E., and W. T. Heller. 2007. ORNL_SAS: software for calculation of small-angle scattering intensities of proteins and protein complexes. J. Appl. Crystallogr. 40:782–785. 38. Schneidman-Duhovny, D., M. Hammel, ., A. Sali. 2013. Accurate SAXS profile computation and its assessment by contrast variation experiments. Biophys. J. 105:962–974. 39. Schneidman-Duhovny, D., M. Hammel, and A. Sali. 2010. FoXS: a web server for rapid computation and fitting of SAXS profiles. Nucleic Acids Res. 38 (Suppl 2):W540–W544. 40. Grishaev, A., L. Guo, ., A. Bax. 2010. Improved fitting of solution x-ray scattering data to macromolecular structures and structural ensembles by explicit water modeling. J. Am. Chem. Soc. 132:15484–15486.

57. Konarev, P. V., V. V. Volkov, ., D. I. Svergun. 2003. PRIMUS: a Windows PC-based system for small-angle scattering data analysis. J. Appl. Crystallogr. 36:1277–1282. 58. Webb, B., and A. Sali. 2014. Comparative protein structure modeling using MODELLER. Curr. Protoc. Bioinformatics. 47:5.6.1–5.6.32. 59. Bernado´, P., L. Blanchard, ., M. Blackledge. 2005. A structural model for unfolded proteins from residual dipolar couplings and small-angle x-ray scattering. Proc. Natl. Acad. Sci. USA. 102:17002–17007. 60. Weast, R. C., D. R. Lide, ., W. H. Beyer. 1989. CRC Handbook of Chemistry and Physics. CRC Press, Boca Raton, FL. 61. Koester, L., H. Rauch, and E. Seymann. 1991. Neutron scattering lengths: a survey of experimental data and methods. At. Data Nucl. Data Tables. 49:65–120.

Biophysical Journal 110, 2185–2194, May 24, 2016 2193

Kim et al. 62. DeLano, W. L. 2002. PyMol: an open-source molecular graphics tool. CCP4 Newsl. Protein Crystallogr. 40:82–92. 63. Jacrot, B. 1976. The study of biological structures by neutron scattering from solution. Rep. Prog. Phys. 39:911–953. 64. Huang, J. R., L. R. Warner, ., M. Blackledge. 2014. Transient electrostatic interactions dominate the conformational equilibrium sampled by multidomain splicing factor U2AF65: a combined NMR and SAXS study. J. Am. Chem. Soc. 136:7068–7076. 65. Kuntz, I. D., Jr. 1971. Hydration of macromolecules. III. Hydration of polypeptides. J. Am. Chem. Soc. 93:514–516. 66. Lund, M., L. Vrbka, and P. Jungwirth. 2008. Specific ion binding to nonpolar surface patches of proteins. J. Am. Chem. Soc. 130:11582– 11583. 67. Vrbka, L., J. Vondra´sek, ., P. Jungwirth. 2006. Quantification and rationalization of the higher affinity of sodium over potassium to protein surfaces. Proc. Natl. Acad. Sci. USA. 103:15440–15444. 68. Jungwirth, P., and P. S. Cremer. 2014. Beyond Hofmeister. Nat. Chem. 6:261–263. 69. Impey, R. W., P. A. Madden, and I. R. McDonald. 1983. Hydration and mobility of ions in solution. J. Phys. Chem. 87:5071–5083. 70. Enderby, J. E. 1995. Ion solvation via neutron scattering. Chem. Soc. Rev. 24:159–168. 71. Collins, K. D., G. W. Neilson, and J. E. Enderby. 2007. Ions in water: characterizing the forces that control chemical processes and biological structure. Biophys. Chem. 128:95–104. 72. Conway, B. E. 1981. Ionic Hydration in Chemistry and Biophysics. Elsevier, Philadelphia, PA.

75. Knight, C. J., and J. S. Hub. 2015. WAXSiS: a web server for the calculation of SAXS/WAXS curves based on explicit-solvent molecular dynamics. Nucleic Acids Res. 43 (W1):W225–W230. 76. Clark, N. J., H. Zhang, ., J. E. Curtis. 2013. Small-angle neutron scattering study of a monoclonal antibody using free-energy constraints. J. Phys. Chem. B. 117:14029–14038. 77. Hura, G. L., H. Budworth, ., J. A. Tainer. 2013. Comprehensive macromolecular conformations mapped by quantitative SAXS analyses. Nat. Methods. 10:453–454. 78. Meisburger, S. P., S. A. Pabit, and L. Pollack. 2015. Determining the locations of ions and water around DNA from x-ray scattering measurements. Biophys. J. 108:2886–2895. 79. Zhang, J., C. P. Jones, and A. R. Ferre´-D’Amare´. 2014. Global analysis of riboswitches by small-angle x-ray scattering and calorimetry. Biochim. Biophys. Acta. 1839:1020–1029. 80. Bernado´, P., E. Mylonas, ., D. I. Svergun. 2007. Structural characterization of flexible proteins using small-angle X-ray scattering. J. Am. Chem. Soc. 129:5656–5664. 81. Sibille, N., and P. Bernado´. 2012. Structural characterization of intrinsically disordered proteins by the combined use of NMR and SAXS. Biochem. Soc. Trans. 40:955–962. 82. Receveur-Brechot, V., and D. Durand. 2012. How random are intrinsically disordered proteins? A small angle scattering perspective. Curr. Protein Pept. Sci. 13:55–75. 83. Gabel, F., M. F. Lensink, ., C. Ebel. 2014. Probing the conformation of FhaC with small-angle neutron scattering and molecular modeling. Biophys. J. 107:185–196.

73. Talon, R., N. Coquelle, ., E. Girard. 2014. An experimental point of view on hydration/solvation in halophilic proteins. Front. Microbiol. 5:66.

84. Kynde, S. A. R., N. Skar-Gislinge, ., L. Arleth. 2014. Small-angle scattering gives direct structural information about a membrane protein inside a lipid environment. Acta Crystallogr. D Biol. Crystallogr. 70:371–383.

74. Rambo, R. P., and J. A. Tainer. 2013. Super-resolution in solution X-ray scattering and its applications to structural systems biology. Annu. Rev. Biophys. 42:415–441.

85. Krueger, S., J.-H. Shin, ., Z. Kelman. 2014. The solution structure of full-length dodecameric MCM by SANS and molecular modeling. Proteins. 82:2364–2374.

2194 Biophysical Journal 110, 2185–2194, May 24, 2016