Abstract
Small-angle X-ray scattering (SAXS) is a method for examining the solution structure, oligomeric state, conformational changes, and flexibility of biomacromolecules at a scale ranging from a few Angstroms to hundreds of nanometers. Wide time scales ranging from real time (milliseconds) to minutes can be also covered by SAXS. With many advantages, SAXS has been extensively used, it is widely used in the structural characterization of biomacromolecules in food science and technology. However, the application of SAXS in charactering the structure of food biomacromolecules has not been reviewed so far. In the current review, the principle, theoretical calculations and modeling programs are summarized, technical advances in the experimental setups and corresponding applications of in situ capabilities: combination of chromatography, time-resolved, temperature, pressure, flow-through are elaborated. Recent applications of SAXS for monitoring structural properties of biomacromolecules in food including protein, carbohydrate and lipid are also highlighted, and limitations and prospects for developing SAXS based on facility upgraded and artificial intelligence to study the structural properties of biomacromolecules are finally discussed. Future research should focus on extending machine time, simplifying SAXS data treatment, optimizing modeling methods in order to achieve an integrated structural biology based on SAXS as a practical tool for investigating the structure-function relationship of biomacromolecules in food industry.
Introduction
Bioactive macromolecules, including peptides, nuclear acids, proteins, carbohydrates, and lipids, are not only essential components of food but also are the dominant substances for food to realize its functions. Meantime, biomacromolecules are widely used as additives for beverages, yogurt, cereal products, nuts, snacks, etc. to improve the food nutrition (1). Besides, other functional properties, such as gelation, foamability, water retention and emulsification, typical functions induced by structural changes of biomacromolecules, play significant roles in food processing. Therefore, the structure-function relationship of biomacromolecules is one of the most important topics in food science and technology.
Several experimental techniques are available for the structural characterization of biomacromolecules. High-resolution structural techniques, including crystallography, electron microscopy (EM) and nuclear magnetic resonance (NMR) have yielded incredibly detailed structural information at the atomic level on highly populated static states (2–4). However, due to the requirement of good crystals for crystallography, requirement of solubilized and monodisperse sample, and the low molecular weight requirement of NMR, a significant fraction of food biomacromolecules cannot be analysed using these three high-resolution methods (5). Furthermore, because of the highly heterogeneous and polydisperse of most food biomacromolecules, it is also challenging for conventional techniques, Fourier Transform Infrared spectroscopy (FTIR), dynamic light scattering (DLS) to monitor the change in the structure of biomacromolecules, especially the dynamics of self-assemble and hydrolysis. Therefore, developing of alternative structural characterization techniques for biomacromolecules with rapid response, easy sample preparation, data collection under near-native conditions, and in situ capability, is needed in food science and technology.
Small-angle X-ray scattering (SAXS) is a powerful tool for structural characterization of samples under resolutions ranging from a few Angstroms to hundreds of nanometers. SAXS is sensitive to both ordered and not-ordered features in the sample and it has several advantages over direct characteristic techniques in that: a very small amount of sample for measurement, rapid data collection, no crystallization or fixation requirement, high-throughput screening model and multiple in situ capacities, etc. (6). These features make SAXS an interesting technique for academic and industrial applications of highly interdisciplinary field, including life science, biomedicine, and biomaterial engineering.
Since SAXS was first used to study the geometry of typical milk proteins β-lactoglobulin tetramer, there have been nearly 60 years of research on food biomacromolecules. Several studies have reviewed the advances and applications of SAXS in food field (7–13). Gilbert summarized the principle and the latest activities in the application of SAXS to food colloids (14). This work provides information for the SAXS expert who is interested in applying this method to food colloids and the food scientist that wishes to gain knowledge of the former.
However, a work concluding the recent development and applications of modeling programs and in situ capabilities of SAXS for the structural characterization of food biomacromolecules is not available yet. Therefore, in the present review, the SAXS principle, theoretical calculations and modeling programs are summarized, technical advances in the experimental setups of in situ capabilities: coupled with chromatography, time-resolved, temperature, pressure, flow-through, are elaborated. Recent applications of SAXS for studying the structural properties of food biomacromolecules including proteins, carbohydrates and lipids are highlighted. Moreover, the limitations and prospects of SAXS are also discussed. We hope this review will provide reference information for food scientists who investigate the relationship between the structure and function of biomacromolecules using SAXS.
Principle, theoretical calculation, and programs
A typical bio-SAXS measurement is performed using a sample concentration at least ∼0.5–10 mg ml–1 with a ∼15–30 μl of volume, and generally takes less than a few minutes on a synchrotron beamline or dozens of minutes to hours using an in-house instrument (15). The principle of SAXS is that a collision between a monochromatic incident X-ray beam and a surface particle results in scattering of the beam in all directions. The one-dimensional (1D) scattered intensity I(q) and the average of the various conformers present in the population of scattered particles are recorded using a two-dimensional (2D) detector. The magnitude of the scattering vector q = 4πsinθ/λ, where θ is the half of the angle between incident and scattered beams. At small angles (θ < 5°), the inhomogeneity in the electron clouds can be observed, which will provide information about the size and shape of biomacromolecules in the sample (16). The “background” scattering from the buffer is independently measured and subtracted from that of the solution (17).
The radius of gyration (Rg) of biomacromolecules can be estimated directly from small q values using Guinier approximation (18), , where I(q) is the scattering intensity and I(0) is the forward scattering intensity. The pair-distance distribution function p(r) (19), corresponding to the paired set of distances between all electrons within the scattered particle, can be generated via indirect Fourier transform by using the GNOM (20), PRIMUS (21), BioXTAS RAW (22), and BIFT programs (23). By comparing with molecular weight determined from I(0), the volume of the biomacromolecules can be calculated by the Porod approximation (24, 25). Moreover, the compactness or flexibility of biomacromolecules can be evaluated using the Kratky (26), dimensionless Kratky (27), and Porod-Debye plots (28). The principle, a typical SAXS measurement and derivative profiles can been seen from Figure 1.
FIGURE 1
The net scattering intensity is critically dependent on the scale factor, especially at high q regions (as shown in Figure 2A), and any minor deviation from the accurate value may have a significant impact on the final results (30). As seen from Figures 2B–F, a sharp decrease in the net scattering intensity at high q regions is observed due to a slight increase in scale factor (1.000–1.001) for background buffer subtraction. SAXS allows the determination of the structural properties under a broad range from a few Angstroms to hundreds of nanometers, which covers the size of biomacromolecules and their complex (Figure 2G). The maximum distance within a scattered particle (Dmax) can be assessed by the distance r of the p(r) distribution equals zero (Figure 2H). When the p(r) curve with a maximum frequency at a distance less than half of the Dmax (olive curve in Figure 2H), the scattered particle adopts rather extended and elongated conformation in solution. Therefore, the change in conformation and structure of biomacromolecules can be monitored by p(r) profile. Moreover, an accurate determination of the scale factor for background subtraction has significant implications for obtaining the further reliable structural parameters of biomacromolecules (25).
FIGURE 2
Small-angle X-ray scattering profile can then be used for a further series of theoretical calculations to obtain structural information, ab initial model and ensemble structures. By translating scattering curves into bead representations consisting of a set of dummy atoms, an ab initio model of biomacromolecule with a resolution of 10–30 Å can be obtained via DAMMIN (31) and GASBOR programs (32). It is noted that DAMMIN is the most used in the modeling programs for reconstructing low-resolution shape of biomacromolecule. An iterative phase retrieval method, density from solution scattering (DENSS), has been expanded to analyze SAXS data, which may avoid many of the assumptions limiting the resolution and accuracy of modeling algorithms by explicitly calculating electron density (33). Deep learning methods, such as DecodeSAXS, have been reported used to classify and reconstruct the shapes with model parameters (34, 35). Critical Assessment of Protein Structure Prediction (CASP), a machine learning program, utilizes SAXS data to build improved models simulating the global shape of the target (36). Google’s AlphaFold 2 indisputably won the CASP14 competition. The results are so incredibly accurate that many have hailed this code as the solution to the long-standing protein structure prediction problem.
MONSA program (31) can be used to treat biomacromolecules containing multiple phases (e.g., intensities from protein/nucleic acid complexes) (6). SREFLEX (37), SASREF (38), and CORAL (39) programs can be used for high-resolution modeling of rigid bodies. SAXSDom has been utilized to model stable multi-domain proteins with flexible linker regions (40). Moreover, CRYSOL is used to compare the SAXS data with a PDB file with an X-ray or NMR structure of a protein or a protein-DNA(RNA) complex (41). SUPCOMB is a tool for superimposing one 3D structure onto another (42). In the case of flexible and mixed biomacromolecule systems (protein, DNA, and RNA), the structural ensembles of the biomacromolecule can be acquired by the ensemble optimization method (EOM) (43). For the oligomeric and transient state system, the volume fraction of components can be calculated using OLIGOMER, and the ab initio model of intermediate may be acquired by DAMMIX (21, 44). A hybrid resolution molecular dynamic (MD) method (hySAXS) has been used to create an ensemble of structures for intrinsically disordered proteins (IDPs), which is comparable to the experimental SAXS data (45). The extended experimental inferential structure determination method (X-EISD) (46) and Bayesian/Maximum entropy (BME) method (47) can be also used to calculate the maximum log-likelihood of an IDPs ensemble derived from SAXS. LIPMIX (48) and BILMIX programs (49) enable polydispersity of the model in multilamellar and asymmetric lipid vesicles and simultaneously generate the corresponding size distribution, respectively. The program ELLLIP can reconstruct the quasi-atomistic models of ellipsoidal liposomes (50) as well as bi-micellar systems involving proteins (51).
Several integrated docking methods by fitting the theoretical scattering curve of possible models to the experimental SAXS data have been developed to estimate the structure of complexes. Examples of such docking methods include SASREF (38), FoXSDock (52), HADDOCK (53), ClusPro FMFT-SAXS (54), pyDockSAXS (55), RosettaDockSAXS (56), PatchDock (57), and ATTRACT-SAXS (58). It has shown that iSPOT can filter docked structure and characterize a native-like model combined SAXS with foot-printing data by generating theoretical scattering of crystal structure (59). Molodenskiy et al. presented a PyMOL plugin, MPBuilder, which provided a set of adaptable routines for modeling membrane proteins (MPs), protein-detergent complex, bicelles, and lipid scaffold (saponin nanoparticles, nanodiscs) validated with SEC-SAXS data (60).
A comprehensive list of programs to reconstruct the structure and model of biomacromolecules based on SAXS data is shown in Table 1, and many of the programs are publicly available to academic users and moderately easy to operate. The details and applications of each program please see the corresponding reference. The popularity of SAXS has been propelled by novel data analysis and modeling algorithms. Developing user-friendly modeling programs will facilitate the utilization of SAXS for large-scale studies, which is also a major achievement in the community toward broader use of the method in combination with complementary techniques and enabling the cross-validation of structural data (61).
TABLE 1
| Program | Accepted experimental file | Functionality | Output | Web server | References |
| Membrane protein (MP) Builder | The plugin of both PyMOL and ATSAS, SAXS data | Generation and refinement of all-atom protein-detergent, bicelle, and lipid-scaffold (saponin nanoparticles, nano-discs) complexes | Models of protein-detergent assembles without minimized energy | https://github.com/emblsaxs/MPBuilder | (60) |
| Critical assessment of protein Structure prediction (CASP) | SAXS data | SAXS-assisted protein structure prediction | Predicted solution structure | https://predictioncenter.org/ | (36) |
| CRYSOL in ATSAS | PDB, SAXS data | Evaluating atomic structure of biomacromolecules based on SAXS experimental data | Fitting with chi values | https://www.embl-hamburg.de/biosaxs/crysol.html | (51) |
| CORAL combines the algorithms of SASREF, BUNCH in ATSAS | PDB, SAXS data | Rigid body modeling of multidomain protein complexes against multiple SAXS data | PDB and fitting | https://www.embl-hamburg.de/biosaxs/manuals/coral.html | (39) |
| DAMMIN or MONSA in ATSAS | Output file of the program GNOM in ATSAS | Restoring ab initio shape of biomacromolecules | PDB and fitting | https://www.embl-hamburg.de/biosaxs/manuals/dammin.html | (31) |
| GASBOR in ATSAS | Output file of the program GNOM in ATSAS | Restoring ab initio of protein structure using a chain-like ensemble of dummy residues | PDB-alike file | https://www.embl-hamburg.de/biosaxs/manuals/gasbor.html | (32) |
| OLIGOMER in ATSAS | PDB, SAXS data | Computation of volume fractions of mixtures of protein with SAXS data from the components | Fitting and file containing volume fractions of components in mixture | https://www.embl-hamburg.de/biosaxs/manuals/oligomer.html | (21) |
| DAMMIX in ATSAS | PDB, SAXS data | Restoring ab initio shape of intermediate state component and its volume fraction | PDB and fitting | https://www.embl-hamburg.de/biosaxs/manuals/dammix.html | (44) |
| EOM in ATSAS | Amino acid sequence, PDB of domains/subunits, SAXS data | Fits an average theoretical scattering intensity derived from an ensemble of conformations to experimental SAXS data. | PDB and fitting | https://www.embl-hamburg.de/biosaxs/manuals/eom.html | (43) |
| FoXS | PDB, SAXS data | Computing a theoretical scattering profile of a structure and fitting of experimental profile | Fitting file of PDB with SAXS curve | https://modbase.compbio.ucsf.edu/foxs/ | (52) |
| SAXSDom | Sequence of individual domain | Multidomain protein assembly modeling | PDB file of multidomain protein | https://github.com/jianlin-cheng/SAXSDom | (40) |
| FoXSDock | PDB files of receptor and ligand, SAXS data | Docking two rigid protein structures based on a SAXS profile of their complex | PDB file of complex | https://modbase.compbio.ucsf.edu/foxsdock | (52) |
| ATTRACT-SAXS | PDB files of receptor and ligand, SAXS data | Docking protein-protein benchmark with simulated SAXS data without a physiochemical force field | High-quality solution models of protein-protein complexes. | http://www.attract.ph.tum.de/services/ATTRACT/attract.htmltum.de | (58) |
| RosettaDockSAXS | SAXS data | Predicting unknown 3D atomic structures of protein-protein complexes | 3D atomic structures | https://rosie.rosettacommons.org/docking/ | (56) |
| DecodeSAXS | SAXS data | Machine learning methods to build 3D models | 3D models | http://liulab.csrc.ac.cn:10005/submit/ | (35) |
| pyDockSAXS | PDB files of receptor and ligand, SAXS data Complex type: enzyme inhibitor, antibody, or antigen | Structural models of protein–protein interactions at large scale. | Models of complex | life.bsc.es/pid/pydocksaxs | (114) |
| ClusPro | PDB files of receptor and ligand | Protein–protein docking server based on fast Fourier transform (FFT) data | Models of complex | http://cluspro.org/nousername.php | (115) |
| X-EISD | Sequence of protein, SAXS experimental data | Generating ensembles of IDPs | Ensembles | https://github.com/THGLab/X-EISD | (46) |
| BME | Experimental data. Calculated data from simulation trajectory | Generating ensembles | Ensembles | https://github.com/KULL-Centre/BME | (47) |
| SAXScreen | SAXS data, ITC titration curve, ligand, and buffer SAXS data | Screening protocol utilizing SAXS to obtain structural information involving protein-RNA interactions. | Models of complex | https://github.com/zharmad/SAXScreen | (116) |
List of some of the available software programs used in the analysis and reconstruction of models based on small-angle X-ray scattering (SAXS) data.
In situ capabilities of small-angle X-ray scattering
Size-exclusion chromatography-small-angle X-ray scattering
The online purification system coupled with SAXS, such as size-exclusion chromatography (SEC), gel filtration chromatography, and reversed-phase chromatography, is a standard approach for separating oligomeric species or components in a heterogeneous sample (62). The programs like CHROMIXS (63), DATASW (64), DELA (65), EFAMIX (66), and US-SOMO HPLC-SAXS module (67) have been developed to process chromatography-SAXS data. The scheme for SEC-SAXS setup is shown in Figures 1, 6, and applications of chromatography combined with SAXS for studying biomacromolecules are summarized in Table 2.
TABLE 2
| Samples | X-ray source | Elution buffer | column | Concentration/ volume | Detector distance from sample | Flow | Exposure | References |
| BSA | EMBL P12 beamline | 50 mM HEPES, 150 mM NaCl, 2% v/v glycerol, pH 7 | Superdex 200 Increase 10/300 | 8.8 mg ml–1 100 μl | Pilatus 6M, 3.3 m | 0.5 mL min–1 + splitter | 1 s | (117) |
| Glucose isomerase, Streptomyces rubiginosus | EMBL P12 beamline | 50 mM Tris, 100 mM NaCl, 1 mM MgCl2, 1% v/v glycerol, pH 7.5 | Superdex 200 Increase 10/300 | 10.3 mg ml–1 100 μl | Pilatus 6M, 1.5 m | 0.5 ml min–1 + splitter | 1 s | (117) |
| Class II pyruvate aldolase | EMBL P12 beamline | 20 mM HEPES, pH 7.5 | Superdex 200 Increase 10/300 | 8 mg ml–1 80 μl | Pilatus 6M, 3.3 m | 0.5 ml min–1 + splitter | 1 s | SASDEX9 |
| Ovalbumin + β Amylase mixture, Ovalbumin | EMBL P12 beamline | 20 mM Tris, 150 mM NaCl, 5% glycerol | Superdex 200 Increase 10/300 | 15 mg ml–1 100 μl | Pilatus 2M, 3.0 m | 0.5 ml min–1 + splitter | 1 s | (118) |
| Cycloamylose | SPring-8 | Milli-Q water or 6% (v/v) methanol | 30 × 1000 mm packed with TOSOH HW-55S | 2.0 mg ml–1 2.0 ml | Hamamatsu Photonics V5445P-MOD, 1.5 m | 2 ml min–1 | 2 s | (119) |
| Caseinate | SSRF BL19U2 beamline | 10 mM Tris-HCl pH 6.7 | Superdex 200 Increase 10/300 | 25 mg ml–1 150 μl | Pilatus 1M, 3.0 m | 0.5 ml min–1 | 1 s | (29) |
| Ovalbumin Proteoglycans | KEK BL-10C station | 10 mM PBS, pH 6.9 50 mM PBS, pH 6.9. | SB-806 M HQ, 300 × 8 mm, Shodex GF-7M HQ, Shodex | 0.735 mg ml–1 3% (w/v) | Pilatus3 2M, 1.98 m | 0.3 ml min–1 0.4 ml min–1 | 3 min with intervals of 10 s | (120, 121) |
| Glucose isomerase | Australian Synchrotron coflow-SEC SAXS beamline | 20 mM PBS with 128 mM NaCl, 22 mM KCl, 5% (v/v) glycerol, pH 7.5 | Superdex S200 Increase 5/150 | 2.5 mg ml–1 100 μl | Pilatus2 1M, 8 m | 0.5 ml min–1 | 2 s | (122) |
| Lysozyme | PLS II 4C SAXS beamline | 10 mM PBS with 138 mM NaCl, pH 7.4 | Agilent Bio SEC-5 | 20 mg ml–1 100 μl | Rayonix 2D, no mention | 0.06 ml min–1 | 10 s | (123) |
| Immunoglobulin G | La-SSS adopts NANOPIX | 100 mM Tris-HCl with 100 mM NaCl, pH 7.5 | Superdex 200 Increase 10/300 | 5.0 mg ml–1 500 μl | HyPix-6000, 0.35 m | 0.02 ml min–1 | 30 s | (124) |
| Apoferritin | BioXolver L, Xenonx | 50 mM HEPES, pH 7.5 | Superdex 200 Increase 10/300 | 0.5 mg mL–1 500 μL | Single-photon-counting detector, 0.6 m | 0.5 ml min–1 | 30 s | (125) |
| Yeast alcohol dehydrogenase | EMBL P12 beamline | 50 mM HEPES, 150 mM NaCl, 2% v/v glycerol, pH 7 | Superdex 200 Increase 10/300 | 9.2 mg ml–1 100 μl | Pilatus 6M, 3 m | 0.5 ml min–1 | 67 s | (117) |
Application of chromatography combined small-angle X-ray scattering (SAXS) for studying biomacromolecules.
Although the nanocluster model for describing casein micelle structure is widely accepted, little direct evidence at the nanometer scale supported this model. Sun et al. (29) reported a method that can prove and quantify the conformation and the fine structure of the casein cluster based on SEC-SAXS. The SEC-SAXS results showed that casein cluster presented Rg values ranging from 39.45 to 40.77 Å with a Dmax of 180 Å. The dimensionless Kratky plot suggested a rather extended and elongated conformation of casein cluster in solution. The experimental Mw according to the Bayesian Interference analysis was 50.3–64.7 kD with a probability of 91.54%, indicating the presence of 2–3 casein monomers in the cluster. Further, the DAMMIX and OLIGOMER results indicated that the cluster consisted of four species, αs1-β-αs2-casein, αs1-casein, αs2-casein and αs1-αs2-casein with a volume fraction of 64.3, 22.8, 8.5, and 4.4 %, respectively. The results of EOM indicated the presence of two conformers in αs1-β-αs2-casein, the elongated one (∼60 Å of Rg) with 64.7% of volume fraction and the compact one (∼35 Å of Rg) with 35.3% of volume fraction. It is the first time to reveal the structural properties of casein cluster based on SEC-SAXS, which may help understand better for internal structure of casein micelles regarding their primary casein cluster. Therefore, SAXS has been proved to be a powerful tool to study the structure and dynamics of the flexible, disordered and mixed biomacromolecules.
Time-resolved and time-dependent small-angle X-ray scattering
Small-angle X-ray scattering measurements are performed over a set time period ranging from microseconds to hours to assess the time-resolved (TR-SAXS) and time-dependent (in situ or real time) changes in structure and function for protein, carbohydrate, fat or non-nutritive compounds, such as gelatinization, assembly, micellization or colloid formation as well as in digestion and hydrolysis (68). This method is particularly suited to differentiate triggers of structural changes, including optical excitation (69), electron transfer (70), temperature jump (T-jump) (71), pH-jump (72), photoreduction (73), and reactant concentration jump (74).
Kuang et al. investigated the lamellar structure change of waxy corn starch during gelatinization and reveal the gelatinization mechanism by TR-SAXS in the temperature range from 35 to 141.85 °C with a measurement of 60 s at each degree (75). Gilbert reviewed the latest activities in the application of time-dependent SAXS to food colloids (14). Hempt et al. reported a novel digestion model of milk using an integrated online flow-through TR-SAXS with an in vitro cell co-culture model (76). Krishnamoorthy et al. reported an approach based on the time-dependent SAXS from protein spherical nucleic acids to elucidate the enzymatic degradation of DNA, which should prove invaluable in probing other enzyme-catalyzed reactions on the nanoscale (77). The details of TR-SAXS equipped with the laser pulse recording as a function of the time delay between laser pulse and incident X-ray are shown in Figure 3.
FIGURE 3
The SAXS beam equipped with the microfluidic device (continuous flow and stopped-flow) may not only reduce the sample damage by radiation (78), but also monitor the dynamic structural alternations during interactions in real-time (79). Hsu et al. characterized the transient partially folded state of bovine α-lactalbumin (BLA) coupled with TR-SAXS following a T-jump (74). The structural responses of BLA after an 11.5°C T-jump from the initial temperatures of 60, 65, and 70°C were individually recorded from 20 μs to 70 ms delay. Three states were molten globule state and two terminal unfolded states, U1 and U2. The application of TR-SAXS for structural characterization of biomacromolecules in foods is listed in Table 3.
TABLE 3
| Samples | X-ray source | q-Range | Acquisition time | References |
| Gluten protein mixtures | ESRF beamline ID02 | 1.2 × 10–4–6.0 × 10–3 Å–1 | 5 ms | (126) |
| Gelation of pea and whey proteins | APS beamline 9-ID-C | 1.0 × 10–4–0.3 Å–1 | A 90 s measurement every 2–5 min | (127) |
| Polyphenol pea protein gel | APS beamline 9-ID-C | 1.0 × 10–4–1.0 Å–1 | 20 s | (128) |
| Zein-based oleo gel | APS beamline 9-ID-C | 1.0 × 10–4–1.2 Å–1 | 20 s | (129) |
| Liquid–liquid phase separation of BSA-YCl3 system | ESRF beamline ID02 | 9.0 × 10–5–7.0 × 10–3 Å–1 | 5–50 ms | (130) |
| Cellulose nanofibers | NSLS beamline X9 | 1.0 × 10–3–0.3 Å–1 | 10 s | (131) |
| Gelation of amylose | SPring-8 BL-40B2 | 1.0 × 10–4–0.8 Å–1 | 1–62 min | (132) |
| Lipid/surfactant assemblies | ESRF beamline ID02 | 3.0 × 10–3–0.19 Å–1 | 20 ms | (133) |
| Waxy corn starch | SSRF BL16B1 | 0.025–0.15 Å–1 | 60 s | (75) |
| Milk lipid crystallization during digestion | Australian Synchrotron SAXS beamline | 0.005 < q < 1.0 Å–1 | 5 s | (68) |
| Milk digestion in presence of a cell | Swiss Light Source SAXS beamline | 0.006 < q < 0.5 Å–1 | 70 min with an exposure time of 1 s and a 9 s delay | (76) |
| Krill oil-in-water emulsion | EMBL P12 beamline | 0.01 < q < 0.5 Å–1 | 3,500 s with a 1 s exposure and 9 s delay | (108) |
| Oleic acid (OA) and glycerol monooleate (GMO) self-assemblies | ELETTRA Austrian SAXS beamline | 0.018 < q < 0.5 Å–1 | Five frames with an exposure time of 20 s | (134) |
| Liquid depot formulations | ELETTRA Austrian SAXS beamline | 0.02 < q < 0.5 Å–1 | 10 min with a 5 s exposure with 5 s delay | (135) |
| Lipid vesicles and Ca2+ | ESRF beamline ID02 | 0.0067 < q < 0.5124 Å–1 | 35 frames, first frame 0.04 s after mixing, last frame 316.16 s with a 0.02 s exposure | (136) |
| Soy phosphatidylcholine-citrem nanoparticles | ELETTRA Austrian SAXS beamline | 0.01 < q < 0.4 Å–1 | Four frames with a 0.25 s exposure | (137) |
Application of time-resolved small-angle X-ray scattering (TR-SAXS) at various synchrotron beamlines.
Temperature
Temperature is one of the most important parameters controlling the formation, morphology, and structure of biomacromolecules, since much of biochemistry is thermally driven, functionally relevant conformational changes can also be triggered by changes in temperature. Generally, a trigger T-jump using a nanosecond laser pulse or an infrared (IR) light with a wavelength of 1450 nm (Figure 3, right upper) can be applied to most temperature-sensitive biomacromolecules to perturb the structural dynamics and reveal the changes in structural kinetics and association under various temperature conditions (80).
Berntsson et al. developed a CoSAXS beamline for millisecond T-jump experiments tracked by TR-SAXS with the Eiger2 and Mythen2 detectors and recorded the scattering of the solvent (80). A ∼15°C T-jump can be triggered by a 2 ms infrared laser light and maintained for several seconds with additional laser pulses. The structural changes in lysozyme induced by a T-jump were observed and the population of lysozyme structures differed at this temperature. Moreover, the data showed that IR radiation absorbed directly by the solvent did not show a significant effect compared with that induced by the thermal changes in the protein. Thus, the temperature induced change in structure of biomacromolecule and thermal dynamics of system can be monitored by SAXS effectively based on the scattering characteristic of sample in SAXS profile.
Pressure
High-pressure (HP) food treatment including pasteurization, sterilization, and shelf-life extension, has widely been used to ensure food safety and preserve various thermally sensitive nutrients and bioactive compounds (81). HP-SAXS can also be used to track a wide range of structural changes of food biomacromolecules under pressure in real time (82). Moreover, the experimental setup of TR-SAXS studies of kinetic events induced by sub-millisecond timescale hydrostatic pressure jumps (P-jump, 1–5,000 bar) is also available in several synchrotron SAXS beamlines (83). Typically, the diamond anvil cell (DAC) covers the measurement of milk, solid powders, crystals and crystalline liquids (84). Hydrostatic pressure cell (HPC) is widely used to study phase diagrams of lipid, nano-assemblies, or pressure-dependent structure-function of biomacromolecules (85, 86).
Lehmkühler et al. reported the pressure-induced formation of super crystals from high-quality PEGylated colloidal nanoparticles using 5 ms P-jump SAXS (87). They demonstrated the crystallization pressure (pc) of the suspension by tracking SAXS patterns at pressures above 2 kbar in steps of 100 bar and verified pc between 2.9 and 3 kbar. They observed that the pressure (pf) jumped from 2.9 to 3.58 kbar averaging over 200 ms exposure time. The characteristic time (tw) decreased from 6.1 to 0.07 s with a reduction in Bragg reflection width from 0.138 to 0.0458 Å–1, suggesting the higher the pf, the faster the formation of nanoparticle structure. The results showed that a larger P-jump induced attractive interactions and thereby accelerated the formation of colloidal nanocrystal superlattices with enhanced crystal quality. Therefore, HP-SAXS can be utilized to track the structural change during interactions of biomacromolecules as well as to monitor the preparation of various biobased nanostructures. Exploiting easy operation setup will broad the applications of SAXS for complex biomacromolecule system.
Applications of small-angle X-ray scattering in characterizing food biomacromolecules
Proteins
As one of the most significant biomacromolecules in food, protein plays an essentially nutritional role in vivo. Meanwhile, protein-based ingredients fulfill several technical functions in food formulations and contribute to texture, color, flavor, and other properties such as solubility, stability emulsification, gelation and foaming (88). These researches involve studying protein structure-function relationships, optimizing the utilization of the components of the product, improving the quality, reducing costs, and developing novel protein application (89). SAXS is one of the most suitable techniques for protein structure and function relations study.
According to Yang et al. SAXS was used to investigate the nanostructure of quinoa protein (Chenopodium quinoa) isolates (QPI), one of the emerging proteins native to South America with a well-balanced amino acid profile, and the effect of NaCl and CaCl2 on the heat-induced gelation of QPI (90). Thermal treatment increased the sample I(q) in low-q region and the scattering intensity remained almost the same in the high-q region, which suggested that heat-induced QPI aggregation and then gelation merely occurred on the micron scale, while the internal structure of QPI on the nanoscale changed little. A Guinier shoulder in the mid-q region (0.02 Å–1< q < 0.08 Å–1) of the Kratky plot suggested the existence of nanoscale protein particles or inhomogeneities in QPI gel containing 0–200 mM NaCl. By fitting with correlation length model (), the correlation length (ζ) or particle size of ∼32 Å was obtained for all the QPI gels containing 0–200 mM NaCl. Calcium binding or protein cross-linking induced minor protein inhomogeneities as indicated by substantial changes in the SAXS curve as well as a small peak at q∼0.2 Å–1 in SAXS patterns of QPI gel containing CaCl2.
Pohl et al. (91) reported a high-throughput SAXS screening approach to assess the conformational stability and initial dispersion state of Thermomyces lanuginosus (TLL) and Rhizomucor miehei (RML), important lipases used in the food industry. They found repulsion in nine different kinds of the buffer as indicated by the decreased intensity in the low q-region induced by interparticle diffraction, and a significantly reduced repulsion and reduced oligomerization in phosphate buffer. Salt (35, 70, 140 mM NaCl) had minimal impact on SAXS profiles of TLL in histidine buffer at pH 5.5 and pH 7.5. The major species in the solution in all conditions was found to be monomeric, which confirmed that the differences in SAXS data were related to protein-protein interaction, suggesting that SAXS is used more widely as a tool to gain in-depth knowledge especially for the later stages of protein formulation in the food industry (Figure 4).
FIGURE 4
The structure of casein micelles contributes to the primary physicochemical and organoleptic properties of milk. Yang et al. (92) analyzed the changes in the internal structure of CNs under HHP (up to ∼1,000 MPa) using in situ HP-SAXS equipped with DAC at room temperature. They found a decrease in both scattering intensities at low q (∼0.003 Å–1) and high q (∼0.08 Å–1), suggesting the disruption of CNs and solubilization of the colloidal calcium phosphate (CCP) nanoclusters under HP treatment. The SAXS profiles under pressures ranging from 270 to 960 MPa showed two isosbestic points at q values of ∼0.013 and 0.03 Å–1, which confirmed the appearance of “sub-micelles” and dissociation of CCP. When the pressure returned to atmospheric pressure, the CNs structure reverted partially to the native one (Figure 5A). Similarly, Yang et al. (84) reported the hierarchical structure of milk at various lengths under a pressure of 200 or 400 MPa at 25, 40, or 60°C using HP-SAXS (Figure 5B). The changes in CNs nanostructures varied with pressure rather than time, and temperature played a central role during the HP process.
FIGURE 5
FIGURE 6
Consequently, SAXS can be used as an effective technique not only to track the dynamic properties of biomacromolecule interactions but also to monitor the internal structure of biomacromolecule assembles.
Carbohydrates
Carbohydrates contribute to the bulk of dietary energy and play a vital role due to their diverse biological properties and functionalities in the food industry, as a thickening, gelating, emulsifying, encapsulating, or bulking agent (93). Starch, as an important polysaccharide macronutrient, determines the processing and nutritional quality of starch-based foods (94). Increased attention is needed to identify significant opportunities for real-time monitoring of structural changes during starch processing, such as swelling, gelatinization, retrogradation, and digestibility of starch.
Liu group reported dynamic changes in lamellar structure and gelatinization of cereal starches with different amylose contents in real time using in situ SAXS (Figure 6; 75, 95, 96). In the low-q region, the curves fitted with a simple power law equation, I(q)∼q–α, where mass fractal dimension (0 < α < 3) was an indication of compactness, whereas the surface fractal dimension (3 < α < 4) was considered smooth. During gelatinization, all cereal starches showed a decreasing α value in the q-region between 0.01 and 0.02 Å–1, with the corresponding size of ∼30 to ∼60 nm with the temperature increasing from ∼70 to ∼90°C, implying a mass fractal structure of the starch gel. Interestingly, an isosbestic point in the middle q-region was observed for all samples, which confirmed a two-step gelatinization of starches, namely, two-correlation length (ξ) of particles in the paste/gel system. The structural paraments of lamellae, the average thickness of amorphous layers (da), crystalline and amorphous layer thickness (dc) and the long period distance (dac) parameters were calculated based on Lorentz-corrected SAXS profiles. For high-amylose maize starch (HAM), dc increased from 65°C with a decreased da value, demonstrating swelling of the lamellae following water uptake. For normal maize starch (NMS) and mung bean starch (MBS), dc increased from 60°C with a decreasing value of da. Both dac and dc rapidly decreased at 72.2, 70.2, and 69.4°C for the high amylopectin (HAP), normal rice starch (NS) and HAM samples, respectively (96).
Starch has a strong tendency to retrograde and undergoes syneresis on cooling, namely, retrogradation. The retrogradation starts with the self-assembly of amylose to form a double helix during the cooling and storage of starch gel, followed by the partial crystallization of branched polymers (amylopectin) after prolonged storage (97). Zeng et al. (94) reported the SAXS patterns of retrograde starch with α values of all samples ranging from 1.32 to 2.43, indicating the mass fractal structures of all retrograde starch samples. Compared with storage day 1, the fractal dimensions (Dm) on storage day 24 increased from 1.32 to 2.30, which was consistent with the formation of ordered crystalline structures in the long-range and an increase in the ordered structure of starch during storage.
Starch digestibility based on sustained dietary energy and low glycemic index (GI) of foods plays a vital role in public health (98). Yang et al. used SAXS to investigate thermally and enzymatically digested corn starches under various treatment times. The semi-crystalline lamellar structure of starch exhibited a scattering peak at a q-region of 0.06–0.07Å–1 with a size of 9-10 nm corresponding to the alternating crystalline and amorphous lamellar structure of amylopectin. The peak area of thermally treated and enzymatic digested starch was quantified by fitting SAXS data (0.02Å–1 < q < 0.2Å –1) with a power-law function combined with a Lorentzian peak with (99). The thermal treatment induced water uptake in the amorphous regions of the granule during heating, leading to an increase in the intensity of the low q-region. In contrast, enzymatically treated samples showed changes in the crystalline and amorphous regions within the semi-crystalline lamellar structure and the amorphous growth rings. However, both treatments had little impact on the mass fractal structures of starch as the power law exponent (P) and long period distance (d) were around ∼2 and ∼10 nm for all samples.
Overall, the structural parameters of biomacromolecules derived from SAXS data facilitated the determination of the structure-function relationship and the evolution of nanomaterials based on the role of carbohydrates in the food industry.
Lipids
Fats and oils are important sources of energy and nutrition, and contribute to the desirable functionality, texture and palatability of foods (100). Chemically, fats consist mainly of triglycerides (TAGs) combined with free fatty acid moieties. Besides nutritional properties, lipids facilitate the delivery of lipophilic nutraceuticals. Lipid-based colloid formation or oleogelation is designed to simulate the structure and semi-solid rheological behavior, and is widely utilized in the food industry, including coating, bakery, dairy products, meat, plant-based and artificial meat products (101).
Clemente et al. (102) explored the water/oil/water interface of phospholipid 1,2-dimyristoyl-sn-glycerol-3-phosphocholine [DMPC; 1% (wt/wt)] dissolved in a mixture of volatile solvents such as cyclohexane/chloroform (volume ratio 2:1) using microfluidic devices and investigated the role of μ-SAXS (a sketch of the device provided in Figure 7 Top). The structural characteristics of oil/DMPC bilayers were indicated by the decay in the intensity of the SAXS pattern with an inflection at q = 0.074 Å–1 corresponding to the first minimum value in the form factor of flat objects extending over large distances (Figure 7 Bottom left), which was also reported in previous studies involving the liquid/liquid (L/L) interface of w/o emulsions generated by a microfluidic apparatus (103). The bilayer thickness was found to shrink under treatment at 50°C for 1 h, but not to increase the order of the lipid bilayers, as suggested by the minimum position (q = 0.074 Å–1) shifting toward higher q-region (q = 0.088 Å –1). The authors reported that the phase behavior and structural dynamics of phospholipids at the L/L interfaces can be detected well via micro-focusing SAXS (Figure 7 Bottom right), which may provide deeper insight into the role of double lipid emulsions in the food industry.
FIGURE 7
Pham et al. (104) investigated the lipid self-assembly during in vitro digestion of bovine, human and goat milk using in situ TR-SAXS. The SAXS data revealed similar structural behavior during the early stages of the digestion of three types of milk, indicating lamellar (Lα), inverse hexagonal (H2), and continuous cubic (V2) phases (Figure 8 Top and Middle). All the milk tested self-assembled into non-lamellar liquid crystalline structures, with coexisting lamellar phases associated with calcium soap formation. By tracking the changes in Lα, H2, and V2 phases, the investigators concluded that different structures were formed during the digestion of all three infant formulas tested. During the digestion, soy and human milk that released long-chain fatty acids showed an inverse micellar cubic I2 phase at the oil–water interface, while bovine and goat milk yielded a greater proportion of medium-chain fatty acids tended to exhibit either the V2 or a H2 hexagonal phase. In addition, a TR-SAXS equipped with pH-stat or HHP system was used to monitor both the kinetics of lipolysis and structural behavior during the in vitro digestion of lipids in the presence of carbohydrates, such as chitosan (105), amylose (106), and other commercial supplements (as shown in Figure 8 Bottom).
FIGURE 8
Undoubtedly, the TR-SAXS (106–108) facilitates real-time monitoring of the crystallization of milk lipids under different treatments, but also assessment of the lipid self-assembly during in vitro digestion or under varying buffer conditions, which play a critical role in industrial applications involving the processing and storage of lipid-based foods.
Other biomacromolecules
Traditionally, natural sausage casings are made from collagen-rich intestinal submucosa derived from bovine, porcine and ovine sources. Collagen arrangement in the intestinal submucosa provides strength to animal tissues, although the relationship between structure and strength is not well-characterized. Gunn et al. (109) demonstrated collagen fibril orientation, orientation index (OI) and d-spacing of bovine, porcine and ovine sausage casings using SAXS. The d-spacing was calculated in the range of 64.8–65.2 nm, and the collagen fibrils were arranged in planar layers with OI values of 0.86–0.91 based on the X-ray adsorption edge energies of all samples.
Pectin has been approved as Generally Recognized as Safe (GRAS) and as a food emulsifier, stabilizer, thickener, and gelling agent (110). Mendez et al. (111) elucidated the different emulsification mechanisms of three pectin sources (watermelon, citrus and apple) using SAXS. The two shoulder-like peaks in the low-q (q < ∼0.03 Å–1) and high-q regions were as attributed to intermolecular interactions and chain clusters, and the scattering of rod-like pectin chains, respectively. The Rg3 value of all samples with cross-sections of rod-like pectin chains ranged from 1.4 to 2.3 nm. The P2 corresponding to the smallest structural level ranged from 1.8 to 2.8, suggesting the existence of flexible folded chains rather than ideal rigid rods. The Rg2 values, referring to the size of the molecular clusters originating as a result of chain bending, determined for pectin solutions (20–38 nm) fall within the range previously determined by SAXS (6.3–42 nm) for pectin with different degrees of esterification.
Food-grade surfactants, like lauric arginate (LAE), have been used widely as a preservative against a wide range of food pathogens and spoilage organisms such as processed meats, dairy products and fruit juices (112). Nallamilli et al. (113) reported a coacervation complex of LAE with λ-carrageenan consisting of loosely packed and disordered LAE molecules with an internal bilayer-like structure indicated by a scattering intensity peak at q ≈ 0.161 Å–1, based on which, a periodicity value of d = 2π/q∼3.9 nm was calculated. This was consistent with the quantitative analysis involving fitting a Teubner-Strey structure factor yielding d-spacing of the lamellae from 3.75 to 4.01 nm. Formation of bilayer coacervates was observed at the LAE/carrageenan weight ratio of 2 and the maximum coacervation was detected at a ratio of 5.
Conclusion and outlook
In the current review, recent advances in the structural characterization of food biomacromolecules using SAXS are summarized, including the principle, theoretical calculation methods, in situ capabilities, and applications. The unique feature of SAXS not only provides direct and rapid structural information of biomolecules in their native state but also facilitates the elucidation of conformational dynamics in real-time. SAXS combined with online chromatography represents a fascinating tool for separating and detecting mixtures and flexible systems synchronously, such as disordered fragments in proteins, long-chain ribonucleic acid and IDPs, which significantly widens the range of SAXS applications. Microfluidics installed in SAXS facilitate the study of binding kinetics by reducing the required sample volume to the sub-microliter level. A combination of T-jump or P-jump pump coupled to TR-SAXS allows direct tracking of the structural dynamics triggered by changes in temperature or pressure over a set time period ranging from microseconds to hours. Based on the volume of studies reported, SAXS is becoming a promising tool for monitoring the structure, conformation, interaction, kinetics, and reaction of biomacromolecules to provide molecular insights into the structure-function relationship of biomolecules in different food processing applications.
In the next decade, the application of the fourth-generation of high-brilliance synchrotron facilities will provide insight into the biomacromolecules at the atomic and molecular levels, and promote cutting-edge research via high-resolution imaging, ultrafast process exploration and advanced structural analysis based on SAXS. Furthermore, with the development of artificial intelligence (AI), research that was previously inconceivable or wildly impractical, especially involving protein structure prediction, is now feasible. We have reason to anticipate potential future applications of AI coupled with high-brilliance SAXS in understanding not merely the individual biomacromolecules and complexes in the food industry, but entire cells or even tissues in life science.
Statements
Author contributions
YS: conceptualization, methodology, software, writing—review and editing, revise, and supervision. XL and RC: software and writing—review and editing. FL: writing and editing. SW: writing—review and editing, revise, and supervision.
Funding
We acknowledge financial support from the Basic Research Program of Yunnan Province (202201AT070031) and High-level Talent of Yunnan Normal University.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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Summary
Keywords
SAXS, structural characterization, food biomacromolecules, modeling, in situ capabilities, time-resolved, chromatography, integrated structural biology
Citation
Sun Y, Li X, Chen R, Liu F and Wei S (2022) Recent advances in structural characterization of biomacromolecules in foods via small-angle X-ray scattering. Front. Nutr. 9:1039762. doi: 10.3389/fnut.2022.1039762
Received
08 September 2022
Accepted
03 November 2022
Published
17 November 2022
Volume
9 - 2022
Edited by
Xiao Feng, Nanjing University of Finance and Economics, China
Reviewed by
Mohammad Alrosan, Universiti Sains Malaysia (USM), Malaysia; Ahmed Gomaa, National Research Centre, Egypt
Updates
Copyright
© 2022 Sun, Li, Chen, Liu and Wei.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Song Wei, 597586641@qq.comYang Sun, sunyang@hbuas.edu.cn
†These authors share first authorship
This article was submitted to Nutrition and Food Science Technology, a section of the journal Frontiers in Nutrition
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