3.2 SAXS experiments

The use of SAXS data in molecular modeling has a number of advantages, the most significant of which is that SAXS experiments are performed on samples in aqueous solutions. Thus, SAXS provides information about the conformations of macromolecules in their natural environment. Improper data processing can lead to errors. While this is certainly true for any method, SAXS is exceedingly sensitive to errors. For example, the SAXS intensity profile is the difference in signals between the sample and the corresponding buffer; inadequate signal subtraction can lead to significant systematic errors in the resulting profile. Furthermore, SAXS-based modeling must take into account the protein hydration shell. However, SAXS intensity profiles from atomic models, such as CRYSOL [11], FoXS [12], AXES [13], AquaSAXS [14] and SASTBX [15], differ in how they treat the hydration shell, putting additional uncertainty on SAXS-derived models.

Sample preparation for SAXS experiments requires neither crystal growth nor protein labeling. Unlike X-ray crystallography, which relies on diffracting crystals, macromolecules in solution always scatter X-rays. Similarly, unlike solution NMR techniques which have some molecular size limitations, SAXS is not limited by the molecular mass; rather larger proteins will scatter better. Furthermore, the quality of SAXS data depends neither on the size nor on the flexibility of the macromolecules under study [16].

SAXS measurements can be performed using a home X-ray source or, more typically, synchrotron radiation. They are performed on samples in a wide range of solution conditions, molecular concentrations and temperatures. For all SAXS experiments, optimal sample preparation is essential for obtaining interpretable SAXS data. In particular, SAXS is exceptionally sensitive to aggregation, as soluble aggregates, even if they represent less than 1% of the sample, are significantly larger and thus will have a major impact on the overall measured scattering. Thus, identifying conditions that prevent sample aggregation is essential (see Note 12). Detailed protocols for SAXS methods (including strategies for detecting aggregation and minimizing radiation) have been recently summarized [16]. Here, we focus on SAXS data collection for IDPs and IDP containing proteins.

Sample production: Purify all samples immediately prior to SAXS measurements using size exclusion chromatography (SEC) to remove trace aggregates.

When possible, filter any trace aggregates immediately prior to SAXS measurements. For this, 0.02 μm syringe filters (GE Healthcare Anotop 10) are suitable (see Note 12).

Sample cells: Sample cells should be thoroughly cleaned prior to use to eliminate trace proteases (i.e., NaOH washes). Prior to measurements, the cells should subsequently be thoroughly washed with SAXS buffer (see Note 13).

Sample concentration: The optimal concentration for SAXS measurements depends on the X-ray source, the size and assembly of the cell (flow-through or static), among other parameters. Typically, measurements are initiated using the lowest concentrations possible. Samples are then concentrated and the SAXS data collected. This is continued until the sample is concentrated as high as can be achieved before aggregation is detected (sometimes this can be as high as 30 mg/ml) (see Note 14).

SAXS data analysis: Numerous software packages are available to analyze SAXS data, with the two most widely used being ATSAS [17] and SCATTER (https://bl1231.als.lbl.gov/scatter/), both of which also allow for the calculation of 3D envelops from the data; these calculations are usually performed with the highest signal/noise dataset (commonly, the highest measured concentration). This can also be done using Fast-SAXS-pro [18].

IDP detection: The Kratky plot, i.e., the plot of q²I(q) as a function of the momentum transfer q, is used to identify IDPs (see Note 15).

Convergence of the Kratky plot at high q suggests compaction, whereas a hyperbolic shape suggests flexibility [19]; the hyperbolic feature is a trademark of random coils and IDPs.

In practice, Kratky plots may be difficult to assess if the SAXS data are noisy or truncated. Recently, analysis based on the Porod-Debye law, i.e. analysis of q⁴I(q) versus q⁴ at intermediate q-values, has been introduced as a more robust approach to tell apart flexible molecules from rigid ones [19].

Molecular flexibility can also be presumed if SAXS data cannot be accounted for with a single model, suggesting that an ensemble of models may be required to fit the experimental data [20,21].

Data processing and the need for ensemble modeling approaches: The standard approach for SAXS data analysis is to analyze the scattering intensity profile, I(q), which enables the determination of the pair-distance distribution function, P(r), and the corresponding molecular envelope [22,23,17]. Molecular envelopes provide an informative visual interpretation of the SAXS data; however, this approach holds only for rigid systems with minor ensemble fluctuations. When SAXS is used to study IDPs, this standard envelope calculation fails [24]. Furthermore, SAXS can be used to determine structures of protein complexes if atomic structures of the constituent proteins are known [19]. However, to achieve this goal with optimal accuracy, structural models of the protein complexes should be fitted directly to the experimental SAXS data; simply placing the protein models into molecular envelopes does not fully use the structural information encoded in the scattering intensity profile I(q).