Qmax Mst 999 Hd V2.2 Software 27
Melva Simons
The Guinier fit is done for two reasons. First, you get the Rg and I(0) parameters.The Rg tells you about the overall size of the molecule, while I(0) dependson the molecular weight times the concentration. These parameters are usefulcharacterizations of your molecule, and are also needed to calculateother information from the SAXS data, including molecular weight andvolume.
These data quality issues cause deviations from linearity in the Guinier region.For this reason, having a good Guinier fit is one of the primary ways we assessthe quality of SAXS data. A good Guinier is a strong indicator that your datais from a monodisperse sample and is otherwise free of artifacts. If you cannotobtain a good Guinier fit, or you can only obtain a good Guinier fit byexcluding a significant amount of data at the lowest q values, then yourdata probably has one or more of the problems listed above and usually shouldnot be used for further analysis.
The Guinier approximation only holds when the exponential \(\exp(-q^2 R_g^2 /3)\)is small. This means that in order to do a good Guinier fit, we needqRg to be sufficiently small. The qRg value at which the Guinier approximationstarts to fail for a given scattering profile depends on the overall shapeof the scatterer. Below is a figure showing the Guinier approximation (black),and the scattering intensity for a sphere, thin rod, and thin disc (all withthe same Rg).
The range of the Guinier fit is thus ideally from the earliest available qvalue until a maximum qRg of 1.0 or 1.3. However, given that Rg is derivedfrom the Guinier fit, how do you determine the correct maximum q value for theend of the fit? The answer is that the Guinier fit is done iteratively:
The minimum q value of a Guinier fit is usually determined by the minimum availableq value in your data, which is set by the instrument on which you make the measurement.However, it is important to have a small enough minimum q to have a reasonablerange for the Guinier fit. Typically, the minimum qRg value should be\(qR_g\leq 0.65\), though for globular systems it can be okay to have\(qR_g\leq 1.0\). This means that the minimum q value required depends on the sizeof the system measured. In some cases, with particularly large systems, you mayhave to deliberately seek out an instrument that can measure to sufficiently low q.
If your data has quality issues at low q, which can be caused by the problemslisted above, you may find that excluding those data from the fit can improvethe quality of the fit. While this can be acceptable, you should proceedwith caution when doing that, and always show the full data range on plots.The most acceptable case for this to happen is when the first few pointsare either too high or too low, but the rest of the range fits perfectly(see below for criteria for a good fit). In that case, those couple of pointsclosest to the beamstop may have poor statistics or higher instrumentalbackground scattering, and can usually be safely ignored.
The minimum q of your fit, qmin, times the Rg of your fitshould be less than 0.65. This criteria ensures you have enough q range toproperly calculate the Rg and I(0) values. For globular particles(sphere- or disk-like), you can get away with \(q_minR_g
The maximum q of your fit, qmax, times the Rg of your fitshould be less than 1.3 for globular (sphere- and disc-like) particlesand less than 1.0 for extended (rod-like) particles. This ensures youremain in the linear range of the Guinier approximation for the fit.
Non-linearities in your Guinier fit are indicative or problems in your sample.The type of non-linearity can indicate what the problem may be. The figurebelow gives a quick summary off the most common pathologies, more detail isavailable in the sections below.
Figure 3 from [2]. A and D show a good (monodisperse) scattering profile andGuinier fit. B and E show scattering profiles with varying degrees ofinterparticle interference. C and F show scattering profiles with varyingdegrees of aggregation.
Aggregation causes a characteristic upturn at low q. This can either be caused byaggregates initially present in your sample, or by radiation induced aggregation(radiation damage). The figure below shows what that might look like in your data.
Good SAXS data depends on subtracting away all scattering from the buffer andinstrument background. If this subtraction is not good, you can end up witha downturn at low q (over subtraction) or an upturn at low q (under subtraction).This will look similar to aggregation or repulsion in the Guinier fit.
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