Assessing Sample Quality for Modeling bioSAXS Experiments Using ScAtter
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Solution state small angle X-ray scattering (SAXS) experiments can be performed on a wide variety of sample types and conditions. The measured SAXS signal originates as a difference measurement taken as the difference between the sample (buffer and particles) and background (buffer). Experiments can be equally performed on homogenous and polydisperse systems. However, the quality of the SAXS sample and accuracy of the difference measurement does limit the information content and usability of the SAXS measurement.
In this webinar, Rob Rambo will demonstrate with the JAVA program ScÅtter how to assess sample and data quality from experimental SAXS profiles available at the SAXS repository bioisis.net. Poor background subtractions limits the effective resolution of the SAXS experiment and can provide mis-leading conclusions from a Kratky analysis. Using built-in features in ScÅtter, the resolution limit of a SAXS dataset can be confidently determined and likewise, quantitative measures of particle compactness can be readily derived. Furthermore, Rob Rambo will show how the implementation of a cross-validation procedure during the transform of the SAXS data to the P(r)-distribution can be used to assess data quality. Join us for this complimentary 45-minute webinar with a live Q&A session to follow.