Lawrence Berkeley National Laboratory

Constructing a comprehensive understanding of macromolecular behavior from a set of correlated small angle scattering (SAS) data is aided by tools that analyze all scattering curves together. SAS experiments on biological systems can be performed on specimens that are more easily prepared, modified, and formatted relative to those of most other techniques. An X-ray SAS measurement (SAXS) can be performed in less than a milli-second in-line with treatment steps such as purification or exposure to modifiers. These capabilities are valuable since biological macromolecules (proteins, polynucleotides, lipids, and carbohydrates) change conformation or assembly under specific conditions that often define their biological role. Furthermore, mutation or post-translational modification change their behavior and provides an avenue to tailor their mechanics. Here, we describe tools to combine multiple correlated SAS measurements for analysis and review their application to biological systems. The SAXS Similarity Map (SSM) compares a set of scattering curves and quantifies the similarity between them for display as a color on a grid. Visualizing an entire correlated data set with SSMs helps identify patterns that reveal biological functions. The SSM analysis is available as a web-based tool at https://sibyls.als.lbl.gov/saxs-similarity/. To make data available and promote tool development, we have also deployed a repository of correlated SAS data sets called Simple Scattering (available at https://simplescattering.com). The correlated data sets used to demonstrate the SSM are available on the Simple Scattering website. We expect increased utilization of correlated SAS measurements to characterize the tightly controlled mechanistic properties of biological systems and fine-tune engineered macromolecules for nanotechnology-based applications.