SAXS Assistant: Automated SAXS Analysis for Structural Discovery in Biologics and Polymeric Nanoparticles.

Abstract

Small-angle X-ray scattering (SAXS) is a powerful technique for assessing macromolecular structure. High-throughput SAXS is limited by the time-consuming and, at times, subjective nature of SAXS data interpretation. We present SAXS Assistant, a Python-based script that streamlines SAXS data analysis to extract features for machine learning (ML) and key structural parameters, including the Guinier radius of gyration (Rg), pair distance distribution function (PDDF)-derived Rg, maximum particle dimension (Dmax), and Kratky plots. The script builds upon BioXTAS RAW, and validates reliability via Guinier/PDDF Rg agreement, an important indicator of well-measured datasets. For assistance in Dmax estimation, a multi-layer perceptron (MLP) regressor was trained with 1,940 data files from the small angle scattering biological data bank (SASBDB). The model achieved a test set performance R2 = 0.90 and mean absolute error (MAE) = 11.7 Å. Training exclusively with experimental data translates analyses from researchers, including experts in the field, to the ML model, which helps assess Dmax estimations from PDDF. Gaussian mixture model (GMM) clustering was implemented to classify profiles into structural classes based on entries in the SASBDB. Users may therefore assess the similarity between experimental samples and known biomolecular shapes within the mapped repository entries. This probabilistic clustering aids in quantifying information from Kratky and generating shape-descriptive features. SAXS Assistant accelerates SAXS data analysis through enforced quality control, ML-ready outputs, and flags for low-confidence results. In addition to providing the ability to analyze large datasets at high-throughput, this tool is versatile and may serve researchers in both biological and synthetic polymer research fields.