From scattering curves to structure: interpretable machine learning maps SAXS/WAXS signals to RNA structural details

Abstract

Solution X-ray scattering offers an ensemble-averaged picture of biomolecular structure and dynamics under near-physiological conditions. Small-angle X-ray scattering (SAXS) reports on global architecture, while extending to the wide-angle regime (WAXS) captures sub-nanometer-level details. However, SAXS/WAXS (SWAXS) interpretation is complicated due to the overlap of multiple correlation lengths. Here, we present a machine-learning (ML) framework that infers structural properties for a given RNA fold. Using molecular simulations to generate ensembles, LightGBM (light gradient-boosting machine) successfully learns mappings from SWAXS features to key geometric properties. We apply this approach to double-stranded RNA (dsRNA) duplexes and a two-way junction construct. The models accurately predict structural changes from SWAXS data and highlight diagnostic q -windows that encode duplex and junction fingerprints. Our results demonstrate that interpretable ML trained on RNA structural ensembles provides a robust route to extract structural information from solution scattering measurements of RNAs.

Weiwei He (何伟渭)

Professor of Biophysics

My research focuses on computational modeling and bioinformatics.