Predicting RNA Structure and Dynamics with Deep Learning and Solution Scattering

While novel deep learning and statistics-based techniques predict accurate structural models for proteins and non-coding RNA, describing their macromolecular conformations in solution is still challenging. Small-angle X-ray scattering (SAXS) in solution is an efficient technique to validate structural predictions by comparing the experimental SAXS profile with those calculated from predicted structures. There are two main challenges in comparing SAXS profiles to RNA structures: the structures often lack cations necessary for stability and charge neutralization, and a single structure inadequately represents the conformational plasticity of RNA. We introduce Solution Conformation Predictor for RNA (SCOPER) to address these challenges. This pipeline integrates kinematics-based conformational sampling with the innovative deep-learning model, IonNet, designed for predicting Mg ²⁺ ion binding sites. Validated through benchmarking against fourteen experimental datasets, SCOPER significantly improved the quality of SAXS profile fits by including Mg ²⁺ ions and sampling of conformational plasticity. We observe that an increased content of monovalent and bivalent ions leads to decreased RNA plasticity. Therefore, carefully adjusting the plasticity and ion density is crucial to avoid overfitting experimental SAXS data. SCOPER is an efficient tool for accurately predicting the solution state of RNAs and providing atomistic models of their structures.

The method is available from: https://github.com/dina-lab3d/IonNet

Our pipeline is available for use as a web server: https://bilbomd.bl1231.als.lbl.gov/