Predicting RNA structure and dynamics with deep learning and solution scattering

Patt, Edan; Classen, Scott; Hammel, Michal; Schneidman-Duhovny, Dina

doi:10.1016/j.bpj.2024.12.024

Title: Predicting RNA structure and dynamics with deep learning and solution scattering

Journal Article · 25 December 2024 · Biophysical Journal

DOI: https://doi.org/10.1016/j.bpj.2024.12.024 · OSTI ID:2483768

^[1]; Classen, Scott ^[2]; Hammel, Michal ^[2]; Schneidman-Duhovny, Dina ^[1]

Hebrew Univ. of Jerusalem (Israel)
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Advanced deep learning and statistical methods can predict structural models for RNA molecules. However, RNAs are flexible, and it remains difficult to describe their macromolecular conformations in solutions where varying conditions can induce conformational changes. 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 absence of cations essential for stability and charge neutralization in predicted structures and the inadequacy of a single structure to represent RNA’s conformational plasticity. We introduce a 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 Mg²⁺ ion binding sites. Validated through benchmarking against 14 experimental data sets, SCOPER significantly improved the quality of SAXS profile fits by including Mg²⁺ 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 validating the solution state of RNAs given an initial, sufficiently accurate structure and provides the corrected atomistic model, including ions.

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: National Cancer Institute (NCI); National Institute of General Medical Sciences (NIGMS); National Institutes of Health (NIH); U.S.-Israel Binational Science Foundation (BSF); USDOE; USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF); USDOE Office of Science (SC), Biological and Environmental Research (BER)

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 2483768

Journal Information:: Biophysical Journal, Journal Name: Biophysical Journal Journal Issue: 3 Vol. 124; ISSN 0006-3495

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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