bioRxivBiophysicsPreprint
Structural Topology-based Electrostatic Model (STEM) Reveals Ion-Coordination Exchange as a Driver of RNA Folding Dynamics
Avijit MainaniD, Akhilesh JaiswariD, José N. OnuchiciD, Karissa Y. SanbonmatsuiD, Susmita RoyiD
Abstract
RNA is a highly charged polyelectrolyte whose folding into functional architectures depends on an ionic atmosphere that screens strong electrostatic repulsion along the phosphate backbone. Whereas monovalent ions primarily stabilize secondary structure, divalent magnesium (Mg2+) drives tertiary folding often via site-specific and adopting various dynamic coordination modes. Current RNA structure-prediction frameworks rely largely on static direct-contact information, overlooking ion-mediated interactions and the dynamic exchange between distinct coordination modes--particularly the dynamic exchange between direct (inner) and solvent-separated (outer-sphere) Mg2+-phosphate coordination that often controls RNAs conformational transition. Here, we introduce the Structural-based Electrostatic Model (STEM), a hybrid implicit-explicit framework that explicitly captures how the dynamic exchange between distinct ion-coordination modes dictates folding pathways. STEM combines explicit Mg2+ ions to resolve site-specific interactions with implicit K+ ions to describe counter-ion condensation mediated electrostatic screening through generalized Manning counter-ion condensation model, enabling computationally efficient exploration of RNA folding landscapes. The model accurately reproduces crystallographic ion-binding sites, experimental preferential ion-interaction coefficients, and Small-Angle X-ray Scattering (SAXS)-derived radii of gyration across diverse RNA systems. Applied to a 58-nt rRNA fragment, STEM reveals that folding from an intermediate to the native state is driven by a chelated Mg2+-mediated tertiary contact and captures the resulting coordination-dependent conformational breathing. By shifting the paradigm from static direct-contact descriptions to ion-mediated dynamic interactions, STEM provides a physically grounded framework for predicting dynamic ensembles of RNA structures, resolving their folding free-energy landscapes, and elucidating the mechanisms of RNA folding and function beyond native conformations across physiological salt conditions. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=91 SRC="FIGDIR/small/734987v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@27da3borg.highwire.dtl.DTLVardef@689310org.highwire.dtl.DTLVardef@18f32f0org.highwire.dtl.DTLVardef@593184_HPS_FORMAT_FIGEXP M_FIG C_FIG
- assay: structural biology
- research focus: protein folding & dynamics
- approach: deep learning
- approach: statistical model
- Citations
- 1
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