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This study presents an enhanced-sampling strategy that significantly accelerates the binding kinetics of magnesium ions to RNA, allowing for a detailed quantitative exploration of ion-binding motifs within a large ribozyme. By employing a combination of barrier-flattening bias and Hamiltonian replica exchange, the researchers achieved efficient sampling of multiple equivalent binding sites, validated against cryo-electron microscopy maps. The findings reveal that inadequate sampling of inner-shell binding can lead to substantial discrepancies with experimental results, emphasizing the critical importance of sampling in accurately modeling divalent ion interactions in biomolecular systems.
Accelerated sampling reveals that insufficient modeling of magnesium binding can lead to significant errors in RNA structural predictions.
Magnesium ions are essential for RNA structure but difficult to model due to slow binding kinetics and experimental limitations. We present an enhanced-sampling strategy that accelerates Mg$^{2+}$ inner-shell binding by orders of magnitude, enabling quantitative exploration of ion-binding motifs in a large ribozyme. The method combines a barrier-flattening bias with Hamiltonian replica exchange to efficiently sample multiple equivalent binding sites, and builds on an approach that achieved top performance in the CASP16 blind assessment of RNA solvation structure. Using cryo-electron microscopy maps for validation, we introduce a local analysis framework that infers the population of individual binding motifs from their agreement with experimental density, enabling site-by-site validation. We find that insufficient sampling of inner-shell binding leads to significantly poorer agreement with experiment, whereas force fields predicting different inner/outer binding equilibria remain largely indistinguishable at the current experimental resolution. These results highlight the dominant role of sampling in modelling divalent ion binding and provide a general strategy for integrating simulations with experimental data in complex biomolecular systems.
