Journal of Chemical Theory and ComputationBiophysicsPeer-reviewed journal article
Toward Accurate RNA Folding Thermodynamics: Evaluation of Enhanced Sampling Methods for Force Field Benchmarking
Petra KührováiD, Vojtěch MlýnskýiD, Ivo FrébortiD, Jitka FrébortováiD, Michal OtyepkaiD, Jiřı́ ŠponeriD, Pavel BanášiD
Abstract
Biologically functional RNAs operate near marginal stability, and their rugged free-energy landscapes and profound structural dynamics - typically not captured by structural biology experiments - play decisive roles. Atomistic molecular dynamics (MD) simulations provide a unique means to characterize these features. However, the applicability of atomistic MD is currently limited by accessible simulation timescales and, most importantly, by force-field (FF) accuracy. Folding free energies ({Delta}G{degrees}fold) of small RNA motifs represent well-defined targets for quantitative benchmarking of RNA FFs. In practice, however, obtaining thermodynamic estimates that are sufficiently robust for direct comparison with experimental data remains highly challenging, even for small RNA systems, and many published studies rely on sampling that is not fully converged. Here, we systematically assess the performance of widely used advanced enhanced-sampling techniques using the 8-mer r(gcGAGAgc) tetraloop as a representative benchmark system. We test temperature replica exchange (T-REMD), two solute-tempering variants of replica exchange (REST2 and REHT), as well as well-tempered metadynamics and on-the-fly probability enhanced sampling combined with solute tempering (ST-MetaD and ST-OPES). Among the tested approaches, T-REMD proves to be the most robust, yielding reproducible folding equilibria and consistent estimates of {Delta}G{degrees}fold after approximately 20 s of simulation time, independent of the initial folded or unfolded conformational ensemble. Our results provide practical guidelines for selecting sampling protocols suitable for quantitative RNA benchmarks and lay the foundation for systematic validation and future refinement of RNA FFs.
- assay: multiome
- research focus: benchmarking
- approach: deep learning
- approach: statistical model
- approach: simulation
- Citations
- 0
- FWCI
- 0.00
- Field percentile
- 26th percentile
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