Aug 2026· Journal of Chemical Theory and Computation· 0 citations· 81 references
TL;DR
This work assesses ML potentials for exploring RNA conformations using the adenine–adenine dinucleoside monophosphate (ApA) dimer, a fundamental RNA building block, and parametrized ML potentials based on the equivariant MACE architecture and informed by both ab initio and semiempirical property data.
Abstract
RNA is a flexible biopolymer that adopts diverse conformations while forming structural motifs essential for its function. Classical RNA force fields often show limited transferability and inefficient sampling of transitions between stable states, particularly in moderately large RNA. To address these limitations, quantum-informed machine learning (ML) potentials have recently emerged as a promising alternative, offering improved accuracy and transferability relative to classical force fields. Here, we assess ML potentials for exploring RNA conformations using the adenine–adenine dinucleoside monophosphate (ApA) dimer, a fundamental RNA building block. We generated an extensive quantum-mechanical (QM) dataset of physicochemical properties for ApA conformations obtained from temperature replica exchange molecular dynamics (TREMD) simulations. Despite its small size, the ApA dimer exhibits a complex energetic landscape with six well-defined conformational clusters in which quantum effects and solvent-mediated interactions play a crucial role. Using this dataset, we parametrized ML potentials based on the equivariant MACE architecture and informed by both ab initio and semiempirical property data. The resulting potentials reproduce key conformational features of the ApA system, including base stacking, sugar geometry, and backbone flexibility, and provide broader coverage of structural transitions than the general-purpose SO3LR and MACE-POLAR-1 models. These findings underscore the importance of comprehensive QM datasets for RNA building blocks to support the structural and energetic characterization of RNA complexes and emphasize the need for robust and efficient validation metrics for ML potentials.
RNA molecules explore heterogeneous conformational ensembles that are essential for their biological function and molecular recognition, yet this intrinsic flexibility poses a major challenge for structure-based drug discovery. In particular, the absence of well-defined binding pockets in static structures limits the identification of ligandable sites. Here, we present an integrative ensemble-based approach that combines enhanced-sampling molecular dynamics simulations with Nuclear Magnetic Resonance data to characterize the conformational landscape of the HIV-1 TAR RNA at atomic resolution. Starting from extensive sampling, we refined the resulting conformational distribution through maximum-entropy reweighting to achieve quantitative agreement with experimental data. Analysis of the reweighted ensemble reveals a diverse set of conformational substates, including compact arrangements that exhibit pocket features compatible with ligand recognition and overlap with known ligand-bound structures. At the same time, highly ligandable conformations, which are only marginally populated, might nonetheless be critical for RNA recognition. Our results demonstrate that integrative ensemble modeling can reveal pharmacologically relevant RNA conformations that are not apparent from experimental static structures, providing a framework for ensemble-based strategies in RNA-targeted drug discovery.
Stefano Bosio, Vincent Schnapka, Mattia Bernetti et al.· bioRxiv· 0 citations
Fluorescent base analogs, such as 2-aminopurine (2AP), are frequently used to investigate nucleic acid conformational dynamics, particularly base-stacking interactions, through fluorescence spectroscopy. Although 2AP substitution can induce structural perturbations, properly accounting for these effects enables its use as a probe of complex RNA dynamics. Here, we apply explainable machine-learning-derived surrogate-model collective variables to perform enhanced-sampling simulations that comprehensively explore the free-energy landscapes of both 2AP-substituted and unsubstituted RNA dinucleotides and trinucleotides. Our results show that while 2AP substitution does not significantly alter stacking propensity, it substantially modulates the free energy landscape, leading to a redistribution of populations among stacking modes. The fraction of stacked 2AP conformations closely correlates with experimentally observed “dark state” populations, supporting the hypothesis that base stacking quenches 2AP fluorescence and promotes dark state formation. This work and follow-up studies in larger, physiologically relevant RNA systems will establish the ability to correlate observed emissive or dark states of 2AP with specific structures within the free-energy landscape.
Revanth Elangovan, Emily Stetson, J. Widom et al.· Physical Chemistry, Chemical...· 0 citations
Pi-Ensemble (Predicting Interpolated Ensemble), a sequence-guided framework for generating protein conformational ensembles interpolating between two structural anchor states, provides an extensible framework for studying protein flexibility, guiding adaptive sampling, and accelerating mechanistic investigations of protein function.
Hassan Nadeem, D. Kleiman, Yuming Zhou et al.· bioRxiv· 0 citations
Accurate determination of RNA conformational ensembles is essential for understanding RNA function and advancing RNA-targeted drug discovery, yet lowly-populated alternative states remain difficult to resolve with atomistic detail. A central constraint is that experimental refinement can only select conformations already present in the starting library, making library generation the limiting step. Using the HIV-1 trans-activation response element (TAR) as a model system, we benchmarked conventional MD (cMD) against the enhanced sampling methods Gaussian-accelerated MD (GaMD), replica-exchange Gaussian-accelerated MD (Rex-GaMD), replica-exchange with solute tempering (REST2), and temperature replica-exchange MD (T-REMD), as well as the structure-prediction based methods FARFAR2 and AlphaFold 3. Each library was refined against experimental residual dipolar couplings (RDC) and validated independently using ensemble-averaged QM/MM chemical shifts. We showed that T-REMD produced the most accurate ensemble by both measures, and its advantage tracked with broader, more continuous coverage of the interhelical conformational landscape. Broad temperature-range T-REMD also sampled conformations resembling excited state 1 (ES1) and the U23-A27-U38 base-triple, without requiring these states to be specified during library generation. More accurate ensembles further improved coverage of experimentally observed ligand-bound TAR conformations and enhanced ensemble-based virtual screening, linking structural accuracy to functional utility. The same workflow applied to the preQ1 class I riboswitch and the UUCG tetraloop improved agreement with experimental data in both cases. Together, these results establish replica-exchange enhanced sampling, particularly T-REMD, as an effective strategy for constructing experimentally validated RNA ensembles and accessing conformations corresponding to rare functional substates. Graphical Abstract
It is demonstrated that BioEmu can generate plausible conformational ensembles for relatively large, six-and seven-pass membrane proteins, sampling rare states at a fraction of the computational cost of conventional MD simulations, suggesting that AI-based ensemble generation could provide an accessible approach for exploring membrane protein dynamics and complement conventional molecular modelling approaches.
B. Clifton, Adam G Grieve, Robin A. Corey· bioRxiv· 0 citations
A generalized essential dynamics-refined ENM (edENM) is introduced for both DNA, RNA, and protein-nucleic acid complexes, parametrized against a diverse set of molecular dynamics simulations and validated using experimental ensembles from nuclear magnetic resonance, X-ray crystallography, and cryogenic electron microscopy.
Domenico Scaramozzino, Marco Cannariato, Byung Ho Lee et al.· Nucleic Acids Research· 0 citations