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.
Abstract
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
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
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.
Leonardo Medrano Sandonas, Macarena Tolmos Nehme, L. F. Cofas-Vargas et al.· Journal of Chemical Theory a...· 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, Yu-Ming Zhou et al.· bioRxiv· 0 citations
Carbonara is presented, a framework that uses experimental small-angle X-ray scattering (SAXS) data to predict alternative physically plausible protein conformations and provides a route from static structural models of flexible multi-domain proteins and multimeric assemblies to solution-state ensembles.
Molecular dynamics (MD) simulations have become an increasingly important component of modern medicinal chemistry and structure-based drug discovery, providing atomistic insight into protein-ligand interactions that extends beyond static experimental structures and docking models. By explicitly accounting for conformational flexibility, solvent effects, and time-dependent behaviour, MD simulations enable the refinement of binding poses, the identification of transient and allosteric sites, and the quantitative estimation of binding thermodynamics and kinetics, the latter increasingly accessible through Markov state models (MSMs) and milestoning approaches that reconstruct long-timescale behaviour from ensembles of short trajectories. In this mini-review, we provide a practical overview of classical atomistic MD methodologies commonly used in medicinal chemistry, including force-field-based simulations, enhanced sampling techniques, and free-energy calculation methods such as alchemical and end-point approaches. Emphasis is placed on the strengths and limitations of each technique, with particular attention to their appropriate use across different stages of the drug discovery pipeline. We further discuss best practices for system preparation, simulation protocol design, convergence assessment, and reproducibility, highlighting common pitfalls that can lead to overinterpretation of simulation results. Selected examples illustrate how MD simulations have informed medicinal chemistry decisions in lead identification and optimisation. Finally, we briefly outline emerging directions, including the integration of machine learning, ensemble-based approaches, and next-generation force fields, which are expected to further expand the role of MD simulations in medicinal chemistry.
S. S. Çınaroğlu· Mini-Reviews in Medical Chem...· 0 citations
RNA molecules perform diverse biological functions by dynamically exploring multiple conformational states rather than adopting a single static structure. Capturing these ensembles is a challenge in molecular biology. Recent advances in cryoEM are now transforming this landscape by enabling the visualization of RNA molecules across a broad spectrum of functionally relevant conformations at near atomic resolution. Here, we examine how cryoEM is reshaping RNA structural biology changing focus from the analysis of static structures to dynamic conformational landscapes. Through 8 representative case studies we illustrate how cryoEM has revealed previously inaccessible mechanisms of RNA motion, including folding processes, ligand-dependent switching, and cooperative assembly. We specifically discuss emerging experimental and computational approaches that address and overcome the challenges associated with studying dynamic RNAs, particularly in construct design, sample preparation, vitrification, and data analysis. These novel methods resolve conformational variability and enable the reconstruction of discrete and continuous RNA conformational landscapes from cryoEM data, highlighting how structural heterogeneity can be harnessed to extract functional insights. Looking forward, the integration of cryoEM with complementary biophysical techniques and time resolved methodologies promises to bridge structural and temporal resolution, to routinely derive experimental molecular movies of RNA in action. These advances will not only deepen our understanding of RNA biology but also provide new opportunities for RNA targeted therapeutics and the rational design of dynamic RNA-based nanodevices. By connecting structural snapshots into coherent dynamic models, cryoEM is establishing a framework for quantitative descriptions of RNA energy landscapes and their functional roles.
Shekhar Jadhav, S. Saha, Qing-Bin Shang et al.· 0 citations
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