Jul 2026· Physical Chemistry, Chemical Physics - PCCP· Vol 28, pp. 18062 - 18071· 0 citations· 58 references
Medicine
TL;DR
The 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 and supporting the hypothesis that base stacking quenches 2AP fluorescence and promotes dark state formation.
Abstract
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.
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
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
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.
Results show that sequence changes outside the binding site can modulate ligand binding indirectly, and that the ligand interaction network is useful for evaluating edited aptamers.
Aamir Aman, Leonhard Sidl, Nitchakan Darai et al.· International Journal of Mol...· 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
The sequence-dependent mechanical landscapes of double-stranded nucleic acid (dsNA) remain largely unexplored beyond canonical dsDNA. We describe cgNA+, a coarse-grained predictive model of the mechanics of dsRNA, DNA:RNA hybrids, and epigenetically modified dsDNA, all parameterised from 1.26 milliseconds of atomistic simulations. cgNA+ predicts non-local sequence-dependent equilibrium shape and stiffness with errors an order of magnitude smaller than sequence-variability, while enabling exploration of numbers of sequences inaccessible to atomistic simulation. We show that dsNA equilibrium shape is strongly influenced by flanking sequence up to octamer context, with flexible dimer-steps more context-sensitive. CpG-modification alters equilibrium shape comparable to changes caused by single-nucleotide polymorphisms. Groove width analysis across dsNA decamers reveals strong sequence dependence, reflecting the differing characteristic helical geometry of dsDNA and dsRNA, whereas DRHs exhibit mixed behaviour depending on DNA-strand pyrimidine content. CTCF binding sites exhibit a distinct groove width signature. Persistence-length spectra from ∼ 9 million sequences indicate that dsRNA is stiffer than dsDNA, whereas DRH exhibit intermediate stiffness modulated by DNA strand pyrimidine content. Persistence length increases upon CpG-modification, but decreases on hypermodification. Overall, the cgNA+ model enables a first, highly accurate, very large-scale, comparative study of sequence-dependent mechanics both within and across dsNA classes, demonstrating previously hidden regulatory layers. Graphical Abstract
Rahul Sharma, A. Patelli, R. Singh et al.· bioRxiv· 0 citations
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