Comparative Assessment of DNA Force Fields for Small-Molecule Ligand Binding via Multicanonical MD-Based Dynamic Docking Simulations.

Integrative Ensemble Modeling reveals RNA conformations targetable by small molecules

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.

Reconsidering Molecular Docking Practices in Aptamer Research

Molecular docking is increasingly used to infer aptamer–target interactions, yet most studies rely on computationally predicted aptamer structures rather than experimentally determined ones. Using a benchmark set of aptamers with known high‐resolution structures, we show that commonly used modeling approaches, including RNAComposer and AlphaFold3, fail to reliably reproduce aptamer conformations, particularly at the binding sites critical for molecular recognition. Key limitations include the use of A‐form RNA models to represent B‐form DNA structures, the prediction of ligand‐free rather than ligand‐bound conformations, and the scarcity of experimentally determined aptamer structures for training machine‐learning models. Using the theophylline aptamer, for which high‐resolution structures are available in both DNA and RNA forms, we systematically evaluated each step of the standard docking workflow. We found that structure‐prediction errors generate incorrect binding pockets, docking scores fail to distinguish theophylline from caffeine despite a 250,000‐fold difference in affinity, and molecular dynamics simulations do not overcome these shortcomings. Together, these results reveal fundamental weaknesses in current aptamer docking workflows and caution against using docking‐derived models to infer binding mechanisms in the absence of experimental structural data.

Probing the molecular determinants of emission enhancement in RNA aptamer-metal complex systems.

Aptamer recognition of metal complexes is distinct from organic molecules, as it requires the nucleic acid to provide complementary interactions based on both the metal-centered geometry and the steric and electronic features of the ligands. Molecular determinants for aptamer recognition, specificity, and productive binding of coordination complexes are poorly understood. Here, we examined the promiscuity and discrimination of the 12Rd5 aptamer, evolved to bind [Ru(bpy)3]2+ (bpy = 2,2'-bipyridine), using structurally related metal complexes. Emission enhancement, equilibrium dialysis, and isothermal titration calorimetry revealed that 12Rd5 accommodates changes in the metal center and net charge on the complex but strongly discriminated between different ligands. Compounds 1-3 exhibited emission enhancement and formed long-lived interactions with the aptamer, with half-lives exceeding 20 h, while analogs with expanded π-surfaces or additional heteroatoms in the ligands failed to engage the aptamer productively. Mutagenesis and truncation of the RNA revealed that even minimal perturbations abolish binding, indicating that recognition is governed by a highly cooperative global fold rather than local sequence motifs. AlphaFold 3 modeling and docking provided limited structural insight, highlighting persistent challenges in RNA structure prediction and a need for additional training data for predictions involving metal complexes. These results demonstrate that a single evolved aptamer can accommodate multiple coordination compounds within specific geometric and electrostatic constraints, providing foundational information for the repurposing or redesign of hybrid inorganic-aptamer-based sensors, imaging agents, and metal-mediated biochemical tools.

Thermodynamic Evaluation of Optimized Heparin–DNA Origami Nanostructures Targeting the SARS-CoV-2 Spike RBD Using MM/PBSA–IE

Although DNA origami scaffolds functionalized with antiviral ligands have been explored as potential platforms against SARS-CoV-2, the equilibrium thermodynamic behavior of heparin-bearing DNA origami constructs in interaction with the spike receptor-binding domain (RBD) remains incompletely understood. Building upon a previous steered molecular dynamics (SMD) study, we performed an equilibrium MD-based analysis of U-shaped DNA origami nanocages functionalized with heparin oligosaccharides of different lengths, including tetra-, hexa-, and decasaccharide chains, using all-atom molecular dynamics simulations and MM/PBSA–Interaction Entropy (MM/PBSA-IE) free-energy calculations. Molecular docking was first used to generate plausible initial binding poses, and the docking results showed a qualitative trend consistent with the subsequent MD-based free-energy estimates. Each RBD-containing complex was simulated in three independent 100 ns replicas to improve statistical reliability. Among the modeled systems, the decasaccharide-functionalized complex showed the most favorable predicted tethered heparin–RBD binding free energy (ΔG bind = −110.1 ± 14.6 kJ/mol), together with the greatest total heparin–RBD contact formation. This relative ranking was qualitatively consistent with the trend observed in the earlier SMD study. However, because the three ligands differ simultaneously in oligosaccharide length, molecular size, total negative charge, and number of available interaction sites, the observed ranking cannot be attributed uniquely to chain length or interpreted as evidence of greater size-normalized interaction efficiency. Overall, these results provide a relative computational ranking of the three modeled tethered-heparin systems under the selected binding configurations. Additional validation using free-heparin controls, more complete spike models, alternative docking poses, and experimental assays is required to confirm biological relevance and antiviral activity.

Exploring Nucleic Acid Structure and Function with Molecular Dynamics and Fragment Molecular Orbital Methods.

Efficient Force Evaluation via Fragmentation: Toward Geometry Optimization of Protein-Ligand Systems with Quantum Mechanical Accuracy.

This work presents a hybrid fragmentation-based approach, FragQMMM, that enables efficient and accurate force evaluation for large molecular systems, and successfully reproduces the hydrogen-bond network and preserving the underlying structure-activity relationship.