Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2404.02789

RNA Dynamics from Experimental and Computational Approaches

Giovanni Bussi·Massimiliano Bonomi·Paraskevi Gkeka·Michael Sattler·Hashim M. Al-Hashimi·Pascal Auffinger·Maria Duca·Yann Foricher·Danny Incarnato·Alisha N. Jones·Serdal Kirmizialtin·Miroslav Krepl·Modesto Orozco·Giulia Palermo·Samuela Pasquali·Loïc Salmon·Harald Schwalbe·Eric Westhof·Martin Zacharias
View original source ↗Read source document ↗Export metadata ↓

Abstract

Ribonucleic acids (RNA) are unique in that they can store genetic information, replicate and perform catalysis. Importantly, RNA molecules are highly dynamic, and thus determining the ensemble of conformations that they populate is crucial not only to elucidate their biological functions, but also for their potential use as therapeutic targets. Computational and experimental techniques provide complementary views on RNA dynamics, and their integration is fundamental to improve the accuracy of computations and the resolution of experiments. Recent exciting developments in this field, were discussed at the CECAM workshop ``RNA dynamics from experimental and computational approaches'', in Paris, June 26-28, 2023. This report outlines key `take-home' messages that emerged during this workshop from the presentations and discussions.

Explore related subjects

q-bio.BMphysics.bio-ph

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Giovanni Bussi, Massimiliano Bonomi, Paraskevi Gkeka, Michael Sattler, Hashim M. Al-Hashimi, Pascal Auffinger, Maria Duca, Yann Foricher, Danny Incarnato, Alisha N. Jones, Serdal Kirmizialtin, Miroslav Krepl, Modesto Orozco, Giulia Palermo, Samuela Pasquali, Loïc Salmon, Harald Schwalbe, Eric Westhof, Martin Zacharias. 2024-04-03. RNA Dynamics from Experimental and Computational Approaches. https://doi.org/10.1016/j.str.2024.07.019

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Synthesizing State-of-the-Art Structure Predictions from Soup of Co-folding Models

Co-folding models have advanced rapidly, yet no single model consistently performs best across all biomolecular complexes. This raises the question of whether independently trained co-folding models encode complementary information that can be transferred across co-folding models. We introduce SoupFold, which improves co-folding predictions by learning simple mappings between the representation spaces of co-folding models. At inference time, SoupFold transfers and incorporates representations from other co-folding models to update the representation used for structure prediction. Importantly, this does not re-train the co-folding models. We evaluate SoupFold on protein-protein and protein-ligand prediction tasks of FoldBench using AlphaFold3, Protenix, ESMFold2, and OpenDDE. By combining their representations, SoupFold achieves state-of-the-art performance on both protein-protein and protein-ligand structure prediction, showing that independently trained co-folding models encode complementary information that can be effectively transferred across models.

q-bio.BM↗
arXiv2026

Sequence-Informed Geometric Evaluation of RNA 3D Structures

Computational RNA structure pipelines generate many candidate conformations for the same sequence. Reliable evaluation therefore requires more than recognising plausible geometry, it requires determining whether that geometry is compatible with the sequence. We introduce SIRGE, a sequence-informed geometric evaluator that conditions structural representations on nucleotide embeddings from a pretrained RNA language model. Early results show that SIRGE outperforms established evaluators in Kendall--$τ$ alignment, Top-1 selection, and Top-3 ranking. Controlled comparisons further show that sequence conditioning corrects errors made by an otherwise matched geometric model and improves target-level rank structure. These findings provide initial evidence that pretrained sequence representations supply ranking information that complements geometric reasoning.

q-bio.BM↗
arXiv2026

PocketVE: Stable and Property-Guided Structure-Based Drug Design with Variance-Exploding Diffusion

Protein-conditioned 3D molecule generation is a central challenge in structure-based drug design, requiring a balance between pocket compatibility, molecular properties, and physical geometry. We propose \textbf{PocketVE}, a protein-pocket-conditioned variance-exploding (VE) diffusion framework that couples stable coordinate denoising with inference-time property guidance. Specifically, PocketVE combines an EDM-style training and sampling setup for 3D denoising, classifier-free guidance for multi-property steering without external property classifiers, and adaptive protein perturbation as a training-time pocket regularizer. Evaluated on CrossDocked2020 under the GenBench3D protocol, PocketVE improves Valid$_{3\text{D}}$ from 58.6 to 80.6 and reduces strain energy from 457.4 to 127.9 relative to its TAGMol architectural baseline, while retaining competitive docking and molecular-property scores under moderate guidance. A guidance-scale study shows that moderate guidance gives a favorable balance between target-related objectives and geometric quality, whereas stronger guidance can degrade geometry and distributional fidelity. Pocket-permutation and PoseCheck diagnostics further support pocket-specific spatial compatibility with reduced steric conflicts. Overall, the results suggest that geometric stability and inference-time property guidance should be considered as coupled design objectives.

q-bio.BM↗