arXiv Machine Learning By Anton E. Dorn, Emile de Bruyn, Fabrice von der Lehr, Philipp Knechtges, Stefan Kesselheim, Alexander Schug

mdRNAcat: A Molecular Dynamics RNA Catalog of REST2 Conformational Ensembles

Read the original on arXiv Machine Learning →

mdRNAcat is a curated dataset of all-atom molecular dynamics simulations for 48 diverse RNA systems, each run with replica-exchange with solute scaling (REST2) across 32 Hamiltonian states and 200–300 ns per state. The dataset includes atomic coordinates, instantaneous forces, and forces re-evaluated under the unscaled reference Hamiltonian, along with structural annotations and replica-exchange metadata, all stored in a standardized HDF5 format. The authors analyze conformational sampling, exchange behavior, base pairing, and sugar-pucker distributions to characterize the simulated ensembles, positioning mdRNAcat as a reusable resource for developing and evaluating data-driven RNA models, including coarse-grained force fields.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Machine Learning
Oct 5

RNADyn: A Benchmark for Generating and Understanding RNA Dynamics

RNADynBench is a new standardized benchmark for RNA molecular dynamics, comprising 2,585 high‑quality 100‑nanosecond all‑atom trajectories with carefully controlled data splits. RNADynNet is a unified model that simultaneously generates RNA trajectories and extracts dynamics fingerprints from a single conformer, leveraging coordinate denoising, alignment, and physical grounding. The model achieves strong agreement with MD data, achieving RMSF correlations of 0.875 and 0.766 on test sets, including a high‑flexibility challenge set.

By Yiming Huang, Lennart Bastian, Hanqun Cao, Luis Vollmers, Tolga Birdal
arXiv AI
Oct 9

La-Ribo: RNA Co-Design via Geometry-Latent Flow Matching

La‑Ribo is a generative framework that jointly designs RNA nucleotide sequences and their three‑dimensional structures using geometry‑latent flow matching. It preserves a sparse phosphate‑sugar‑base scaffold while encoding nucleotide identity and local conformation in residue‑wise latent variables, which a shared flow network generates before an RNA‑specific decoder reconstructs all heavy atoms. The authors also built a large, quality‑controlled corpus of 168,561 RNA structures—combining experimental data with predictions from three folding models—to expand supervision, and demonstrate that La‑Ribo outperforms baselines in designability and co‑designability across sampling budgets and refolding models, while also enabling scaffold‑conditioned inverse folding without extra training.

By Runze Ma, Will Hua, Shuangjia Zheng
arXiv AI
Oct 23

REFLEX: RNA Ensemble Generation via Flexibility-Calibrated Stochastic Bridge

REFLEX is a sequence‑conditioned generator that produces RNA backbone‑frame ensembles using a heteroscedastic stochastic bridge on SE(3). It calibrates residue‑wise bridge widths with experimental B‑factors, allowing each observed conformer to be accessed through local perturbation neighborhoods while maintaining sharpness in constrained regions. In inference, REFLEX requires only the input sequence and, on a multi‑conformer RNA benchmark, achieves the best precision‑recall trade‑off for ensemble coverage across evaluation thresholds, with ablation studies showing improved target‑frame recovery under stronger perturbations.

By Wanli Ma, Tianmeng Hu, Ke Li