Designing RNA molecules that bind to target protein structures requires generating both an RNA conformation and its bound complex geometry, without assuming a predefined binding site. Existing approac...
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:2603. 19636v2 Announce Type: replace Abstract: Accurate RNA structure modeling remains difficult because RNA backbones are highly flexible, non-canonical interactions are prevalent, and experimentally determined 3D structures are comparatively scarce.
By Zhou Zhang, Hanqun Cao, Cheng Tan, Fang Wu, Pheng Ann Heng, Tianfan Fu
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
arXiv:2611.04287v2 Announce Type: replace-cross
Abstract: Protein structure prediction has largely focused on native conformations, leaving the continuum of unfolded states, folding intermediates, an...
By Lisheng Zhang, Qingsong Li, Zinan Xie, Yanting Yang, Qikui Yang, Peng Lu, Zhican Fu, Wentao Cui, Runtong Hou, Zhengwei Xie
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