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
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
The paper introduces SIRGE, a sequence-informed geometric evaluator for RNA 3D structures that integrates nucleotide embeddings from a pretrained RNA language model into structural representations. SIRGE demonstrates superior performance over existing evaluators in Kendall–τ alignment, Top‑1 selection, and Top‑3 ranking. Controlled experiments reveal that sequence conditioning corrects errors of a purely geometric model and enhances target‑level ranking, suggesting that pretrained sequence representations provide complementary ranking information to geometric reasoning.
By Andrea Zerio, Yighua Yao, Alessandro Micheli, Roland G. Huber, Mile Sikic, Samir Bhatt, Andres R. Masegosa, Yuangang Pan
SymFold introduces a symmetric dual‑path architecture that combines protein language models (PLMs) and multimodal protein language models (MPLMs) to iteratively guide protein sequence generation for inverse folding. By leveraging pretrained sequence evolution knowledge from PLMs and structural knowledge from MPLMs, the method improves upon the traditional serial pipeline where structure encoders produce coarse sequences refined by PLMs. Experiments on standard inverse‑folding benchmarks show state‑of‑the‑art performance, and ablation studies confirm the effectiveness of the symmetric design.
By Handong Wang, Jiaxin Qi, Baisheng Lai, Jianqiang Huang
arXiv:2609.36885v1 Announce Type: cross
Abstract: RNA design aims to identify sequences that fold into specified secondary structures. Existing methods formulate the task as target-specific search or...
By Zefeng Lin, Xianyong Fang, Tianfan Fu, Xiaohua Xu