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
LapDDPM is a conditional Graph Diffusion Probabilistic Model that generates high‑fidelity, biologically plausible single‑cell RNA sequencing data. It incorporates graph‑based inductive biases and a spectral adversarial perturbation mechanism to enforce robustness against structural noise, effectively acting as a Distributionally Robust Optimization framework. The model extends to spatial transcriptomics and multi‑modal data, and experimental results on datasets such as PBMC3K, Dentate Gyrus, HLCA, Visium, and 10x Multiome show it outperforms state‑of‑the‑art baselines in distribution matching, manifold preservation, and downstream utility.
By Lorenzo Bini, Stephane Marchand-Maillet
arXiv:2608. 16094v1 Announce Type: new Abstract: Accurate protein structure prediction is fundamental to structural biology because protein structure underlies molecular function and provides a basis for mechanistic interpretation.
By Wengan He, Yongsheng Luo, Lihong Jiang, Wenhui Xu, Yu Li
arXiv:2602. 06020v3 Announce Type: replace Abstract: How do protein structure prediction models fold proteins?
By Kevin Lu, Jannik Brinkmann, Stefan Huber, Aaron Mueller, Yonatan Belinkov, David Bau, Chris Wendler
The paper introduces MCTH (Monte Carlo Tree Hallucination), an inference-only framework that performs all‑atom biomolecular sequence‑structure co‑design by treating pretrained folding and inverse‑folding models as black‑box operators. MCTH uses Monte Carlo Tree Search to allocate a fixed inference budget across competing design trajectories, incorporating model confidence, uncertainty, and cross‑expert consensus. Experiments across protein‑RNA, protein‑DNA, protein‑protein, and protein‑ligand design show that adaptive search outperforms simpler sampling strategies, and evaluations with AlphaFold3 and Chai‑1 demonstrate transferability beyond the search‑time oracle.
By Xuefeng Liu, Mingxuan Cao, Xiao Luo, Songhao Jiang, Tobin Sosnick, Jinbo Xu, Louis Maher, Rick Stevens
arXiv:2607. 03787v1 Announce Type: new Abstract: Accurately modeling biomolecular interactions is a central bottleneck in biology and therapeutic discovery.
By Aureka AI OpenDDE project
arXiv:2410.20317v2 Announce Type: replace
Abstract: Molecular dynamics (MD) simulations are a principled but computationally expensive approach for studying protein conformational variability, making...
By Siddharth Viswanath, Xingzhi Sun, Lucas Lee, Danqi Liao, Hiren Madhu, David R. Johnson, Jo\~ao Felipe Rocha, Egbert Castro, Jackson D. Grady, Michael Perlmutter, Dhananjay Bhaskar, Smita Krishnaswamy