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...
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
RiboBridge is a new structure-first framework for designing RNA molecules that bind to target proteins without assuming a predefined binding site. It separates RNA conformational generation from protein–RNA assembly by first proposing candidate binding interfaces on the protein surface, generating canonical RNA conformations in a pretrained RNA latent space (RiboSphere), and then predicting interaction constraints to bridge the RNA and protein coordinate frames. The predicted interaction geometry is converted into a geometrically consistent protein-relative pose via rigid placement, and RiboBridge outperforms existing methods on a benchmark of 124 protein–RNA complexes, achieving higher fNAT and DockQ scores and lower RMSDs.
By Zhou Zhang, Hanqun Cao, Minghao Sun, Odin Zhang, Yang Zhang, Pheng Ann Heng, Wujun Li, Tianfan Fu
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
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.
By Anton E. Dorn, Emile de Bruyn, Fabrice von der Lehr, Philipp Knechtges, Stefan Kesselheim, Alexander Schug
Eco‑RNA is an 80‑million‑parameter RNA foundation model that introduces adjustable inference depth via learned iterative refinement. The shared, input‑conditioned Transformer core repeatedly refines sequence representations, with the number of iterations chosen after training to control effective depth. On the RNAGym fitness benchmark, increasing iterations from one to four improves Spearman correlation from 0.242 to 0.286, and at four iterations Eco‑RNA outperforms the 40‑billion‑parameter Evo 2 model across all aggregate fitness metrics, while also excelling in non‑coding RNA classification, secondary‑structure prediction, and chemical probing profile prediction.
By Pengan Chen, Liang Hong, Xinyuan Liu, Han Huang, Ziqian Lin, Jiayang Chen, Yimin Fan, Jiyue Jiang, Yu Li