Aug 2026· Science· Vol 393 6814, pp.
931-937
· 1 citation· 44 references
Medicine
TL;DR
It is shown that intricate RNA structures can be generated with current deep learning tools through accurate de novo design of pseudoknot secondary structures, suggesting that some difficult RNA design tasks may be tractable without first solving RNA 3D structure prediction.
Abstract
RNA design has been hindered by the limited accuracy of three-dimensional (3D) structure prediction. In this study, we show that intricate RNA structures can be generated with current deep learning tools through accurate de novo design of pseudoknot secondary structures. In an Eterna competition involving 57 pseudoknots, generative artificial intelligence (AI) methods matched experienced human designers in solving most blind challenges, evaluated by single nucleotide-resolution chemical mapping, compensatory mutagenesis, and cryo-electron microscopy. AI-generated molecules with accurate secondary structures formed well-ordered 3D folds stabilized by noncanonical tertiary interactions not modeled during design. Success was guided by an RNet foundation model trained on prior chemical mapping data, suggesting that some difficult RNA design tasks may be tractable without first solving RNA 3D structure prediction.
This PhD research focuses on the development and analysis of Monte Carlo Tree Search (MCTS) and related stochastic search algorithms for de novo RNA design at the tertiary structure level to explore how such methods can efficiently navigate the high-dimensional sequence space while integrating feedback from modern stru...
RPDynaFlow is presented, a flow-matching model to generate conformation ensembles of RNA–protein complexes, trained on 600 ns trajectories of molecular dynamics simulation, which could be treated as a rapid and efficient complement to MD trajectories for studying RNA–protein interactions.
In recent years, deep-learning has revolutionized protein structure prediction, achieving remarkable speed and accuracy. RNA structure prediction, however, has lagged behind. Although several methods have shown moderate success in predicting RNA secondary and tertiary structures, none have reached the accuracy observed...
Conner J. Langeberg, Taehan Kim, Roma Nagle et al.· RNA: A publication of the RN...· 0 citations
Designing RNA sequences that reliably adopt functional three-dimensional structures remains a central challenge in RNA engineering because folding depends on cooperative interactions beyond canonical base pairing. Here we present DS3dRNA, an interaction-based framework for de novo RNA sequence design that combines a th...
UnZipro is presented, an efficient, scalable, and generalizable framework for zero-shot, in silico protein evolution, that integrates a compact, pre-trained inverse folding model with meta-learning to derive family-specific fitness landscapes.
Zhao-Hui Qin, Sanzeng Zhao, Zhao-Long Deng et al.· Molecules and Cells· 0 citations
NucleicBERT is developed, a self-supervised masked-language model that learns contextual representations from single sequences without evolutionary information that advances RNA structure prediction and informs how large language models encode biological information.
Utkarsh Upadhyay, Julian Herold, Markus Götz et al.· Nature Machine Intelligence· 1 citation
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
