ArticleNature communications2025
NuFold: end-to-end approach for RNA tertiary structure prediction with flexible nucleobase center representation.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
34 citing papers in PubMed.
- Crosstalk between RNA secondary and three-dimensional structure prediction: a comprehensive study.RNA biology · 2026Article
- Hierarchical breakdown of RNA structure prediction in CASP16: from reliable local helices to speculative multimer assembly.Bioinformatics (Oxford, England) · 2026Article
- The trRosettaRNA server for RNA structure prediction.Nature protocols · 2026Review
- The Advantages of AI for Computational Protein Studies and Looking Ahead at the Next Challenges: Single Structures Are Not Enough.Journal of molecular biology · 2026Review
- Modeling conformational transitions in DNA, RNA, and protein-nucleic acid complexes.Nucleic acids research · 2026Article
- Limits of deep-learning-based RNA prediction methods.Nucleic acids research · 2026Article
- RNAbpFlow: base pair-augmented SE(3) flow matching for conditional RNA 3D structure generation.Nature methods · 2026Article
- The Use of Deep Learning in RNA Therapeutic Development.ACS nano · 2026Review
- CDsyn: A comprehensive database for deleterious human synonymous variation prediction.iScience · 2026Article
- Iterative design of a NAND hybrid riboswitch by deep batch Bayesian optimization.Nucleic acids research · 2026Article
- Computational approaches for RNA structure prediction and design.Cell reports. Physical science · 2026Article
- gCoSRNA: Generalizable Coaxial Stacking Prediction for RNA Junctions Using Secondary Structure.Biomolecules · 2026Article
- The FsrA-Mediated Iron-Sparing Response Regulates the Biosynthesis of the Epipeptide EPE in Bacillus subtilis.Molecular microbiology · 2026Article
- Integrated experimental and AI innovations for RNA structure determination.Nature biotechnology · 2026Review
- Quality assessment of RNA 3D structure models using deep learning and intermediate 2D maps.Communications biology · 2026Article
- TiRNA: a coarse-grained method with temperature and ion effects for RNA structure folding and prediction.Nucleic acids research · 2026Article
- Structure Modeling Protocols for Protein Multimer and RNA in CASP16 With Enhanced MSAs, Model Ranking, and Deep Learning.Proteins · 2026Article
- Article
- Deep Learning in Modeling Tools for Structural Insights into Protein-RNA Complexes, Bridging Computational and Spectroscopic Approaches.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Bioinformatics Approaches in Noncoding RNAs Research.Experientia supplementum (2012) · 2026Review
Corrections and comments
- Update of
Authors and funding
7 authors.
Funding
Abstract
RNA plays a crucial role not only in information transfer as messenger RNA during gene expression but also in various biological functions as non-coding RNAs. Understanding mechanical mechanisms of function needs tertiary structure information; however, experimental determination of three-dimensional RNA structures is costly and time-consuming, leading to a substantial gap between RNA sequence and structural data. To address this challenge, we developed NuFold, a novel computational approach that leverages state-of-the-art deep learning architecture to accurately predict RNA tertiary structures. NuFold is a deep neural network trained end-to-end for the output structure from the input sequence. NuFold incorporates a nucleobase center representation, which enables flexible conformation of ribose rings. Benchmark study showed that NuFold clearly outperformed energy-based methods and demonstrated comparable results with existing state-of-the-art deep-learning-based methods. NuFold exhibited a particular advantage in building correct local geometries of RNA. Analyses of individual components in the NuFold pipeline indicated that the performance improved by utilizing metagenome sequences for multiple sequence alignment and increasing the number of recycling. NuFold is also capable of predicting multimer complex structures of RNA by linking the input sequences.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.