Evidence map›Paper›PMID 41491252›Full record

ReviewNature biotechnology2026

Integrated experimental and AI innovations for RNA structure determination.

Wenkai Wang, Baoquan Su, Zhenling Peng, Jianyi Yang

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature biotechnology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Review
  2. De novo design of RNA pseudoknots with deep learning.bioRxiv : the preprint server for biology · 2026
    Article
  3. Review
  4. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Wenkai Wang *MOE Frontiers Science Center for Nonlinear Expectations, State Key Laboratory of Cryptography and Digital Economy Security, Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, Qingdao, China.ORCID http://orcid.org/0000-0001-8603-8250
Baoquan Su *MOE Frontiers Science Center for Nonlinear Expectations, State Key Laboratory of Cryptography and Digital Economy Security, Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, Qingdao, China.ORCID http://orcid.org/0009-0001-6049-6162
Zhenling PengMOE Frontiers Science Center for Nonlinear Expectations, State Key Laboratory of Cryptography and Digital Economy Security, Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, Qingdao, China. zhenling@email.sdu.edu.cn.
Jianyi YangMOE Frontiers Science Center for Nonlinear Expectations, State Key Laboratory of Cryptography and Digital Economy Security, Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, Qingdao, China. yangjy@sdu.edu.cn.ORCID http://orcid.org/0000-0003-2912-7737

Funding

China Postdoctoral Science Foundation BX20240212National Natural Science Foundation of China (National Science Foundation of China) 32430063National Natural Science Foundation of China (National Science Foundation of China) 62501364National Natural Science Foundation of China (National Science Foundation of China) T2222012National Natural Science Foundation of China (National Science Foundation of China) T2225007
6 · The paper itself

Abstract

RNAs act as crucial 'social' mediators within the cell, orchestrating a wide array of biological processes. Their functionality hinges on their complex three-dimensional structures, which dictate stability, binding specificity and molecular interactions. In recent years, a surge of research has focused on solving and/or predicting RNA structures to unlock their functional secrets. However, the dynamic nature and unique physicochemical properties of RNAs pose notable challenges to accurate structural determination. This Perspective reviews recent breakthroughs in RNA structure determination, driven by innovative experimental techniques, such as cryo-electron microscopy, alongside artificial intelligence-based tools inspired by advances in protein structure prediction. We explore how integrative approaches that combine experimental and computational methods are proving particularly powerful in illuminating the RNA world, offering enhanced resolution and scalability. We discuss remaining challenges and opportunities to overcome these hurdles. By integrating experiments with computation, the field is poised to deepen our understanding of RNA biology, paving the way for transformative applications in biotechnology and medicine.

Indexed as

Artificial IntelligenceNucleic Acid ConformationRNAComputational BiologyCryoelectron MicroscopyHumansRNA

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.