Evidence map›Paper›PMID 39737572›Full record

ArticleBriefings in bioinformatics2024

R3Design: deep tertiary structure-based RNA sequence design and beyond.

Cheng Tan, Yijie Zhang, Zhangyang Gao, Hanqun Cao, Siyuan Li, Siqi Ma, Mathieu Blanchette, Stan Z Li

Abstract read
In one paragraph

Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
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

8 authors.

Cheng TanZhejiang University, Zhejiang, China.
Yijie ZhangSchool of Computer Science, McGill University, Montreal QC H3A 2T8, Canada.
Zhangyang GaoAI Lab, Research Center for Industries of the Future, Westlake University, Zhejiang 310058, China.
Hanqun CaoDepartment of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong, China.
Siyuan LiAI Lab, Research Center for Industries of the Future, Westlake University, Zhejiang 310058, China.
Siqi MaAI Lab, Research Center for Industries of the Future, Westlake University, Zhejiang 310058, China.
Mathieu BlanchetteSchool of Computer Science, McGill University, Montreal QC H3A 2T8, Canada.
Stan Z LiAI Lab, Research Center for Industries of the Future, Westlake University, Zhejiang 310058, China.ORCID 0000-0002-2961-8096

Funding

Center of Synthetic Biology and Integrated Bioengineering of Westlake UniversityNational Natural Science Foundation of China Project 624B2115Science & Technology Innovation 2030 Major Program 2021ZD0150100Westlake University Industries of the Future Research WU2023C019
6 · The paper itself

Abstract

The rational design of Ribonucleic acid (RNA) molecules is crucial for advancing therapeutic applications, synthetic biology, and understanding the fundamental principles of life. Traditional RNA design methods have predominantly focused on secondary structure-based sequence design, often neglecting the intricate and essential tertiary interactions. We introduce R3Design, a tertiary structure-based RNA sequence design method that shifts the paradigm to prioritize tertiary structure in the RNA sequence design. R3Design significantly enhances sequence design on native RNA backbones, achieving high sequence recovery and Macro-F1 score, and outperforming traditional secondary structure-based approaches by substantial margins. We demonstrate that R3Design can design RNA sequences that fold into the desired tertiary structures by validating these predictions using advanced structure prediction models. This method, which is available through standalone software, provides a comprehensive toolkit for designing, folding, and evaluating RNA at the tertiary level. Our findings demonstrate R3Design's superior capability in designing RNA sequences, which achieves around $44\%$ in terms of both recovery score and Macro-F1 score in multiple datasets. This not only denotes the accuracy and fairness of the model but also underscores its potential to drive forward the development of innovative RNA-based therapeutics and to deepen our understanding of RNA biology.

Indexed as

Nucleic Acid ConformationRNASoftwareAlgorithmsBase SequenceComputational BiologyRNA FoldingSequence Analysis, RNARNAartificial intelligencebiomolecular engineeringgraph neural networksinverse foldingRNA

Identifiers

PMID39737572
PMCPMC11685104

What OpenQuestion holds

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Registered trials

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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.