Evidence map›Paper›PMID 32670749›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2020

A Loop-Based and AGO-Incorporated Virtual Screening Model Targeting AGO-Mediated miRNA-mRNA Interactions for Drug Discovery to Rescue Bone Phenotype in Genetically Modified Mice.

Zhenjian Zhuo, Youyang Wan, Daogang Guan, Shuaijian Ni, Luyao Wang, Zongkang Zhang, Jin Liu, Chao Liang, Yuanyuan Yu, Aiping Lu and 2 more

Open access · goldAbstract read
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.

0numbers the graph read from it
0cells of the map it votes in
44citing papers in PubMed
8.9field-weighted citation impact, top 1% of its field
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

44 citing papers in PubMed, 133 citations in OpenAlex.

  1. Machine Learning for RNA-Targeting Drug Design.Journal of chemical information and modeling · 2026
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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

12 authors at 2 institutions in 3 countries.

Zhenjian ZhuoSchool of Chinese Medicine Faculty of Medicine The Chinese University of Hong Kong Hong Kong SAR China.
Youyang WanLaw Sau Fai Institute for Advancing Translational Medicine in Bone & Joint Diseases School of Chinese Medicine Hong Kong Baptist University Hong Kong SAR China.
Daogang GuanLaw Sau Fai Institute for Advancing Translational Medicine in Bone & Joint Diseases School of Chinese Medicine Hong Kong Baptist University Hong Kong SAR China.
Shuaijian NiLaw Sau Fai Institute for Advancing Translational Medicine in Bone & Joint Diseases School of Chinese Medicine Hong Kong Baptist University Hong Kong SAR China.
Luyao WangLaw Sau Fai Institute for Advancing Translational Medicine in Bone & Joint Diseases School of Chinese Medicine Hong Kong Baptist University Hong Kong SAR China.
Zongkang ZhangSchool of Chinese Medicine Faculty of Medicine The Chinese University of Hong Kong Hong Kong SAR China.
Jin LiuLaw Sau Fai Institute for Advancing Translational Medicine in Bone & Joint Diseases School of Chinese Medicine Hong Kong Baptist University Hong Kong SAR China.
Chao LiangLaw Sau Fai Institute for Advancing Translational Medicine in Bone & Joint Diseases School of Chinese Medicine Hong Kong Baptist University Hong Kong SAR China.
Yuanyuan YuLaw Sau Fai Institute for Advancing Translational Medicine in Bone & Joint Diseases School of Chinese Medicine Hong Kong Baptist University Hong Kong SAR China.
Aiping LuLaw Sau Fai Institute for Advancing Translational Medicine in Bone & Joint Diseases School of Chinese Medicine Hong Kong Baptist University Hong Kong SAR China.
Ge ZhangLaw Sau Fai Institute for Advancing Translational Medicine in Bone & Joint Diseases School of Chinese Medicine Hong Kong Baptist University Hong Kong SAR China.ORCID https://orcid.org/0000-0002-7807-7695
Bao-Ting ZhangSchool of Chinese Medicine Faculty of Medicine The Chinese University of Hong Kong Hong Kong SAR China.
Hong Kong Baptist University · HKChinese University of Hong Kong · HK

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Several virtual screening models are proposed to screen small molecules only targeting primary miRNAs without selectivity. Few attempts have been made to develop virtual screening strategies for discovering small molecules targeting mature miRNAs. Mature miRNAs and their specific target mRNA can form unique functional loops during argonaute (AGO)-mediated miRNA-mRNA interactions, which may serve as potential targets for small-molecule drug discovery. Thus, a loop-based and AGO-incorporated virtual screening model is constructed for targeting the loops. The previously published studies have found that miR-214 can target ATF4 to inhibit osteoblastic bone formation, whereas miR-214 can target TRAF3 to promote osteoclast activity. By using the virtual model, the top ten candidate small molecules targeting miR-214-ATF4 mRNA interactions and top ten candidate small molecules targeting miR-214-TRAF3 mRNA interactions are selected, respectively. Based on both in vitro and in vivo data, one small molecule can target miR-214-ATF4 mRNA to promote ATF4 protein expression and enhance osteogenic potential, whereas one small molecule can target miR-214-TRAF3 mRNA to promote TRAF3 protein expression and inhibit osteoclast activity. These data indicate that the loop-based and AGO-incorporated virtual screening model can help to obtain small molecules specifically targeting miRNA-mRNA interactions to rescue bone phenotype in genetically modified mice.

Indexed as

bone rescuedrug discoverymiRNA–mRNA interactionssmall molecule screeningvirtual screening models

Identifiers

PMID32670749
PMCPMC7341099
OpenAlexW3030723095

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.