Evidence map›Paper›PMID 35228528›Full record

ArticleBone research2022

Generation of functional oligopeptides that promote osteogenesis based on unsupervised deep learning of protein IDRs.

Mingxiang Cai, Baichuan Xiao, Fujun Jin, Xiaopeng Xu, Yuwei Hua, Junhui Li, Pingping Niu, Meijing Liu, Jiaqi Wu, Rui Yue and 5 more

Open access · goldAbstract read
In one paragraph

Article in Bone research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
1.2field-weighted citation impact, top 23% 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

8 citing papers in PubMed, 18 citations in OpenAlex.

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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

15 authors at 6 institutions in 1 country.

Mingxiang Cai *Department of Oral Implantology, School of Stomatology, Tongji University, Shanghai Engineering Research Center of Tooth Restoration and Regeneration, Shanghai, 200072, China.
Baichuan Xiao *Key Laboratory of Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing, 100191, China.
Fujun Jin *The First Affiliated Hospital of Jinan University, School of Stomatology, Clinical Research Platform for Interdiscipline of Stomatology, Jinan University, Guangzhou, 510630, China.
Xiaopeng Xu *Guangzhou Laboratory, Bioland Laboratory, Guangzhou Regenerative Medicine and Health Guangdong Laboratory, Guangzhou, 510320, China.
Yuwei HuaInstitute for Regenerative Medicine, Shanghai East Hospital, Shanghai Key Laboratory of Signaling and Disease Research, Frontier Science Center for Stem Cell Research, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
Junhui LiDepartment of Oral Implantology, School of Stomatology, Tongji University, Shanghai Engineering Research Center of Tooth Restoration and Regeneration, Shanghai, 200072, China.
Pingping NiuDepartment of Oral Implantology, School of Stomatology, Tongji University, Shanghai Engineering Research Center of Tooth Restoration and Regeneration, Shanghai, 200072, China.
Meijing LiuKey Laboratory of Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing, 100191, China.
Jiaqi WuKey Laboratory of Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing, 100191, China.
Rui YueInstitute for Regenerative Medicine, Shanghai East Hospital, Shanghai Key Laboratory of Signaling and Disease Research, Frontier Science Center for Stem Cell Research, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
Yong ZhangInstitute for Regenerative Medicine, Shanghai East Hospital, Shanghai Key Laboratory of Signaling and Disease Research, Frontier Science Center for Stem Cell Research, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
Zuolin WangDepartment of Oral Implantology, School of Stomatology, Tongji University, Shanghai Engineering Research Center of Tooth Restoration and Regeneration, Shanghai, 200072, China. zuolin@tongji.edu.cn.
Yongbiao ZhangKey Laboratory of Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing, 100191, China. zhangyongbiao@gmail.com.
Xiaogang WangThe First Affiliated Hospital of Jinan University, School of Stomatology, Clinical Research Platform for Interdiscipline of Stomatology, Jinan University, Guangzhou, 510630, China. xiaogangwang@buaa.edu.cn.
Yao SunDepartment of Oral Implantology, School of Stomatology, Tongji University, Shanghai Engineering Research Center of Tooth Restoration and Regeneration, Shanghai, 200072, China. yaosun@tongji.edu.cn.ORCID http://orcid.org/0000-0002-4323-0506
Tongji University · CNShanghai East Hospital · CNBeihang University · CNFirst Affiliated Hospital of Jinan University · CNGuangzhou Regenerative Medicine and Health Guangdong Laboratory · CNStomatology Hospital · CN

Funding

National Natural Science Foundation of China (National Science Foundation of China) 8206113022, 92049201, 81770873, 81822012, 81771043, 81802193, 81970898
6 · The paper itself

Abstract

Deep learning (DL) is currently revolutionizing peptide drug development due to both computational advances and the substantial recent expansion of digitized biological data. However, progress in oligopeptide drug development has been limited, likely due to the lack of suitable datasets and difficulty in identifying informative features to use as inputs for DL models. Here, we utilized an unsupervised deep learning model to learn a semantic pattern based on the intrinsically disordered regions of ~171 known osteogenic proteins. Subsequently, oligopeptides were generated from this semantic pattern based on Monte Carlo simulation, followed by in vivo functional characterization. A five amino acid oligopeptide (AIB5P) had strong bone-formation-promoting effects, as determined in multiple mouse models (e.g., osteoporosis, fracture, and osseointegration of implants). Mechanistically, we showed that AIB5P promotes osteogenesis by binding to the integrin α5 subunit and thereby activating FAK signaling. In summary, we successfully established an oligopeptide discovery strategy based on a DL model and demonstrated its utility from cytological screening to animal experimental verification.

Identifiers

PMID35228528
PMCPMC8885677
OpenAlexW4214672816

What OpenQuestion holds

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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.