Evidence map›Paper›PMID 30104635›Full record

ArticleNature methods2018

A proximity-tagging system to identify membrane protein-protein interactions.

Qiang Liu, Jun Zheng, Weiping Sun, Yinbo Huo, Liye Zhang, Piliang Hao, Haopeng Wang, Min Zhuang

Abstract read
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In one paragraph

Article in Nature methods, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 137 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
137citing papers in PubMed, 1 pooled it
–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

137 citing papers in PubMed, 1 synthesis or guideline pooled it.

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77 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Qiang LiuSchool of Life Science and Technology, ShanghaiTech University, Shanghai, China.
Jun ZhengSchool of Life Science and Technology, ShanghaiTech University, Shanghai, China.
Weiping SunSchool of Life Science and Technology, ShanghaiTech University, Shanghai, China.
Yinbo HuoSchool of Life Science and Technology, ShanghaiTech University, Shanghai, China.
Liye ZhangSchool of Life Science and Technology, ShanghaiTech University, Shanghai, China.ORCID http://orcid.org/0000-0003-2104-6828
Piliang HaoSchool of Life Science and Technology, ShanghaiTech University, Shanghai, China.
Haopeng WangSchool of Life Science and Technology, ShanghaiTech University, Shanghai, China. wanghp@shanghaitech.edu.cn.ORCID http://orcid.org/0000-0002-4995-0838
Min ZhuangSchool of Life Science and Technology, ShanghaiTech University, Shanghai, China. zhuangmin@shanghaitech.edu.cn.ORCID http://orcid.org/0000-0001-6215-5807

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The communication between cells and between cellular organelles is often controlled by the interaction of membrane proteins. Although many methods for the detection of protein-protein interactions (PPIs) exist, membrane PPIs remain difficult to detect. Here we developed a proximity-based tagging system, PUP-IT (pupylation-based interaction tagging), to identify membrane protein interactions. In this approach, a small protein tag, Pup, is applied to proteins that interact with a PafA-fused bait, enabling transient and weak interactions to be enriched and detected by mass spectrometry. Pup does not diffuse from the enzyme, which allows high-specificity labeling. We applied this approach to CD28, a critical costimulatory receptor for T lymphocyte activation, and identified known CD28 binding partners and multiple potential interacting proteins. In addition, we demonstrated that this method can identify the interaction between a cell surface receptor and its ligand.

Indexed as

Protein Interaction MapsCD28 AntigensHumansMass SpectrometryMembrane ProteinsProtein BindingProteolysisCD28 AntigensMembrane Proteins

Identifiers

What OpenQuestion holds

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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.