Evidence map›Paper›PMID 34770748›Full record

ReviewMolecules (Basel, Switzerland)2021

Quantitative FRET (qFRET) Technology for the Determination of Protein-Protein Interaction Affinity in Solution.

Jiayu Liao, Vipul Madahar, Runrui Dang, Ling Jiang

Abstract readReview
In one paragraph

Review in Molecules (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Macromolecular High-Affinity Binding Probed by Advanced Fluorescence Techniques.Chembiochem : a European journal of chemical biology · 2025
    Review
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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

4 authors.

Jiayu LiaoDepartment of Bioengineering, Bourns College of Engineering, University of California at Riverside, 900 University Avenue, Riverside, CA 92521, USA.ORCID 0000-0002-2624-5781
Vipul MadaharDepartment of Bioengineering, Bourns College of Engineering, University of California at Riverside, 900 University Avenue, Riverside, CA 92521, USA.
Runrui DangDepartment of Bioengineering, Bourns College of Engineering, University of California at Riverside, 900 University Avenue, Riverside, CA 92521, USA.
Ling JiangDepartment of Biochemistry and Molecular Biology, Heilongjiang University of Chinese Medicine, 24 Heping Road, Harbin 150040, China.

Funding

Academic Senate, University of California, Riverside 2020Attaisina Inc 2019 Gift Fund
6 · The paper itself

Abstract

Protein-protein interactions play pivotal roles in life, and the protein interaction affinity confers specific protein interaction events in physiology or pathology. Förster resonance energy transfer (FRET) has been widely used in biological and biomedical research to detect molecular interactions in vitro and in vivo. The FRET assay provides very high sensitivity and efficiency. Several attempts have been made to develop the FRET assay into a quantitative measurement for protein-protein interaction affinity in the past. However, the progress has been slow due to complicated procedures or because of challenges in differentiating the FRET signal from other direct emission signals from donor and receptor. This review focuses on recent developments of the quantitative FRET analysis and its application in the determination of protein-protein interaction affinity (

Indexed as

AlgorithmsFluorescence Resonance Energy TransferKineticsModels, TheoreticalProtein BindingProtein Interaction MappingProteinsSolutionsProteinsSolutionsFRET excitationFRET quenchingKD determinationprotein interaction affinityquantitative FRET assay

Identifiers

PMID34770748
PMCPMC8588070

What OpenQuestion holds

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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.