Evidence map›Paper›PMID 33987207›Full record

ArticleFrontiers in molecular biosciences2021

Pathways and Mechanism of Caffeine Binding to Human Adenosine A

Hung N Do, Sana Akhter, Yinglong Miao

Open access · goldAbstract read
In one paragraph

Article in Frontiers in molecular biosciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 1 pooled it
1.2field-weighted citation impact, top 22% 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

17 citing papers in PubMed, 1 synthesis or guideline pooled it, 25 citations in OpenAlex.

  1. Pooled it
  2. Trial
  3. Trial
  4. Article
  5. Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. Review
  11. TRPA1, TRPV1, and Caffeine: Pain and Analgesia.International journal of molecular sciences · 2024
    Review
  12. Review
  13. Article
  14. Letter: Caffeine and fractional flow reserve overestimation: a word of caution.EuroIntervention : journal of EuroPCR in collaboration with the Working Group on Interventional Cardiology of the European Society of Cardiology · 2022
    Article
  15. Gaussian Accelerated Molecular Dynamics in OpenMM.The journal of physical chemistry. B · 2022
    Article
  16. Review
  17. 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

3 authors at 1 institution in 1 country.

Hung N DoCenter for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, KS, United States.
Sana AkhterCenter for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, KS, United States.
Yinglong MiaoCenter for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, KS, United States.
University of Kansas · US

Funding

Enhanced Sampling of G-Protein-Coupled Receptor-G Protein InteractionsR01GM132572 · NIGMS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI MIAO, YINGLONG · 2019 to 2023
$1.5M
NIGMS NIH HHS R01 GM132572
6 · The paper itself

Abstract

Caffeine (CFF) is a common antagonist to the four subtypes of adenosine G-protein-coupled receptors (GPCRs), which are critical drug targets for treating heart failure, cancer, and neurological diseases. However, the pathways and mechanism of CFF binding to the target receptors remain unclear. In this study, we have performed all-atom-enhanced sampling simulations using a robust Gaussian-accelerated molecular dynamics (GaMD) method to elucidate the binding mechanism of CFF to human adenosine A

Indexed as

adenosine A2A receptorcaffeineGaussian accelerated molecular dynamicsligand bindingmechanismpathways

Identifiers

PMID33987207
PMCPMC8111288
OpenAlexW3157703588

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.