Evidence map›Paper›PMID 28900175›Full record

ArticleScientific reports2017

Dynamic and Kinetic Elements of µ-Opioid Receptor Functional Selectivity.

Abhijeet Kapoor, Gerard Martinez-Rosell, Davide Provasi, Gianni de Fabritiis, Marta Filizola

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed
2.9field-weighted citation impact, top 9% 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

30 citing papers in PubMed, 56 citations in OpenAlex.

  1. Article
  2. Article
  3. Total synthesis and biological activity of "carbamorphine": O-to-CHProceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. Dynamics of the Apo µ-Opioid Receptor in Complex with Gi Protein.International journal of molecular sciences · 2023
    Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Review
  15. Review
  16. Encoding mu-opioid receptor biased agonism with interaction fingerprints.Journal of computer-aided molecular design · 2021
    Article
  17. Article
  18. Review
  19. Markov State Models to Elucidate Ligand Binding Mechanism.Methods in molecular biology (Clifton, N.J.) · 2021
    Review
  20. Exploring the activation pathway and GProceedings of the National Academy of Sciences of the United States of America · 2020
    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

5 authors at 3 institutions in 2 countries.

Abhijeet KapoorDepartment of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Gerard Martinez-RosellComputational Biophysics Laboratory (GRIB-IMIM), Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), C Dr Aiguader 88, Barcelona, 08003, Spain.
Davide ProvasiDepartment of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Gianni de FabritiisComputational Biophysics Laboratory (GRIB-IMIM), Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), C Dr Aiguader 88, Barcelona, 08003, Spain. gianni.defabritiis@upf.edu.
Marta FilizolaDepartment of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA. marta.filizola@mssm.edu.ORCID 0000-0002-4382-8276
Icahn School of Medicine at Mount Sinai · USInstitució Catalana de Recerca i Estudis Avançats · ESUniversitat Pompeu Fabra · ES

Funding

Transforming Genomics with 5 PB Big Omics Data Engine Cray CS300-AC SupercomputerS10OD018522 · OD · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI KOVATCH, PATRICIA · 2014 to 2014
$1.9M
Dynamic Mechanisms of GPCRs Targeted by Drugs of AbuseR01DA034049 · NIDA · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI FILIZOLA, MARTA · 2012 to 2016
$1.8M
Structural Aspects of Oligomerization in the Function of GPCRsK02DA026434 · NIDA · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI FILIZOLA, MARTA · 2009 to 2018
$1.2M
Enhanced Molecular Dynamics Methods to Investigate GPCR Ligand BindingR21MH107053 · NIMH · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI FILIZOLA, MARTA · 2016 to 2017
$466k
NIDA NIH HHS K02 DA026434NIDA NIH HHS R01 DA034049NIH HHS S10 OD018522NIMH NIH HHS R21 MH107053
6 · The paper itself

Abstract

While the therapeutic effect of opioids analgesics is mainly attributed to µ-opioid receptor (MOR) activation leading to G protein signaling, their side effects have mostly been linked to β-arrestin signaling. To shed light on the dynamic and kinetic elements underlying MOR functional selectivity, we carried out close to half millisecond high-throughput molecular dynamics simulations of MOR bound to a classical opioid drug (morphine) or a potent G protein-biased agonist (TRV-130). Statistical analyses of Markov state models built using this large simulation dataset combined with information theory enabled, for the first time: a) Identification of four distinct metastable regions along the activation pathway, b) Kinetic evidence of a different dynamic behavior of the receptor bound to a classical or G protein-biased opioid agonist, c) Identification of kinetically distinct conformational states to be used for the rational design of functionally selective ligands that may eventually be developed into improved drugs; d) Characterization of multiple activation/deactivation pathways of MOR, and e) Suggestion from calculated transition timescales that MOR conformational changes are not the rate-limiting step in receptor activation.

Indexed as

AnalgesicsAnalgesics, OpioidKineticsMolecular Dynamics SimulationReceptors, Opioid, muSubstrate SpecificityAnalgesicsAnalgesics, OpioidReceptors, Opioid, mu

Identifiers

PMID28900175
PMCPMC5595830
OpenAlexW2753735048

What OpenQuestion holds

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Registered trials

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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.