Evidence map›Paper›PMID 41353204›Full record

ArticleNature communications2025

Temporally resolved and interpretable machine learning model of GPCR conformational transition.

Babgen Manookian, Elizaveta Mukhaleva, Grigoriy Gogoshin, Supriyo Bhattacharya, Sivaraj Sivaramakrishnan, Nagarajan Vaidehi, Andrei S Rodin, Sergio Branciamore

Abstract read
In one paragraph

Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Babgen Manookian *Department of Computational and Quantitative Medicine, Beckman Research Institute of the City of Hope, Duarte, CA, USA.ORCID http://orcid.org/0000-0002-6273-0995
Elizaveta Mukhaleva *Department of Computational and Quantitative Medicine, Beckman Research Institute of the City of Hope, Duarte, CA, USA.
Grigoriy GogoshinDepartment of Computational and Quantitative Medicine, Beckman Research Institute of the City of Hope, Duarte, CA, USA.ORCID http://orcid.org/0000-0003-0675-1799
Supriyo BhattacharyaDepartment of Computational and Quantitative Medicine, Beckman Research Institute of the City of Hope, Duarte, CA, USA.ORCID http://orcid.org/0000-0003-0483-2149
Sivaraj SivaramakrishnanDepartment of Genetics, Cell and Developmental Biology, University of Minnesota, Minneapolis, MN, USA.ORCID http://orcid.org/0000-0002-9541-6994
Nagarajan VaidehiDepartment of Computational and Quantitative Medicine, Beckman Research Institute of the City of Hope, Duarte, CA, USA. nvaidehi@coh.org.ORCID http://orcid.org/0000-0001-8100-8132
Andrei S RodinDepartment of Computational and Quantitative Medicine, Beckman Research Institute of the City of Hope, Duarte, CA, USA. arodin@coh.org.ORCID http://orcid.org/0000-0002-2570-0332
Sergio BranciamoreDepartment of Computational and Quantitative Medicine, Beckman Research Institute of the City of Hope, Duarte, CA, USA. sbranciamore@coh.org.ORCID http://orcid.org/0000-0002-2556-8765

Funding

Emergent cellular functions of GPCRs and myosinsR35GM126940 · NIGMS · UNIVERSITY OF MINNESOTA · PI Sivaraj Sivaramakrishnan · 2018 to 2026
$3.8M
Structural dynamics underlying GPCR-G protein selectivityR01GM117923 · NIGMS · BECKMAN RESEARCH INSTITUTE/CITY OF HOPE · PI VAIDEHI, NAGARAJAN · 2017 to 2024
$3.8M
An integrated toolkit combining computational systems biology techniques with molecular dynamics simulations to delineate functionality of GPCRsR01LM013876 · NLM · BECKMAN RESEARCH INSTITUTE/CITY OF HOPE · PI RODIN, ANDREI, VAIDEHI, NAGARAJAN · 2022 to 2025
$1.5M
Emergent role of allostery on function of GPCRs and Trimeric G proteinsR35GM156498 · NIGMS · BECKMAN RESEARCH INSTITUTE/CITY OF HOPE · PI Nagarajan Vaidehi · 2025 to 2026
$890k
Scalable Bayesian Network analysis of multimodal FACS and SUMOylation data, with generalization to other big mixed biological datasetsR01LM013138 · NLM · BECKMAN RESEARCH INSTITUTE/CITY OF HOPE · PI RODIN, ANDREI · 2020 to 2022
$776k
Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.) R01-GM117923Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.) R01-LM013138NIGMS NIH HHS R01 GM117923NIGMS NIH HHS R35 GM126940NIGMS NIH HHS R35 GM156498NLM NIH HHS R01 LM013138NLM NIH HHS R01 LM013876
6 · The paper itself

Abstract

Identifying target-specific drugs remains a challenge in pharmacology, especially for highly homologous proteins such as dopamine receptors D

Indexed as

Machine LearningReceptors, Dopamine D3AlgorithmsBayes TheoremHumansMolecular Dynamics SimulationProtein ConformationReceptors, Adrenergic, beta-2Receptors, Dopamine D2Receptors, Adrenergic, beta-2Receptors, Dopamine D2Receptors, Dopamine D3

Identifiers

PMID41353204
PMCPMC12783649

What OpenQuestion holds

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LicenceCC BY
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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.