Evidence map›Paper›PMID 41738444›Full record

ArticleBiochemistry2026

Allostery between Distant Structural Regions Dictates Selectivity in GPCR:G Protein Coupling.

Elizaveta Mukhaleva, Edgardo J Sánchez Rivas, Sergio Branciamore, Andrei S Rodin, Sivaraj Sivaramakrishnan, Nagarajan Vaidehi

Abstract read
In one paragraph

Article in Biochemistry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Elizaveta MukhalevaDepartment of Computational and Quantitative Medicine, Beckman Research Institute of the City of Hope, Monrovia, California 91016, United States.ORCID 0000-0002-9911-7625
Edgardo J Sánchez RivasDepartment of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, Minnesota 55455, United States.ORCID 0000-0002-6456-3676
Sergio BranciamoreDepartment of Computational and Quantitative Medicine, Beckman Research Institute of the City of Hope, Monrovia, California 91016, United States.
Andrei S RodinDepartment of Computational and Quantitative Medicine, Beckman Research Institute of the City of Hope, Monrovia, California 91016, United States.
Sivaraj SivaramakrishnanDepartment of Genetics, Cell Biology and Development, University of Minnesota, Minneapolis, Minnesota 55455, United States.ORCID 0000-0002-9541-6994
Nagarajan VaidehiDepartment of Computational and Quantitative Medicine, Beckman Research Institute of the City of Hope, Monrovia, California 91016, United States.ORCID 0000-0001-8100-8132

Funding

TRAINING PROGRAM IN MUSCLE RESEARCHT32AR007612 · NIAMS · UNIVERSITY OF MINNESOTA TWIN CITIES · PI JAMES M ERVASTI, DAWN A LOWE · 2001 to 2026
$11.3M
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
NIAMS NIH HHS T32 AR007612NIGMS 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

Despite extensive structural and functional studies, the molecular mechanisms governing G-protein coupled receptor-G (GPCR-G) protein coupling selectivity remain unresolved. Here, using an interpretable machine learning Bayesian Network model with Molecular Dynamics simulations and experiments, we reveal the influence of distant residue communities within the Gα protein core on coupling selectivity. We observed distinct cooperative hotspot residues across different Gα protein subtypes, including key regions such as the N-terminus, h4s6 loop, and H5 helix. These results demonstrate the intricate allosteric dependencies between the core and the H5 helix in stabilizing selective interactions. The functional significance of these cooperative regions is validated through subtype-swapping mutations. By introducing targeted Gαq-like mutations in the Gαs core, we successfully altered the receptor coupling profile to signal through Gαq. Our findings emphasize that cooperative interactions in the Gα core are not only crucial for selectivity but can also be leveraged to engineer Gα proteins with tailored coupling preferences.

Indexed as

Receptors, G-Protein-CoupledAllosteric RegulationBayes TheoremGTP-Binding Protein alpha Subunits, Gq-G11HumansMolecular Dynamics SimulationMutationGTP-Binding Protein alpha Subunits, Gq-G11Receptors, G-Protein-Coupled

Identifiers

PMID41738444
PMCPMC13001101

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.