Evidence map›Paper›PMID 41659557›Full record

ArticlebioRxiv : the preprint server for biology2026

Designing Rigid Protein Fiducials to Visualize GPCR Conformational States.

Alina Vo, Yuan-En Sun, Justin G English, Michael J Robertson

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 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

4 authors.

Alina VoVerna and Marrs McLean Department of Biochemistry and Molecular Pharmacology, Baylor College of Medicine, Houston, TX, USA.
Yuan-En SunDepartment of Biochemistry, University of Utah School of Medicine, Salt Lake City, UT, USA.
Justin G EnglishDepartment of Biochemistry, University of Utah School of Medicine, Salt Lake City, UT, USA.
Michael J RobertsonVerna and Marrs McLean Department of Biochemistry and Molecular Pharmacology, Baylor College of Medicine, Houston, TX, USA.

Funding

ACQUISITION OF HIGH-THROUGHPUT 200 kV CRYO-TEMS10OD032204 · OD · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI SERYSHEVA, IRINA I · 2022 to 2022
$2.0M
Improved Targeting of Somatostatin Receptors for Pediatric ConditionsR00HD107581 · NICHD · BAYLOR COLLEGE OF MEDICINE · PI ROBERTSON, MICHAEL · 2024 to 2025
$411k
NICHD NIH HHS R00 HD107581NIH HHS S10 OD032204
6 · The paper itself

Abstract

G-protein coupled receptors (GPCRs) mediate precise ligand-specific signaling profiles, yet structural visualization of how ligands alter receptor conformational landscapes in the absence of signaling partners or mimetics has proven incredibly challenging. Here we show that by combining generative protein design with deep-learning based conformational ensemble prediction we can reliably design 'fiducial markers' to facilitate cryogenic electron microscopy (cryoEM) of GPCRs at arbitrary fusion points, enabling the visualization of previously intractable states. We validate the approach with high-throughput determination of inactive state structures of four pharmaceutically relevant GPCRs, allowing for key details of receptor pharmacology to be resolved in each case. We then engineered an extracellular fiducial marker for the prototypical β2-adrenergic receptor that enabled direct structural characterization of the rearrangement of key intracellular motifs in the absence of G-protein. Comparison with recent co-folding models highlights gaps in current methods for predicting ligand-induced GPCR conformational changes. These results present a generalizable framework for accessing traditionally inaccessible structural states of small, dynamic proteins.

Identifiers

PMID41659557
PMCPMC12873965

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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.