Evidence map›Paper›PMID 41165346›Full record

ReviewChemical senses2025

Olfactory GPCRs through the lens of structural bioinformatics.

Alessandro Nicoli, Florian Bößl, Antonella Di Concilio Moschen, Francesco Ferri, Clarissa Rienaecker, Antonella Di Pizio

Abstract readReview
In one paragraph

Review in Chemical senses, 2025. 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.

Alessandro NicoliSection In Silico Biology & Machine Learning, Leibniz Institute for Food Systems Biology at the Technical University of Munich, Freising 85354, Germany.ORCID 0000-0001-6177-9749
Florian BößlSection In Silico Biology & Machine Learning, Leibniz Institute for Food Systems Biology at the Technical University of Munich, Freising 85354, Germany.ORCID 0009-0008-8724-8901
Antonella Di Concilio MoschenSection In Silico Biology & Machine Learning, Leibniz Institute for Food Systems Biology at the Technical University of Munich, Freising 85354, Germany.ORCID 0009-0003-7374-4641
Francesco FerriSection In Silico Biology & Machine Learning, Leibniz Institute for Food Systems Biology at the Technical University of Munich, Freising 85354, Germany.ORCID 0009-0005-1824-0234
Clarissa RienaeckerSection In Silico Biology & Machine Learning, Leibniz Institute for Food Systems Biology at the Technical University of Munich, Freising 85354, Germany.ORCID 0000-0002-2075-3486
Antonella Di PizioSection In Silico Biology & Machine Learning, Leibniz Institute for Food Systems Biology at the Technical University of Munich, Freising 85354, Germany.ORCID 0000-0002-8520-5165

Funding

Leibniz Program for Women Professors P116/2020
6 · The paper itself

Abstract

Olfactory perception, mediated by G protein-coupled receptors (GPCRs) such as odorant receptors (ORs) and trace amine-associated receptors (TAARs), plays a pivotal role in human health, influencing behaviors like food choices and serving as early biomarkers for neurodegenerative diseases. Despite their importance, olfactory GPCRs are among the least understood members of the GPCR superfamily, and most ORs and TAARs are still orphan receptors. This review provides a comprehensive overview of recent advancements in the structural bioinformatics of olfactory GPCRs. We outline how computational, structure-based strategies have succeeded in identifying novel modulators for olfactory receptors. By discussing recent breakthroughs in GPCR structural biology, such as the first resolved experimental structures of ORs and TAARs, and the transformative impact of AI-driven structure prediction tools for olfactory receptors, this review offers a roadmap for future olfaction pharmacology research.

Indexed as

Computational BiologyReceptors, G-Protein-CoupledReceptors, OdorantAnimalsHumansSmellReceptors, G-Protein-CoupledReceptors, OdorantAlphaFolddeorphanizationmolecular dockingodorant receptorsodorant recognitiontrace amine-associated receptors

Identifiers

PMID41165346
PMCPMC12573245

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.