EconPapers    
Economics at your fingertips  
 

Generic residue numbering of the GAIN domain of adhesion GPCRs

Florian Seufert, Guillermo Pérez-Hernández, Gáspár Pándy-Szekeres, Ramon Guixà-González, Tobias Langenhan, David E. Gloriam () and Peter W. Hildebrand ()
Additional contact information
Florian Seufert: Medical Faculty
Guillermo Pérez-Hernández: corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin
Gáspár Pándy-Szekeres: Universitetsparken 2
Ramon Guixà-González: corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin
Tobias Langenhan: Leipzig University
David E. Gloriam: Universitetsparken 2
Peter W. Hildebrand: Medical Faculty

Nature Communications, 2025, vol. 16, issue 1, 1-11

Abstract: Abstract The GPCR autoproteolysis inducing (GAIN) domain is an ancient protein fold ubiquitous in adhesion G protein-coupled receptors (aGPCR). It contains a tethered agonist necessary and sufficient for receptor activation. The GAIN domain is a hotspot for pathological mutations. However, the low primary sequence conservation of GAIN domains has thus far hindered the knowledge transfer across different GAIN domains in human receptors as well as species orthologs. Here, we present a scheme for generic residue numbering of GAIN domains, based on structural alignments of over 14,000 modeled GAIN domain structures. This scheme is implemented in the GPCR database (GPCRdb) and elucidates the domain topology across different aGPCRs and their homologs in a large panel of species. We identify conservation hotspots and statistically cancer-enriched positions in human aGPCRs and show the transferability of positional and structural information between GAIN domain homologs. The GAIN-GRN scheme provides a robust strategy to allocate structural homologies at the primary and secondary levels also to GAIN domains of polycystic kidney disease 1/PKD1-like proteins, which now renders positions in both GAIN domain types comparable to one another. In summary, our work enables researchers to generate hypothesis and rationalize experiments related to GAIN domain function and pathology.

Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-024-55466-6 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-024-55466-6

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-024-55466-6

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

 
Page updated 2025-03-19
Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-024-55466-6