ArticleNature communications2025
Generic residue numbering of the GAIN domain of adhesion GPCRs.
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.
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Who cites it
5 citing papers in PubMed.
- Adhesion G protein-coupled receptors.Pharmacological reviews · 2026Review
- Structural basis of GAIN domain autoproteolysis and cleavage-resistance in the adhesion G-protein coupled receptors.Nature communications · 2026Article
- Structure of the GNature communications · 2025Article
- Mechanosensing of Shear Stress and Uterine Spiral Artery Remodeling by Invasive Trophoblasts in Early Pregnancy.International journal of molecular sciences · 2025Review
- Generic residue numbering of the GAIN domain of adhesion GPCRs.Nature communications · 2025Article
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7 authors.
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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.
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