9m42: Difference between revisions

[https://www.uniprot.org/uniprot/GNAS2_HUMAN GNAS2_HUMAN] Pseudopseudohypoparathyroidism;Pseudohypoparathyroidism type 1A;Progressive osseous heteroplasia;Polyostotic fibrous dysplasia;Monostotic fibrous dysplasia;Pseudohypoparathyroidism type 1C;Pseudohypoparathyroidism type 1B;McCune-Albright syndrome. The disease is caused by mutations affecting the gene represented in this entry.  The disease is caused by mutations affecting the gene represented in this entry.  The disease is caused by mutations affecting the gene represented in this entry.  The disease is caused by mutations affecting the gene represented in this entry.  The disease is caused by mutations affecting the gene represented in this entry.  The disease is caused by mutations affecting the gene represented in this entry.  The disease is caused by mutations affecting the gene represented in this entry. Most affected individuals have defects in methylation of the gene. In some cases microdeletions involving the STX16 appear to cause loss of methylation at exon A/B of GNAS, resulting in PHP1B. Paternal uniparental isodisomy have also been observed.  The disease is caused by mutations affecting the gene represented in this entry.  The disease is caused by mutations affecting the gene represented in this entry.

The orphan G protein-coupled receptor (GPCR) GPR139 attracts interest as a target for neuropsychiatric disorders. Whereas the physiological functions of GPR139 remain elusive, a high-resolution receptor structure is now available. To assess whether structural information enables ligand discovery, we computationally dock 235 million compounds to the GPR139 binding site. Of 68 top-ranked compounds evaluated experimentally, five are full agonists with potencies ranging from 160 nM to 3.6 microM. Structure-guided optimization identifies one of the most potent GPR139 agonists, and a cryo-EM structure of the receptor-ligand complex confirms the predicted binding mode. Functional characterization provides insights into GPR139 signalling, and one agonist elicits behavioural effects in mice. We also explore the potential to replace experimental structure determination with the deep-learning method AlphaFold3, revealing a limited capability of artificial intelligence to model receptor-ligand interactions for understudied GPCRs. The results demonstrate how high-resolution GPCR structures combined with large-library docking can accelerate drug discovery.

Ultra-large virtual screening unveils potent agonists of the neuromodulatory orphan receptor GPR139.,Cabeza de Vaca I, Trapkov B, Shen L, Vo DD, Zhang X, Yang Y, Pezeshki M, Zhang X, Ballgren F, Saleh A, Tarnovskiy AV, Radchenko DS, Moroz YS, Brauner-Osborne H, Svenningsson P, Kihlberg J, Liu ZJ, Hauser AS, Carlsson J Nat Commun. 2025 Dec 9;17(1):129. doi: 10.1038/s41467-025-66845-y. PMID:41365886<ref>PMID:41365886</ref>

From MEDLINE&reg;/PubMed&reg;, a database of the U.S. National Library of Medicine.<br>

[[Category: Large Structures]]

[[Category: Niallia circulans]]

[[Category: Cabezadevaca I]]

[[Category: Carlsson J]]

[[Category: Hauser AS]]

[[Category: Liu Zj]]

[[Category: Pezeshki M]]

[[Category: Shen L]]

[[Category: Trapkov B]]

[[Category: Zhang XH]]