{"entity": "researcher", "timestamp": "2026-09-23T23:53:37.185Z", "family": "Cabeza de Vaca", "given": "Israel", "initials": "I", "orcid": "0000-0002-6208-1091", "affiliations": ["Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Box 596, SE-751 24 Uppsala, Sweden."], "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/researcher/defa085f944942239696861994f25e79.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/researcher/defa085f944942239696861994f25e79"}}, "publications": [{"entity": "publication", "iuid": "76e5f848ccc041ac9b4fa821b20ba8b0", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/76e5f848ccc041ac9b4fa821b20ba8b0.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/76e5f848ccc041ac9b4fa821b20ba8b0"}}, "title": "Ultra-large virtual screening unveils potent agonists of the neuromodulatory orphan receptor GPR139.", "authors": [{"family": "Cabeza de Vaca", "given": "Israel", "initials": "I", "orcid": "0000-0002-6208-1091", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/defa085f944942239696861994f25e79.json"}}, {"family": "Trapkov", "given": "Boris", "initials": "B", "orcid": "0000-0003-1245-888X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/1aff7885ecf14656b053ddfd6d94b17a.json"}}, {"family": "Shen", "given": "Ling", "initials": "L"}, {"family": "Vo", "given": "Duy Duc", "initials": "DD"}, {"family": "Zhang", "given": "Xiaoqun", "initials": "X", "orcid": "0000-0002-9461-8682", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/4395a11b24bf40308b6d3a8467763517.json"}}, {"family": "Yang", "given": "Yunting", "initials": "Y"}, {"family": "Pezeshki", "given": "Mitra", "initials": "M", "orcid": "0009-0001-0155-7463", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/5d5cade660844b84980e852280aabedb.json"}}, {"family": "Zhang", "given": "Xuehan", "initials": "X"}, {"family": "B\u00e4llgren", "given": "Frida", "initials": "F"}, {"family": "Saleh", "given": "Aljona", "initials": "A"}, {"family": "Tarnovskiy", "given": "Andrii V", "initials": "AV"}, {"family": "Radchenko", "given": "Dmytro S", "initials": "DS", "orcid": "0000-0001-5444-7754", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/5a8f8993ce024259aa09cfae9a745a20.json"}}, {"family": "Moroz", "given": "Yurii S", "initials": "YS", "orcid": "0000-0001-6073-002X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/85e5c6a40d854426b96476233f59ccfa.json"}}, {"family": "Br\u00e4uner-Osborne", "given": "Hans", "initials": "H"}, {"family": "Svenningsson", "given": "Per", "initials": "P", "orcid": "0000-0001-6727-3802", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/b01bbacfa24e4b9794734bf1121d1c38.json"}}, {"family": "Kihlberg", "given": "Jan", "initials": "J", "orcid": "0000-0002-4205-6040", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/7edb4dfa7c4b4d6c8c4cf0282a9c9a1a.json"}}, {"family": "Liu", "given": "Zhi-Jie", "initials": "ZJ", "orcid": "0000-0001-7279-2893", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/3ab058d718354e5b96adef9dca906de2.json"}}, {"family": "Hauser", "given": "Alexander Sebastian", "initials": "AS", "orcid": "0000-0003-1098-6419", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/b8ac3dae8d9543cfabc166986ce35847.json"}}, {"family": "Carlsson", "given": "Jens", "initials": "J", "orcid": "0000-0003-4623-2977", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/792c1cd4aca248dc94b967115ffa82df.json"}}], "type": "journal article", "published": "2025-12-09", "journal": {"title": "Nat Commun", "issn": "2041-1723", "volume": "17", "issue": "1", "pages": "129", "issn-l": "2041-1723"}, "abstract": "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 \u00b5M. 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.", "doi": "10.1038/s41467-025-66845-y", "pmid": "41365886", "labels": {"SciLifeLab Fellow": "", "Jens Carlsson": ""}, "xrefs": [{"db": "pmc", "key": "PMC12775434"}, {"db": "pii", "key": "10.1038/s41467-025-66845-y"}], "notes": [], "created": "2026-09-23T09:12:16.102Z", "modified": "2026-09-23T09:12:16.377Z"}, {"entity": "publication", "iuid": "bdf3cb94b9174341ab174b54af83f9e4", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/bdf3cb94b9174341ab174b54af83f9e4.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/bdf3cb94b9174341ab174b54af83f9e4"}}, "title": "AlphaFold accelerated discovery of psychotropic agonists targeting the trace amine-associated receptor 1.", "authors": [{"family": "D\u00edaz-Holgu\u00edn", "given": "Alejandro", "initials": "A", "orcid": "0000-0002-3449-5086", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/049a8f250afa402fb1813f9a0288dcb0.json"}}, {"family": "Saarinen", "given": "Marcus", "initials": "M"}, {"family": "Vo", "given": "Duc Duy", "initials": "DD"}, {"family": "Sturchio", "given": "Andrea", "initials": "A"}, {"family": "Branzell", "given": "Niclas", "initials": "N"}, {"family": "Cabeza de Vaca", "given": "Israel", "initials": "I", "orcid": "0000-0002-6208-1091", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/defa085f944942239696861994f25e79.json"}}, {"family": "Hu", "given": "Huabin", "initials": "H"}, {"family": "Mitjavila-Dom\u00e8nech", "given": "N\u00faria", "initials": "N"}, {"family": "Lindqvist", "given": "Annika", "initials": "A", "orcid": "0009-0003-2587-8434", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/89bd045cc7844e508c837da8a2887a22.json"}}, {"family": "Baranczewski", "given": "Pawel", "initials": "P", "orcid": "0000-0001-5772-6791", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/327feae3833d425c9d5936f48627390c.json"}}, {"family": "Millan", "given": "Mark J", "initials": "MJ"}, {"family": "Yang", "given": "Yunting", "initials": "Y", "orcid": "0000-0001-5700-1547", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/b69ec9e8f589441482276a35ef678124.json"}}, {"family": "Carlsson", "given": "Jens", "initials": "J", "orcid": "0000-0003-4623-2977", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/792c1cd4aca248dc94b967115ffa82df.json"}}, {"family": "Svenningsson", "given": "Per", "initials": "P", "orcid": "0000-0001-6727-3802", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/b01bbacfa24e4b9794734bf1121d1c38.json"}}], "type": "journal article", "published": "2024-08-09", "journal": {"title": "Sci Adv", "issn": "2375-2548", "volume": "10", "issue": "32", "pages": "eadn1524", "issn-l": "2375-2548"}, "abstract": "Artificial intelligence is revolutionizing protein structure prediction, providing unprecedented opportunities for drug design. To assess the potential impact on ligand discovery, we compared virtual screens using protein structures generated by the AlphaFold machine learning method and traditional homology modeling. More than 16 million compounds were docked to models of the trace amine-associated receptor 1 (TAAR1), a G protein-coupled receptor of unknown structure and target for treating neuropsychiatric disorders. Sets of 30 and 32 highly ranked compounds from the AlphaFold and homology model screens, respectively, were experimentally evaluated. Of these, 25 were TAAR1 agonists with potencies ranging from 12 to 0.03 \u03bcM. The AlphaFold screen yielded a more than twofold higher hit rate (60%) than the homology model and discovered the most potent agonists. A TAAR1 agonist with a promising selectivity profile and drug-like properties showed physiological and antipsychotic-like effects in wild-type but not in TAAR1 knockout mice. These results demonstrate that AlphaFold structures can accelerate drug discovery.", "doi": "10.1126/sciadv.adn1524", "pmid": "39110804", "labels": {"SciLifeLab Fellow": "", "Jens Carlsson": ""}, "xrefs": [{"db": "pmc", "key": "PMC11305387"}], "notes": [], "created": "2026-09-23T08:42:45.034Z", "modified": "2026-09-23T08:42:45.344Z"}]}