{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Herrington NB"],"funding":["NCATS NIH HHS","NCI NIH HHS","National Institutes of Health"],"pagination":["e1012302"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11268620"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["20(7)"],"pubmed_abstract":["Protein kinase function and interactions with drugs are controlled in part by the movement of the DFG and ɑC-Helix motifs that are related to the catalytic activity of the kinase. Small molecule ligands elicit therapeutic effects with distinct selectivity profiles and residence times that often depend on the active or inactive kinase conformation(s) they bind. Modern AI-based structural modeling methods have the potential to expand upon the limited availability of experimentally determined kinase structures in inactive states. Here, we first explored the conformational space of kinases in the PDB and models generated by AlphaFold2 (AF2) and ESMFold, two prominent AI-based protein structure prediction methods. Our investigation of AF2's ability to explore the conformational diversity of the"],"journal":["PLoS computational biology"],"pubmed_title":["A comprehensive exploration of the druggable conformational space of protein kinases using AI-predicted structures."],"pmcid":["PMC11268620"],"funding_grant_id":["U01CA271318","U01 CA271318","UL1 TR004419"],"pubmed_authors":["Li YC","Pandey G","Stein D","Herrington NB","Schlessinger A"],"additional_accession":[]},"is_claimable":false,"name":"A comprehensive exploration of the druggable conformational space of protein kinases using AI-predicted structures.","description":"Protein kinase function and interactions with drugs are controlled in part by the movement of the DFG and ɑC-Helix motifs that are related to the catalytic activity of the kinase. Small molecule ligands elicit therapeutic effects with distinct selectivity profiles and residence times that often depend on the active or inactive kinase conformation(s) they bind. Modern AI-based structural modeling methods have the potential to expand upon the limited availability of experimentally determined kinase structures in inactive states. Here, we first explored the conformational space of kinases in the PDB and models generated by AlphaFold2 (AF2) and ESMFold, two prominent AI-based protein structure prediction methods. Our investigation of AF2's ability to explore the conformational diversity of the","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Jul","modification":"2026-06-02T23:12:05.304Z","creation":"2025-04-19T13:10:52.263Z"},"accession":"S-EPMC11268620","cross_references":{"pubmed":["39046952"],"doi":["10.1371/journal.pcbi.1012302"]}}