{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Li Z"],"funding":["U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences","U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS)","NIGMS NIH HHS"],"pagination":["891"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12830971"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["17(1)"],"pubmed_abstract":["Predicting changes in protein thermostability caused by amino acid substitutions is essential for understanding human diseases and engineering proteins for practical applications. While recent protein generative models demonstrate impressive zero-shot performance in predicting various protein properties without task-specific training, their strong unsupervised prediction ability remains underexploited to improve protein stability prediction. We present SPURS, a deep learning framework that rewires and integrates two complementary protein generative models-a protein language model and an inverse folding model-and reprograms this unified framework for stability prediction through supervised fine-tuning on mega-scale thermostability data. SPURS delivers accurate, efficient, and scalable stabi"],"journal":["Nature communications"],"pubmed_title":["Generalizable and scalable protein stability prediction with rewired protein generative models."],"pmcid":["PMC12830971"],"funding_grant_id":["R35GM150890","R35 GM150890"],"pubmed_authors":["Li Z","Luo Y"],"additional_accession":[]},"is_claimable":false,"name":"Generalizable and scalable protein stability prediction with rewired protein generative models.","description":"Predicting changes in protein thermostability caused by amino acid substitutions is essential for understanding human diseases and engineering proteins for practical applications. While recent protein generative models demonstrate impressive zero-shot performance in predicting various protein properties without task-specific training, their strong unsupervised prediction ability remains underexploited to improve protein stability prediction. We present SPURS, a deep learning framework that rewires and integrates two complementary protein generative models-a protein language model and an inverse folding model-and reprograms this unified framework for stability prediction through supervised fine-tuning on mega-scale thermostability data. SPURS delivers accurate, efficient, and scalable stabi","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Dec","modification":"2026-06-11T06:03:45.664Z","creation":"2026-06-11T03:12:01.231Z"},"accession":"S-EPMC12830971","cross_references":{"pubmed":["41422228"],"doi":["10.1038/s41467-025-67609-4"]}}