Generalizable and scalable protein stability prediction with rewired protein generative models.

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

SUBMITTER: Li Z 

PROVIDER: S-EPMC12830971 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature