Rewiring protein sequence and structure generative models to enhance protein stability prediction.

ABSTRACT: Predicting changes in protein thermostability due to amino acid substitutions is essential for understanding human diseases and engineering useful proteins for clinical and industrial applications. While recent advances in protein generative models, which learn probability distributions over amino acids conditioned on structural or evolutionary sequence contexts, have shown impressive performance in predicting various protein properties without task-specific training, their strong unsupervised prediction ability does not extend to all protein functions. In particular, their potential to improve protein stability prediction remains underexplored. In this work, we present SPURS, a novel deep learning framework that adapts and integrates two general-purpose protein generative models-a protein

SUBMITTER: Li Z 

PROVIDER: S-EPMC11870403 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature