Predicting the effects of amino acid substitutions on protein thermostability is critical for understanding diseases and engineering proteins. Recent protein generative models excel in predicting properties from structural or evolutionary contexts but fall short in stability prediction. We introduce SPURS, a deep learning framework integrating a protein language model (ESM) and an inverse folding model (ProteinMPNN). SPURS rewires ProteinMPNN’s structural representations into ESM’s attention layers, combining structural and sequence information for mutation prediction. Trained on a mega-scale thermostability dataset, SPURS achieves state-of-the-art accuracy and scalability across benchmarks. Beyond stability, it identifies functional sites unsupervised and enhances low-N fitness models, establishing itself as a versatile tool for protein engineering and analysis.