  We introduce TemPL, a novel deep learning approach for zero-shot prediction
of protein stability and activity, harnessing temperature-guided language
modeling. By assembling an extensive dataset of 96 million sequence-host
bacterial strain optimal growth temperatures (OGTs) and {\Delta}Tm data for
point mutations under consistent experimental conditions, we effectively
compared TemPL with state-of-the-art models. Notably, TemPL demonstrated
superior performance in predicting protein stability. An ablation study was
conducted to elucidate the influence of OGT prediction and language modeling
modules on TemPL's performance, revealing the importance of integrating both
components. Consequently, TemPL offers considerable promise for protein
engineering applications, facilitating the design of mutation sequences with
enhanced stability and activity.
