Engineering a protein’s stability improves its shelf life and expands its application environment. Current studies of protein stability often involve predicting stability change from single-point mutations. However, the prediction model must be able to resolve single-character difference in a protein sequence typically several hundred amino acids long.

In this study, we predicted single-point mutational effect on protein stability and compared sequence-only and geometric learning approaches. Despite the inclusion of structural information, we showed that geometric learning does not outperform non-geometric models. Surprisingly, a simple MLP incorporating only the embed- ding at the mutation site performs the best. The finding could be attributed to the limited non-local mutational effect in the embedding.