AS‐pHopt: An Optimal pH Prediction Model Enhanced by Active Site of Enzymes

The catalytic activity of enzymes is highly dependent on the environmental pH. Mining enzymes with high activity under specific pH conditions can enhance catalytic efficiency, rendering significant industrial application value and economic benefits. To address the current challenge of insufficient accuracy in predicting enzyme optimal pH (pHopt), we developed active site‐based pHopt (AS‐pHopt), a prediction model enhanced by information of active site and pseudo‐label prediction. AS‐pHopt integrates key structural information from active sites and other physicochemical properties, which significantly influence enzyme pHopt. It uses Evolutionary Scale Modeling (ESM)‐2 with active site weighting to capture the active site‐specific information and ESM‐Cambrian to capture the overall protein representation. Additionally, a double cross‐attention mechanism is employed to merge both global and local protein information. To reduce prediction bias, we introduce a secondary loss based on cosine similarity and an label distribution smoothing‐weighted loss to optimize the model's overall performance. Compared with other existing studies, AS‐pHopt improves the R 2 by 8%–20%. Meanwhile, by combining few‐shot fine‐tuning, AS‐pHopt has achieved the screening and prediction of enzyme mutants to a certain extent. Overall, AS‐pHopt offers a promising strategy for accurate pHopt prediction and for advancing enzyme engineering across diverse biocatalytic applications.