Advancing Enzyme Optimal pH Prediction via Retrieved Embedding Data Augmentation

The optimal enzyme pH is a critical factor that directly influences the catalytic efficiency of the enzymes. Accurate computational prediction of the optimal pH can greatly advance our understanding and design of enzymes for diverse scientific and industrial applications. However, current prediction tools often fall short in terms of accuracy and robustness. In this study, we propose OpHReda, a novel method that significantly improves enzyme optimal pH prediction by leveraging a retrieved embedding data augmentation mechanism. Given an enzyme sequence, OpHReda first retrieves similar sequence embeddings from a preconstructed augmentation database. It then jointly analyzes the original and retrieved embeddings through the Multiple Embedding Alignment transformer to narrow the prediction range. Finally, the calibrator integrates residue-level information with the refined prediction range to make the final prediction. By moving beyond the limitations of single-sequence-based models, OpHReda achieves a 55% improvement in F1-score compared to that of state-of-the-art methods. Extensive ablation studies demonstrate that this enhancement arises from the synergy between our tailored architecture and the augmentation mechanism. Overall, OpHReda offers a promising advancement in enzyme optimal pH prediction and holds potential for downstream applications such as enzyme engineering and rational design.