Qiu, S.; Hu, B.; Zhao, J.; Xu, W.; Yang, A.

An accurate deep learning predictor is needed for enzyme optimal temperature (Topt), which quantitatively describes how temperature affects the enzyme catalytic activity. Seq2Topt, developed in this study, reached a superior accuracy on Topt prediction just using protein sequences (RMSE = 13.3 and R2=0.48) in comparison with existing models, and could capture key protein regions for enzyme Topt with multi-head attention on residues. Through case studies on thermophilic enzyme selection and predicting enzyme Topt shifts caused by point mutations, Seq2Topt was demonstrated as a promising computational tool for enzyme mining and in-silico enzyme design. Additionally, accurate deep learning predictors of enzyme optimal pH (Seq2pHopt, RMSE=0.92 and R2=0.37) and melting temperature (Seq2Tm, RMSE=7.57 and R2=0.64) were developed based on the model architecture of Seq2Topt, suggesting that the development of Seq2Topt could potentially give rise to a useful prediction platform of enzymes.