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bioRxiv · 10.1101/2024.08.12.607600

Seq2Topt: a sequence-based deep learning predictor of enzyme optimal temperature

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

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.

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Qiu, S., Hu, B., Zhao, J., Xu, W., Yang, A.. 2024-08-14. Seq2Topt: a sequence-based deep learning predictor of enzyme optimal temperature. https://doi.org/10.1101/2024.08.12.607600

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