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

Sizhe Qiu¹, Bozhen Hu^2,3, Jing Zhao^4,5

¹Department of Engineering Science, University of Oxford, Parks Road, OX1 3PJ, Oxford, United Kingdom.

Briefings in Bioinformatics

|March 13, 2025

Summary

This summary is machine-generated.

A new deep learning model, Seq2Topt, accurately predicts enzyme optimal temperature using only protein sequences. This tool aids in enzyme mining and computational enzyme design.

Keywords:

attention mechanism deep learning enzyme optimal temperature sequence-based prediction thermophilic proteins

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A Protocol for Computer-Based Protein Structure and Function Prediction

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Published on: November 3, 2011

Area of Science:

Biochemistry
Computational Biology
Enzyme Engineering

Background:

Enzyme optimal temperature (Topt) is crucial for catalytic activity.
Accurate prediction of Topt is essential for enzyme applications.
Existing models have limitations in Topt prediction accuracy.

Purpose of the Study:

Develop a deep learning predictor for enzyme Topt using protein sequences.
Enhance enzyme mining and in-silico enzyme design.
Create a versatile prediction platform for enzyme properties.

Main Methods:

Developed Seq2Topt, a deep learning model utilizing protein sequences.
Employed multi-head attention to identify key protein regions for Topt.
Validated Seq2Topt through case studies on thermophilic enzymes and mutation effects.

Main Results:

Seq2Topt achieved superior accuracy in Topt prediction (RMSE = 12.26°C, R2 = 0.57).
The model identified critical protein regions influencing Topt.
Developed accurate predictors for enzyme optimal pH (Seq2pHopt) and melting temperature (Seq2Tm).

Conclusions:

Seq2Topt is a promising computational tool for enzyme discovery and design.
The model architecture can be extended to predict other enzyme properties.
This work lays the foundation for a comprehensive enzyme property prediction platform.