High-temperature industrial biocatalysis requires highly thermostable enzymes with retained catalytic efficiency. Glycoside hydrolase family 1 (GH1) enzymes, key catalysts for cellulose saccharification and glycoside biotransformation, are constrained by the dynamic coupling among the (β/α)₈ triosephosphate isomerase (TIM)-barrel scaffold, substrate channel loops, and surface electrostatic networks. This review surveys multiscale computational approaches—including molecular dynamics (MD) simulations with enhanced sampling, free-energy calculations, machine learning (ML), and protein language models (PLMs)—for engineering GH1 thermostability. We compare the model transferability across β-glucosidases, β-galactosidases, and β-mannosidases, discuss the possible structural origins of epistasis in multisite mutations, and summarize a closed-loop workflow of “structural modeling-dynamics profiling-energy ranking-intelligent combinatorial mutagenesis-experimental validation.” Furthermore, we propose a three-tier cooperative stabilization (3TCS) framework, featuring optimized TIM-barrel core rigidity, regulated loop dynamics, and reinforced surface electrostatic networks, to guide the rational design of thermostable GH1 biocatalysts.

Contents

1 Introduction

2 Structural Characteristics and Thermostability of GH1 Family Enzymes

2.1 Typical (β/α)₈ TIM-barrel Architecture

2.2 Computational Thermodynamic Description of Thermostability

2.3 Common Rules of Thermostability in GH1 Family

2.4 Impact of GH1 Family Subtype Differences on Stability Design and Model Transferability

3 Application of Computational Simulation Methods in Thermostability Research

3.1 Molecular Dynamics Simulation

3.2 Stability-Related Energy Evaluation and Free-Energy Calculation

3.3 Machine Learning and Artificial Intelligence Methods

3.4 Multiscale Integration Strategies

4 Challenges, Industrial Translation, and Future Prospects

4.1 Current Key Challenges

4.2 Emerging Computational Tools and Development Trends

5 Conclusions and Prospects

5.1 Conclusions

5.2 Prospects