Discovery of Thermophilic Enzyme Properties Through Structural Modeling, Phylogenetic Analysis, and Machine Learning Prediction

The rapid growth of the global population and the urgent need for sustainable resource utilization have intensified research into renewable natural resources, particularly lignocellulosic biomass. Among the various enzymes involved in lignocellulose degradation, thermophilic glycoside hydrolases (GH) family 5 cellulases have gained significant attention due to their ability to sustain enzymatic activity at elevated temperatures exceeding 60°C. These high temperatures not only accelerate enzymatic reactions, improving reaction rates and process efficiency, but also enhance substrate solubility and reduce the risk of microbial contamination, making them highly valuable for paper, food, feed, pharmaceutical, and biofuel industries. In this work, we performed structural modeling of GH5 cellulases and investigated their thermophilicity, integrating data from experimental structures and computational simulations. We analyze structural characteristics such as compact protein folds, conserved active site residues, increased hydrophobic interactions, hydrogen bonds, and other interatomic contacts. Understanding these features is crucial for protein engineering and optimizing these enzymes for various high-temperature biotechnological applications.