Molecular Adaptations of Thermophilic Proteins—From Mechanistic Understanding to Machine Learning Approaches
TL;DR
Combining experimental thermodynamic data, structural studies, and modern AI‐based computational tools, it is possible to develop more reliable strategies to design thermostable proteins, in particular, for various industrial and biotechnological applications.
Abstract
Thermophilic proteins are known for their stability and activity at elevated temperatures and hence serve as useful models for engineering enzymes with enhanced stability. The stability of these proteins is governed by both thermodynamic and kinetic factors that affect the folding–unfolding equilibrium and the rate of irreversible denaturation. Structural studies have shown that enhanced thermostability often results from multiple cooperative interactions, including improved hydrophobic packing, strengthened electrostatic interactions, and optimized hydrogen‐bonding networks. Comparative studies between thermophilic and mesophilic proteins have helped to identify general mechanisms that support thermal adaptations. Mutational and protein‐engineering approaches have further demonstrated the influence of specific amino acid residues or motifs on the stability through altered or modified local structural interactions. In recent years, computational approaches and machine learning methods have emerged as inevitable tools to predict protein stability. Although these approaches have improved the predictions, there still exist several limitations like limited training datasets, bias toward certain protein families, difficulties in model interpretation, etc. Therefore, combining experimental thermodynamic data, structural studies, and modern AI‐based computational tools, it is possible to develop more reliable strategies to design thermostable proteins, in particular, for various industrial and biotechnological applications.