Molecular Adaptations of Thermophilic Proteins—From Mechanistic Understanding to Machine Learning Approaches

Hybridized Evolution Accelerating Thermostability (HEAT): A Novel MOEA Approach to EASME Protein Engineering

The vital functions of life are driven by proteins — long folded strings of amino acids. Millions of proteins exist in nature, all optimized for a certain set of operating conditions. One particularly important trait of a protein is thermostability, or the tendency to maintain form and function at high temperatures. Protein thermostability is important for both biological organisms and industrial enzymatic processes that utilize them. This work proposes a novel hybrid EC approach, employing the EAs Simulating Molecular Evolution (EASME) software, for evolving thermostable variants of proteins with minimal computational overhead and customized problem-specific fitness functions. A variation of NSGA-II with a novel recombination/mutation interplay was designed to tackle the specific challenges of working with proteins. The results of this work, which computationally improved the thermostability of a protein more effectively than a state of the art machine learning model from 2025, demonstrate that EC may have been historically undervalued in the realm of protein design. The pipeline designed for this work could be applied to numerous other proteins, easily expanded to optimize other traits of those proteins, or employed on another problem entirely where mutations have a notable fitness cost.

Dual Engineering of the Hydrophobic Core and Functional Loop Reshapes the Conformational Energy Landscape for Significantly Enhanced Xylanase Activity and Thermostability.

The industrial application of enzyme catalysts is often constrained by the trade-off between thermostability and catalytic activity. Here, a region-focused engineering strategy was applied to a thermophilic GH10 xylanase to simultaneously improve both properties. The strategy integrates qProtein-guided hydrophobic cluster design for scaffold stabilization and dynamic loop analysis for active-site optimization. The resulting triple mutant A206S-N209D-F130L exhibited substantially improved thermostability, with a 5.79 °C increase in melting temperature and an 18.8-fold extension of the half-life at 60 °C. Its optimum temperature increased from 60 to 70 °C, accompanied by a 129.4% enhancement in catalytic activity at 70 °C relatively to the wild type. Molecular dynamics simulations indicated that these mutations reshape the conformational energy landscape by stabilizing hydrophobic packing and modulating loop dynamics. This study provides a generalizable framework for simultaneously improving enzyme stability and catalytic performance.

Machine learning guided cell-free expression maps the biochemical landscape of carbonic anhydrase

This work demonstrates that integrating cell-free enzyme engineering with machine learning enables opportunities for high-throughput experimental measurements to benchmark and improve protein language models, accelerate design loops, and expand functional exploration within protein families where experimental information is limited.

Computer-aided multi-shell electrostatic remodeling of BhS7Xyl for enhanced activity and Thermostability.

Xylanases with high catalytic efficiency and environmental robustness are important for lignocellulosic biomass valorization, but many enzymes are rapidly inactivated under the alkaline and high-temperature conditions used in industrial processes. In this study, a computationally guided rational-design strategy was developed to improve the catalytic performance and stability of the alkaline xylanase BhS7Xyl. Constant-pH molecular dynamics, isothermal compressibility perturbation analysis, and ECNet-assisted fitness prediction were integrated to identify alkaline-sensitive and structurally unstable residues for engineering. The triple mutant H51R/D150N/E287K showed the best overall performance, with a specific activity of 1045.29 U/mg, representing a 3.73-fold increase compared with the wild type. Its melting temperature increased from 55.82 °C to 64.58 °C, while its half-life at pH 10.0 increased from 33.96 to 95.84 min. The thermal half-life at 75 °C was extended from 10.97 to 215.42 min, corresponding to a 19.64-fold improvement. Structural analyses suggested that the improved performance of H51R/D150N/E287K was associated with a more continuous xylohexaose-binding interface, increased hydrogen-bonding contacts, additional electrostatic/polar interactions, strengthened local interaction networks and enhanced dissipation of local thermal perturbation. Under optimized hydrolysis conditions, the triple mutant produced higher levels of xylose and xylooligosaccharides from standard xylan, corn cob xylan, and hardwood pulp xylan than the wild type. These work demonstrates that multi-shell electrostatic remodeling is a useful strategy for improving the activity, alkaline tolerance, and thermal stability of xylanase for xylooligosaccharide production.

Thermostability modification and simulated application of acidic catalase

Network reorganization of a cold-adapted β-glucosidase couples conformational flexibility to catalytic efficiency under low-temperature conditions

Introduction Elucidating how cold-active enzymes maintain efficient catalysis under low-temperature conditions remains a significant question in enzymology. Although enhanced conformational flexibility has been frequently associated with cold adaptation, flexibility alone cannot fully explain how catalytic precision and reproducibility are maintained. This suggests that an additional layer of structural organization is required. Methods In this study, a psychrophilic β-glucosidase (pBGL) from Pseudoalteromonas sp. BSw20308 and its mutant variant pBGL-S306P were examined through biochemical assays, molecular dynamics simulations, and dynamic interaction network analysis. Results The results indicate that pBGL preserves high catalytic efficiency at low temperature despite pronounced thermal instability, maintaining a rigid and conserved catalytic core. Notably, increased flexibility is spatially redistributed toward peripheral and interfacial regions rather than being globally amplified. Network analysis reveals that cold adaptation is associated with a distributed interaction network that connects these flexible regions to the catalytic core, constraining motions into coordinated dynamics that support catalysis. Discussion These findings suggest that cold adaptation in pBGL may involve not only enhanced flexibility but also network-mediated organization of conformational dynamics, providing a structural framework for understanding the activity–stability balance of cold-active enzymes.