Biophysical JournalBiophysicsPeer-reviewed journal article
Thermal Adaptation of Extremozymes: Temperature-Sensitive Contact Analysis of Serine Proteases
Abstract
Enzyme thermal adaptation reflects a delicate interplay between sequence, structure, and dynamics of proteins, fine-tuning the catalytic activity to environmental demands. Understanding these evolutionary relationships can drive bioengineering advances, including industrial enzyme design, biocatalysts for extreme conditions, and novel therapeutics. This work explores sequence-dynamics connections in subtilisin-like serine protease homologs using a recently developed computational methodology that uses expanded ensemble simulations and temperature-sensitive contact analysis. We reveal that thermophilic enzymes achieve thermal stability through extensive salt bridges and hydrophobic networks, while psychrophilic enzymes rely on targeted interaction stability for cold adaptation. An unsupervised cluster analysis of residue conservation, flexibility, and hydrophobic interactions provides a comprehensive view of residue-specific contributions to thermal adaptation. These findings underscore the coordinated roles of conserved and variable regions in enzyme stability and offer a framework for tailoring enzymes to specific thermal properties for biotechnological applications. SIGNIFICANCEThis study reveals how subtilisin-like serine proteases adapt to extreme temperatures by balancing sequence, structure, and dynamics. Through multi-ensemble simulations, we demonstrate that conserved catalytic regions and specific residue variations allow fine-tuned thermal adaptation. The analysis of simulations reveals the importance of salt bridges and hydrophobic interactions in enhancing thermophilic enzyme stability. We find that psychrophilic enzymes exhibit unique stability mechanisms for cold environments. The approach used in this study provides a comprehensive framework for understanding enzyme thermal adaptation and offers valuable insights for bioengineering enzymes with tailored properties for industrial and medical applications.
- assay: proteomics
- research focus: protein folding & dynamics
- approach: deep learning
- approach: statistical model
- Citations
- 2
- FWCI
- 0.60
- Field percentile
- top 40%
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