qProtein: Exploring Physical Features of Protein Thermostability Based on Structural Proteomics

Area of Science:

• Structural biology
• Computational chemistry
• Biophysics

Background:

• Protein thermostability is crucial for enzyme function but universal predictive principles remain elusive.
• Advances in deep learning, like AlphaFold2 and ESMFold, have revolutionized protein structure determination.
• Existing computational tools face challenges in analyzing protein thermostability on a large scale.

Purpose of the Study:

• To introduce qProtein, a Python workflow for quantitative analysis of physical interactions in structural proteomics.
• To investigate structural features contributing to protein thermostability across multiple glycoside hydrolase (GH) families.
• To assess the utility of qProtein in identifying determinants of thermal stability in enzymes.

Main Methods:

• Developed qProtein, a Python-implemented workflow accepting protein sequences.
• qProtein quantifies four structural features: hydrophobic clusters, hydrogen bonds, electrostatic interactions, and disulfide bonds.
• Analyzed 3,811 protein structures from six glycoside hydrolase families using qProtein.

Main Results:

• Thermophilic enzymes in five GH families (GH11, GH12, GH5_2, GH10, GH48) showed larger average hydrophobic cluster areas than nonthermophilic counterparts.
• Hydrophobic clusters were predominantly located in distal regions of GH11 enzymes.
• Thermophilic GH11 enzymes exhibited significantly larger hydrophobic cluster areas in distal regions compared to nonthermophilic enzymes.

Conclusions:

• qProtein is effective for analyzing structural features related to protein thermal stability at the scale of structural proteomics.
• Increased hydrophobic cluster area, particularly in distal regions, is a key feature associated with thermostability in certain GH families.
• The findings provide insights into the structural basis of enzyme thermostability and highlight qProtein's utility for future research.