Physics-Based Protein Networks Might Recover Effectful Mutations─a Case Study on Cathepsin G

Area of Science:

• Biophysics
• Computational Biology
• Structural Biology

Background:

• Molecular dynamics simulations are crucial for studying protein structure and dynamics, but often fail to capture relevant timescales.
• Analyzing simulation data is essential for identifying functionally important regions like mutation sites or allosteric hotspots.
• Existing analysis methods may not fully capture the complex dynamics influencing protein function.

Purpose of the Study:

• To introduce and validate a physics-based protein network for analyzing molecular dynamics simulation data.
• To assess the network's ability to identify functionally relevant regions in serine proteases.
• To compare the efficacy of the physics-based network against traditional distance-based methods.

Main Methods:

• Development of a physics-based network model representing residue interactions within proteins.
• Application of the network analysis to molecular dynamics simulations of cathepsin G and neutrophil elastase.
• Comparative analysis of network outputs with distance-based network approaches.

Main Results:

• The physics-based network successfully identified the catalytic triad in both cathepsin G and neutrophil elastase.
• The network effectively distinguished functional differences between highly similar enzymes, including a mutant cathepsin G.
• Physics-based network analysis demonstrated superior performance in capturing protein structural behavior compared to distance-based networks.

Conclusions:

• Physics-based protein networks offer a powerful approach for analyzing complex protein dynamics from simulation data.
• This method enhances the identification of critical functional sites and differences between related proteins.
• The developed network provides a more accurate representation of protein structural behavior than distance-based methods.