The Gaussian network model as a framework for allosteric analysis: dynamic distance, edge centrality, and entropy...

Area of Science:

• Biophysics
• Computational Biology
• Structural Biology

Background:

• Allosteric communication in proteins is vital for biological function.
• Understanding how conformational signals transmit through protein networks is a key challenge.

Purpose of the Study:

• To introduce a unified mathematical framework for quantifying protein allosteric communication.
• To identify critical residue pairs that act as bottlenecks in protein networks.

Main Methods:

• Developed a framework based on dynamic distance, edge centrality, and entropy sensitivity.
• Applied the framework to the oncoprotein KRAS, including analysis of mutations and drug binding.

Main Results:

• Demonstrated that dynamic distance, edge centrality, and entropy sensitivity are linearly related.
• Identified specific residue pairs in KRAS as crucial for allosteric communication.
• Showed how mutations and drug binding (adagrasib) rewire communication pathways.

Conclusions:

• The unified framework provides a graph-theoretical explanation for the functional importance of specific residue pairs in allosteric regulation.
• This approach can predict and explain how perturbations alter protein communication networks.