Computational Method to Identify Protein Hotspots and Allosteric Sites

Protein allostery is important in many biological processes and drug development. Understanding the mechanisms involved would be highly beneficial for therapeutic interventions. Existing techniques focus primarily on equilibrium dynamics and network analysis for allosteric interaction mapping. Unfortunately, these are limited by their reliance on steady-state analysis and time-dependence, which results in missed relevant temporal aspects of allosteric communication.

 

Researchers at Arizona State University have developed a novel computational method to understand protein allostery and regulation to help identify regulatory sites in proteins, predict mutational effects on proteins, and develop effective therapeutic strategies targeting allosteric regulation in various disease-relevant proteins. This method combines molecular dynamics and time-dependent linear response theory to identify protein allosteric sites and hotspots. By analyzing protein active sites and dynamic residue responses over time and frequency domains, and integrating machine learning, this method is able to predict mutational impacts and identify regulatory sites. This could have great applications in studying antibiotic resistance and drug target discovery.

 

By combining molecular dynamic trajectories with time-dependent linear response theory, this new approach is able to provide mechanistic insights on allosteric regulations.

 

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