AlphaFold2-guided Markov state modeling the conformational land- scape of inflammasome activation

Protein flexibility, motion and conformational transitions form the bedrock of biological processes.

Characterizing the conformations, their functional annotation, and the inter-conversion rates linking states are all thus essential prerequisites for understanding the molecular basis of protein function and thereby invaluable to drug-development efforts. However, relatively few biological processes have been thoroughly simulated or mapped. The project has recently been able to capture Cryo-EM structures of a major human inflammasome in two different, inactive and active, states.

While these new structures provide valuable insight into the protein function, structures of all functional states are necessary to address questions such as why certain mutations in the protein cause autoimmune diseases.

To fill this gap, the project team sought to integrate advanced molecular dynamics simulation techniques with artificial intelligence tools and experimental mutagenesis. AlphaFold2 (AF2), an AI-based tool, has recently demonstrated the capability of generating an ensemble of protein states.

The project will use these structures as a starting point for parallel MD simulations that will be stitched together using Markov state modeling. The knowledge developed in this project will shed light on the structure and energetics and kinetics along the conformational landscapeinflammasome function and how this landscape can be regulated with disease-related mutations.