The resolution revolution has increasingly enabled single-particle cryo-EM reconstructions of previously inaccessible systems, including large membrane protein complexes that constitute a disproportionate share of drug targets. However, resolving structures in multiple functional states remains a challenge.
When structural changes are subtle, averaging of EM micrograph during image reconstruction typically leads to a single state. Parallel to developments in cryo-EM, computational methods for modeling and refining structures into EM maps have been developed, but their main focus has been to build accurate protein structures in the given map.
Here, the project's team proposes to extend the scope of a newly developed fitting algorithm in our lab, based on Bayes’ theorem, to not only accurately fit protein structure but also to determine the energetic landscape of protein function.
The new algorithm has already demonstrated its power to lead protein structure along its minimum energy path during the fitting process. The resulting pathway will be used to create seeds for parallel MD simulation followed by Markov state modeling.
The outcome of this project will establish a new avenue to construct free-energy landscapes directly from Cryo-EM maps.