Cryo-EM Data Statistics and Theoretical Analysis of KaiC Hexamer

Abstract

Cryo-electron microscopy (cryo-EM) provides a powerful tool to resolve the structure of biological macromolecules in natural state. One advantage of cryo-EM technology is that different conformation states of a protein complex structure can be simultaneously built, and the distribution of different states can be measured. This provides a tool to push cryo-EM technology beyond just to resolve protein structures, but to obtain the thermodynamic properties of protein machines. Here, we used a deep manifold learning framework to get the conformational landscape of KaiC proteins, and further obtained the thermodynamic properties of this central oscillator component in the circadian clock by means of statistical physics.