Cryo-EM Data Statistics and Theoretical Analysis of KaiC Hexamer  

作　　者：Xu Han Zhaolong Wu Tian Yang Qi Ouyang 韩旭;吴赵龙;杨添;欧阳颀(Department of Physics,Peking University,Beijing 100871,China;Center for Quantitative Biology and Peking-Tsinghua Center for Life Sciences,AAIC,Peking University,Beijing 100871,China)

机构地区：[1]Department of Physics,Peking University,Beijing 100871,China [2]Center for Quantitative Biology and Peking-Tsinghua Center for Life Sciences,AAIC,Peking University,Beijing 100871,China

出　　处：《Chinese Physics Letters》2022年第7期24-29,共6页中国物理快报（英文版）

基　　金：supported by the National Natural Science Foundation of China (Grant No. 12090054)。

摘　　要：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 Kai C proteins, and further obtained the thermodynamic properties of this central oscillator component in the circadian clock by means of statistical physics.

关 键 词：Cryo-EM Data Statistics and Theoretical Analysis of KaiC Hexamer 

分 类 号：Q6-33[生物学—生物物理学] O212[理学—概率论与数理统计]