随机六聚体
低温电子显微
组分(热力学)
分子机器
统计物理学
生物系统
高分子
物理
化学
计算机科学
生物物理学
纳米技术
生物
结晶学
材料科学
热力学
生物化学
作者
Xu Han,Zhaolong Wu,Ye Tian,Qi Ouyang
标识
DOI:10.1088/0256-307x/39/7/070501
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 KaiC proteins, and further obtained the thermodynamic properties of this central oscillator component in the circadian clock by means of statistical physics.
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