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Cryo-EM Data Statistics and Theoretical Analysis of KaiC Hexamer |
| Xu Han1, Zhaolong Wu1, Tian Yang1, and Qi Ouyang1,2* |
1Department of Physics, Peking University, Beijing 100871, China
2Center for Quantitative Biology and Peking-Tsinghua Center for Life Sciences, AAIC, Peking University, Beijing 100871, China
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| Cite this article: |
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Xu Han, Zhaolong Wu, Tian Yang et al 2022 Chin. Phys. Lett. 39 070501 |
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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.
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Received: 11 May 2022
Editors' Suggestion
Published: 29 June 2022
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| PACS: |
05.10.Ln
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(Monte Carlo methods)
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02.50.-r
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(Probability theory, stochastic processes, and statistics)
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64.60.De
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(Statistical mechanics of model systems (Ising model, Potts model, field-theory models, Monte Carlo techniques, etc.))
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