New computational approaches capture molecular motion from cryo-EM images and provide a more complete understanding of protein dynamics.
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References
Zhong, E. D., Bepler, T., Berger, B. & Davis, J. H. Nat. Methods 18, 176–185 (2021).
Chen, M. & Ludtke, S. Nat. Methods https://doi.org/10.1038/s41592-021-01220-5 (2021).
Yip, K. M., Fischer, N., Paknia, E., Chari, A. & Stark, H. Nature 587, 157–161 (2020).
Nakane, T. et al. Nature 587, 152–156 (2020).
Cheng, Y., Grigorieff, N., Penczek, P. A. & Walz, T. Cell 161, 438–449 (2015).
Punjani, A. & Fleet, D. J. Preprint at bioRxiv https://doi.org/10.1101/2021.04.22.440893 (2021).
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Grant, T. Neural networks learn the motions of molecular machines. Nat Methods 18, 869–871 (2021). https://doi.org/10.1038/s41592-021-01235-y
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DOI: https://doi.org/10.1038/s41592-021-01235-y