2024Schwab DynaMight: Difference between revisions
Created page with "== Citation == Schwab, Johannes / Kimanius, Dari / Burt, Alister / Dendooven, Tom / Scheres, Sjors H. W. DynaMight: estimating molecular motions with improved reconstruction from cryo-EM images. 2024. Nature Methods, p. 1-8 == Abstract == How to deal with continuously flexing molecules is one of the biggest outstanding challenges in single-particle analysis of proteins from cryogenic-electron microscopy (cryo-EM) images. Here, we present DynaMight, a software tool tha..." |
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Latest revision as of 06:53, 21 August 2024
Citation
Schwab, Johannes / Kimanius, Dari / Burt, Alister / Dendooven, Tom / Scheres, Sjors H. W. DynaMight: estimating molecular motions with improved reconstruction from cryo-EM images. 2024. Nature Methods, p. 1-8
Abstract
How to deal with continuously flexing molecules is one of the biggest outstanding challenges in single-particle analysis of proteins from cryogenic-electron microscopy (cryo-EM) images. Here, we present DynaMight, a software tool that estimates a continuous space of conformations in a cryo-EM dataset by learning three-dimensional deformations of a Gaussian pseudo-atomic model of a consensus structure for every particle image. Inversion of the learned deformations is then used to obtain an improved reconstruction of the consensus structure. We illustrate the performance of DynaMight for several experimental cryo-EM datasets. We also show how error estimates on the deformations may be obtained by independently training two variational autoencoders on half sets of the cryo-EM data, and how regularization of the three-dimensional deformations through the use of atomic models may lead to important artifacts due to model bias. DynaMight is distributed as free, open-source software, as part of RELION-5.
Keywords
Links
https://www.nature.com/articles/s41592-024-02377-5