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	<title>2024Schwab DynaMight - Revision history</title>
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	<updated>2026-06-13T13:45:53Z</updated>
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	<entry>
		<id>https://3demmethods.i2pc.es/index.php?title=2024Schwab_DynaMight&amp;diff=4721&amp;oldid=prev</id>
		<title>WikiSysop: Created page with &quot;== 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...&quot;</title>
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		<updated>2024-08-21T06:53:02Z</updated>

		<summary type="html">&lt;p&gt;Created page with &amp;quot;== 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...&amp;quot;&lt;/p&gt;
&lt;p&gt;&lt;b&gt;New page&lt;/b&gt;&lt;/p&gt;&lt;div&gt;== Citation ==&lt;br /&gt;
&lt;br /&gt;
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&lt;br /&gt;
&lt;br /&gt;
== Abstract ==&lt;br /&gt;
&lt;br /&gt;
How to deal with continuously flexing molecules is one of the biggest&lt;br /&gt;
outstanding challenges in single-particle analysis of proteins from&lt;br /&gt;
cryogenic-electron microscopy (cryo-EM) images. Here, we present&lt;br /&gt;
DynaMight, a software tool that estimates a continuous space of&lt;br /&gt;
conformations in a cryo-EM dataset by learning three-dimensional&lt;br /&gt;
deformations of a Gaussian pseudo-atomic model of a consensus structure&lt;br /&gt;
for every particle image. Inversion of the learned deformations is then&lt;br /&gt;
used to obtain an improved reconstruction of the consensus structure. We&lt;br /&gt;
illustrate the performance of DynaMight for several experimental cryo-EM&lt;br /&gt;
datasets. We also show how error estimates on the deformations may be&lt;br /&gt;
obtained by independently training two variational autoencoders on half&lt;br /&gt;
sets of the cryo-EM data, and how regularization of the three-dimensional&lt;br /&gt;
deformations through the use of atomic models may lead to important&lt;br /&gt;
artifacts due to model bias. DynaMight is distributed as free, open-source&lt;br /&gt;
software, as part of RELION-5.&lt;br /&gt;
&lt;br /&gt;
== Keywords ==&lt;br /&gt;
&lt;br /&gt;
== Links ==&lt;br /&gt;
&lt;br /&gt;
https://www.nature.com/articles/s41592-024-02377-5&lt;br /&gt;
&lt;br /&gt;
== Related software ==&lt;br /&gt;
&lt;br /&gt;
== Related methods ==&lt;br /&gt;
&lt;br /&gt;
== Comments ==&lt;/div&gt;</summary>
		<author><name>WikiSysop</name></author>
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