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	<title>2022Zhang CRITASSER - Revision history</title>
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	<updated>2026-05-24T20:17:59Z</updated>
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		<title>WikiSysop: Created page with &quot;== Citation ==  Zhang, Xi / Zhang, Biao / Freddolino, Peter L. / Zhang, Yang. CR-I-TASSER: assemble protein structures from cryo-EM density maps using deep convolutional neura...&quot;</title>
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		<updated>2022-03-23T21:17:25Z</updated>

		<summary type="html">&lt;p&gt;Created page with &amp;quot;== Citation ==  Zhang, Xi / Zhang, Biao / Freddolino, Peter L. / Zhang, Yang. CR-I-TASSER: assemble protein structures from cryo-EM density maps using deep convolutional neura...&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;
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Zhang, Xi / Zhang, Biao / Freddolino, Peter L. / Zhang, Yang. CR-I-TASSER: assemble protein structures from cryo-EM density maps using deep convolutional neural networks. 2022-02, Nature methods, Vol. 19, p. 195-204&lt;br /&gt;
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== Abstract ==&lt;br /&gt;
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Cryo-electron microscopy (cryo-EM) has become a leading approach for protein structure determination, but it remains challenging to accurately model atomic structures with cryo-EM density maps. We propose a hybrid method, CR-I-TASSER (cryo-EM iterative threading assembly refinement), which integrates deep neural-network learning with I-TASSER assembly simulations for automated cryo-EM structure determination. The method is benchmarked on 778 proteins with simulated and experimental density maps, where CR-I-TASSER constructs models with a correct fold (template modeling (TM) score &amp;gt;0.5) for 643 targets that is 64% higher than the best of some other de novo and refinement-based approaches on high-resolution data samples. Detailed data analyses showed that the main advantage of CR-I-TASSER lies in the deep learning-based Cα position prediction, which significantly improves the threading template quality and therefore boosts the accuracy of final models through optimized fragment assembly simulations. These results demonstrate a new avenue to determine cryo-EM protein structures with high accuracy and robustness covering various target types and density map resolutions. &lt;br /&gt;
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== Keywords ==&lt;br /&gt;
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== Links ==&lt;br /&gt;
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https://www.nature.com/articles/s41592-021-01389-9&lt;br /&gt;
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== Related software ==&lt;br /&gt;
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== Related methods ==&lt;br /&gt;
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== Comments ==&lt;/div&gt;</summary>
		<author><name>WikiSysop</name></author>
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