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A deep learning algorithm maps out the continuous conformational changes of flexible protein molecules from single-particle cryo-electron microscopy images, allowing the visualization of the
conformational landscape of a protein with improved resolution of its moving parts. Access through your institution Buy or subscribe This is a preview of subscription content, access via
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subscriptions * Read our FAQs * Contact customer support REFERENCES * Lyumkis, D. Challenges and opportunities in cryo-EM single-particle analysis. _Biol. Chem._ 29, 5181–5197 (2019). THIS
REVIEW ARTICLE PROVIDES AN OVERVIEW OF SINGLE-PARTICLE CRYO-EM FUNDAMENTALS AND THE IMPORTANCE OF MAPPING OUT CONFORMATIONAL HETEROGENEITY. Article Google Scholar * Punjani, A. &
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PAPER REPORTS A COMPUTATIONAL METHOD FOR FITTING A LINEAR SUBSPACE MODEL OF CRYO-EM DENSITY TO SINGLE PARTICLE DATA AND IS AN IMPORTANT BASELINE FOR METHODS THAT RESOLVE CONTINUOUS
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affiliations. THIS IS A SUMMARY OF: Punjani, A. & Fleet, D. J. 3DFlex: determining structure and motion of flexible proteins from cryo-EM. _Nat. Methods_
https://doi.org/10.1038/s41592-023-01853-8 (2023). RIGHTS AND PERMISSIONS Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Mapping the motion and structure of flexible proteins
from cryo-EM data. _Nat Methods_ 20, 797–798 (2023). https://doi.org/10.1038/s41592-023-01883-2 Download citation * Published: 12 May 2023 * Issue Date: June 2023 * DOI:
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