https://3demmethods.i2pc.es/index.php?action=history&feed=atom&title=2024Huang_MiLoPYP 2026-05-24T21:06:44Z Revision history for this page on the wiki MediaWiki 1.44.2 https://3demmethods.i2pc.es/index.php?title=2024Huang_MiLoPYP&diff=4780&oldid=prev 2024-10-08T10:54:18Z

<p>Created page with &quot;== Citation == Q. Huang, Y. Zhou, and A. Bartesaghi, “MiLoPYP: self-supervised molecular pattern mining and particle localization in situ,” Nature Methods, pp. 1–10, 2024. == Abstract == Cryo-electron tomography allows the routine visualization of cellular landscapes in three dimensions at nanometer-range resolutions. When combined with single-particle tomography, it is possible to obtain near-atomic resolution structures of frequently occurring macromolecules w...&quot;</p> <p><b>New page</b></p><div>== Citation ==<br /> <br /> Q. Huang, Y. Zhou, and A. Bartesaghi, “MiLoPYP: self-supervised molecular pattern mining and particle localization in situ,” Nature Methods, pp. 1–10, 2024.<br /> <br /> == Abstract ==<br /> <br /> Cryo-electron tomography allows the routine visualization of cellular<br /> landscapes in three dimensions at nanometer-range resolutions. When<br /> combined with single-particle tomography, it is possible to obtain<br /> near-atomic resolution structures of frequently occurring macromolecules<br /> within their native environment. Two outstanding challenges associated<br /> with cryo-electron tomography/single-particle tomography are the<br /> automatic identification and localization of proteins, tasks that are hindered<br /> by the molecular crowding inside cells, imaging distortions characteristic<br /> of cryo-electron tomography tomograms and the sheer size of tomographic<br /> datasets. Current methods suffer from low accuracy, demand extensive and<br /> time-consuming manual labeling or are limited to the detection of specific<br /> types of proteins. Here, we present MiLoPYP, a two-step dataset-specific<br /> contrastive learning-based framework that enables fast molecular pattern<br /> mining followed by accurate protein localization. MiLoPYP’s ability to<br /> effectively detect and localize a wide range of targets including globular and<br /> tubular complexes as well as large membrane proteins, will contribute to<br /> streamline and broaden the applicability of high-resolution workflows for<br /> in situ structure determination.<br /> <br /> == Keywords ==<br /> <br /> == Links ==<br /> <br /> https://www.nature.com/articles/s41592-024-02403-6<br /> <br /> == Related software ==<br /> <br /> == Related methods ==<br /> <br /> == Comments ==</div>

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