https://3demmethods.i2pc.es/index.php?action=history&feed=atom&title=2019_Pothula 2026-05-24T21:06:01Z Revision history for this page on the wiki MediaWiki 1.44.2 https://3demmethods.i2pc.es/index.php?title=2019_Pothula&diff=3510&oldid=prev 2019-06-07T05:16:38Z

<p>Created page with &quot;== Citation == Pothula, K. R.; Smyrnova, D. &amp; Schroder, G. F. Clustering cryo-EM images of helical protein polymers for helical reconstructions. Ultramicroscopy, 2019 , 203 ,...&quot;</p> <p><b>New page</b></p><div>== Citation ==<br /> <br /> Pothula, K. R.; Smyrnova, D. &amp; Schroder, G. F. Clustering cryo-EM images of helical protein polymers for helical reconstructions. Ultramicroscopy, 2019 , 203 , 132-138 <br /> <br /> == Abstract ==<br /> <br /> Helical protein polymers are often dynamic and complex assemblies, with many conformations and flexible domains possible within the helical assembly. During cryo-electron microscopy reconstruction, classification of the image data into homogeneous subsets is a critical step for achieving high resolution, resolving different conformations and elucidating functional mechanisms. Hence, methods aimed at improving the homogeneity of these datasets are becoming increasingly important. In this paper, we introduce a new algorithm that uses results from 2D image classification to sort 2D classes into groups of similar helical polymers. We show that our approach is able to distinguish helical polymers that differ in conformation, composition, and helical symmetry. Our results on test and experimental cases - actin filaments and amyloid fibrils - illustrate how our approach can be useful to improve the homogeneity of a data set. This method is exclusively applicable to helical polymers and other limitations are discussed.<br /> <br /> == Keywords ==<br /> <br /> == Links ==<br /> <br /> https://www.sciencedirect.com/science/article/pii/S0304399118303164<br /> <br /> == Related software ==<br /> <br /> == Related methods ==<br /> <br /> == Comments ==</div>

WikiSysop