https://3demmethods.i2pc.es/index.php?action=history&feed=atom&title=2023Fernandez_Subtraction 2026-05-24T20:20:12Z Revision history for this page on the wiki MediaWiki 1.44.2 https://3demmethods.i2pc.es/index.php?title=2023Fernandez_Subtraction&diff=4511&oldid=prev 2024-01-08T07:26:46Z

<p>Created page with &quot;== Citation == Fernandez-Gimenez, Estrella / Mart\inez, M. M. / Marabini, R. / Strelak, D. / Sánchez-Garc\ia, R. / Carazo, J. M. / Sorzano, C. O. S. A new algorithm for part...&quot;</p> <p><b>New page</b></p><div>== Citation ==<br /> <br /> Fernandez-Gimenez, Estrella / Mart\inez, M. M. / Marabini, R. / Strelak, D. / Sánchez-Garc\ia, R. / Carazo, J. M. / Sorzano, C. O. S. A new algorithm for particle weighted subtraction to decrease signals from unwanted components in single particle analysis. 2023. J. Structural Biology, Vol. 215, No. 4, p. 108024 <br /> <br /> == Abstract ==<br /> <br /> Single particle analysis (SPA) in cryo-electron microscopy (cryo-EM) is highly used to obtain the near-atomic<br /> structure of biological macromolecules. The current methods allow users to produce high-resolution maps<br /> from many samples. However, there are still challenging cases that require extra processing to obtain high<br /> resolution. This is the case when the macromolecule of the sample is composed of different components and we<br /> want to focus just on one of them. For example, if the macromolecule is composed of several flexible subunits and<br /> we are interested in a specific one, if it is embedded in a viral capsid environment, or if it has additional<br /> components to stabilize it, such as nanodiscs. The signal from these components, which in principle we are not<br /> interested in, can be removed from the particles using a projection subtraction method. Currently, there are two<br /> projection subtraction methods used in practice and both have some limitations. In fact, after evaluating their<br /> results, we consider that the problem is still open to new solutions, as they do not fully remove the signal of the<br /> components that are not of interest. Our aim is to develop a new and more precise projection subtraction method,<br /> improving the performance of state-of-the-art methods. We tested our algorithm with data from public databases<br /> and an in–house data set. In this work, we show that the performance of our algorithm improves the results<br /> obtained by others, including the localization of small ligands, such as drugs, whose binding location is unknown<br /> a priori.<br /> <br /> == Keywords ==<br /> <br /> == Links ==<br /> <br /> https://www.sciencedirect.com/science/article/pii/S1047847723000874<br /> <br /> == Related software ==<br /> <br /> == Related methods ==<br /> <br /> == Comments ==</div>

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