FERNANDEZ-GIMENEZ, E., M M MARTINEZ, R. MARABINI, David STŘELÁK, R. SANCHEZ-GARCIA, J M CARAZO and C O S SORZANO. A new algorithm for particle weighted subtraction to decrease signals from unwanted components in single particle analysis. Journal of Structural Biology. UNITED STATES: ACADEMIC PRESS INC ELSEVIER SCIENCE, 2023, vol. 215, No 4, p. 1-8. ISSN 1047-8477. Available from: https://dx.doi.org/10.1016/j.jsb.2023.108024.
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Basic information
Original name A new algorithm for particle weighted subtraction to decrease signals from unwanted components in single particle analysis
Authors FERNANDEZ-GIMENEZ, E., M M MARTINEZ, R. MARABINI, David STŘELÁK (203 Czech Republic, guarantor, belonging to the institution), R. SANCHEZ-GARCIA, J M CARAZO and C O S SORZANO.
Edition Journal of Structural Biology, UNITED STATES, ACADEMIC PRESS INC ELSEVIER SCIENCE, 2023, 1047-8477.
Other information
Original language English
Type of outcome Article in a journal
Field of Study 10608 Biochemistry and molecular biology
Country of publisher United States of America
Confidentiality degree is not subject to a state or trade secret
WWW URL
Impact factor Impact factor: 3.000 in 2022
Organization unit Institute of Computer Science
Doi http://dx.doi.org/10.1016/j.jsb.2023.108024
UT WoS 001089468800001
Keywords in English Projection subtraction; Nanodisc; Ligand; SPA; Cryo-EM
Tags rivok
Tags International impact, Reviewed
Changed by Changed by: Mgr. Alena Mokrá, učo 362754. Changed: 27/8/2024 10:18.
Abstract
Single particle analysis (SPA) in cryo-electron microscopy (cryo-EM) is highly used to obtain the near-atomic structure of biological macromolecules. The current methods allow users to produce high-resolution maps from many samples. However, there are still challenging cases that require extra processing to obtain high resolution. This is the case when the macromolecule of the sample is composed of different components and we want to focus just on one of them. For example, if the macromolecule is composed of several flexible subunits and we are interested in a specific one, if it is embedded in a viral capsid environment, or if it has additional components to stabilize it, such as nanodiscs. The signal from these components, which in principle we are not interested in, can be removed from the particles using a projection subtraction method. Currently, there are two projection subtraction methods used in practice and both have some limitations. In fact, after evaluating their results, we consider that the problem is still open to new solutions, as they do not fully remove the signal of the components that are not of interest. Our aim is to develop a new and more precise projection subtraction method, improving the performance of state-of-the-art methods. We tested our algorithm with data from public databases and an in-house data set. In this work, we show that the performance of our algorithm improves the results obtained by others, including the localization of small ligands, such as drugs, whose binding location is unknown a priori.
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