A new algorithm for particle weighted subtraction to decrease signals from unwanted components in single particle analysis

Journal article

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 prioriThe authors acknowledge the economic support from MICIN to the Instruct Image Processing Center (I2PC) as part of the Spanish participation in Instruct-ERIC, the European Strategic Infrastructure Project (ESFRI) in Structural Biology. Grant PID2019-104757RB-I00 is funded by MCIN/AEI/ 10.13039/ 501100011033 and “ERDF A way of making Europe”, by the European Union. The “Comunidad Autónoma de Madrid” through Grant S2022/BMD-7232, the European Union (EU) and Horizon 2020 through grant HighResCells (ERC-2018-SyG, Proposal: 810057). The authors also acknowledge grant PID2019-104098 GB-I00/AEI/10.13039/501100011033, cofunded by the Spanish State Research Agency and the European Regional Development and grant 2023AEP082 by Agencia Estatal CSIC. Ruben Sanchez-Garcia is funded by an Astex Pharmaceuticals Sustaining Innovation Post-Doctoral Awar

Authors: Fernández-Giménez, E; Martínez, MM; Marabini, R; Strelak, D; Sánchez-García, R

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Elsevier
• Journal: Journal of Structural Biology
• Volume: 215
• Issue: 4
• Pages: 108024-108024
• Article number: 108024
• Publication date: 2023-09-11
• DOI: 10.1016/j.jsb.2023.108024
• ISSN: 1047-8477
• Language: English
• Keywords: SIGNAL (programming language); A priori and a posteriori; Subtraction; Single particle analysis; Set (abstract data type); Sample (material); Algorithm; Physics; Biological system; Particle (ecology); Resolution (logic); Pixel; Computer science; Chromatography; Chemistry; Background subtraction; Mathematics; Artificial intelligence; Projection (relational algebra)