2023
A new algorithm for particle weighted subtraction to decrease signals from unwanted components in single particle analysis
FERNANDEZ-GIMENEZ, E., M M MARTINEZ, R. MARABINI, David STŘELÁK, R. SANCHEZ-GARCIA et. al.Základní údaje
Originální název
A new algorithm for particle weighted subtraction to decrease signals from unwanted components in single particle analysis
Autoři
FERNANDEZ-GIMENEZ, E., M M MARTINEZ, R. MARABINI, David STŘELÁK (203 Česká republika, garant, domácí), R. SANCHEZ-GARCIA, J M CARAZO a C O S SORZANO
Vydání
Journal of Structural Biology, UNITED STATES, ACADEMIC PRESS INC ELSEVIER SCIENCE, 2023, 1047-8477
Další údaje
Jazyk
angličtina
Typ výsledku
Článek v odborném periodiku
Obor
10608 Biochemistry and molecular biology
Stát vydavatele
Spojené státy
Utajení
není předmětem státního či obchodního tajemství
Odkazy
Impakt faktor
Impact factor: 3.000 v roce 2022
Organizační jednotka
Ústav výpočetní techniky
UT WoS
001089468800001
Klíčová slova anglicky
Projection subtraction; Nanodisc; Ligand; SPA; Cryo-EM
Štítky
Příznaky
Mezinárodní význam, Recenzováno
Změněno: 27. 8. 2024 10:18, Mgr. Alena Mokrá
Anotace
V originále
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.