J 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.