Disentangling conformational states of macromolecules in 3D-EM through likelihood optimization

By Sjors H. W. Scheres, Haixiao Gao, M. Valle, G. Herman, P. Eggermont, Joachim Frank, J. M. Carazo. Nature Methods, 2007

440 citations recorded by the source corpus.

Subfields: Ecology, Molecular Biology, Structural Biology

View Paper: https://www.semanticscholar.org/paper/1bf459fcb9355ea84b2326af3f99ddec50b8626c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecology

In Molecular Biology

In Structural Biology

Citations in the corpus

3D architecture of DNA Pol α reveals the functional core of multi-subunit replicative polymerases (2009, positive): “This structural evidence provides an explanation for the observation that Zn-2 of Pol d is necessary and sufficient for interaction with the B subunit in yeast two- hybrid experiments (Sanchez Garcia et al, 2004).”
Handbook of ion channels (2015, background): “Alternatively, the maximum-likelihood-based classification and multireference analysis may allow the original dataset to be divided into several subsets, each of which will yield the 3D reconstruction of a specific conformational state (Scheres et al. 2007a).”
Massively parallel unsupervised single-particle cryo-EM data clustering via statistical manifold learning (2016, neutral): “Since individual biomolecules may sample multiple conformations, a prerequisite for high-resolution structure determination is to obtain a ‘pure’ dataset that only includes particle images in the same conformational state [3].”
Single-Particle Cryo-EM at Crystallographic Resolution. (2015, methodology): “While there are many ways to classify particles according to their conformations or functional states, a particularly powerful approach is to use a maximum likelihood-based method for classification and refinement (Scheres et al., 2007).”
Image processing for electron microscopy single-particle analysis using XMIPP (2008, neutral): “This technique may yield structurally homogeneous subsets, even without knowing beforehand what kind of structural variability is present in the dat”
Characterisation of molecular motions in cryo-EM single-particle data by multi-body refinement in RELION (2018, methodology): “…classiﬁcation of a discrete number of three-dimensional states became possible with the introduction of maximum likelihood classiﬁcation methods (Scheres et al., 2007), which have since then been implemented in multiple image processing packages (Sorzano et al., 2004; Scheres, 2012b; Lyumkis…”
3D Variability Analysis: Resolving continuous flexibility and discrete heterogeneity from single particle cryo-EM (2020, methodology): “However, methods for fitting linear subspace models are not widely used in practice compared to K-way discrete 3D classification methods [12, 36, 38, 39] .”