Modelling structures in cryo-EM maps

Malhotra, Sony; Träger, Sylvain; Peraro, Matteo Dal; Topf, Maya

doi:10.1016/j.sbi.2019.05.024

Cited by 57 publications

(56 citation statements)

References 121 publications

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“…[1][2][3] Here, we highlight some of the new efforts to expand this direction, while acknowledging that more exhaustive overview of traditional modeling and validation methods have recently been published. 4,5 Cryo-EM has uses beyond being a powerful technique for determining PDB structures.…”

mentioning

confidence: 99%

What Will Computational Modeling Approaches Have to Say in the Era of Atomistic Cryo-EM Data?

Fraser

Lindorff‐Larsen

Bonomi

2020

J. Chem. Inf. Model.

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confidence: 99%

What Will Computational Modeling Approaches Have to Say in the Era of Atomistic Cryo-EM Data?

Fraser

Lindorff‐Larsen

Bonomi

2020

J. Chem. Inf. Model.

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“…Two areas are expected to make substantial progress in coming years. First, as the resolution of cryo-EM improves, the study of macromolecular conformational dynamics will become possible, which requires new development of the software that enables it [87]. Macromolecules may have discrete or continuous conformational states [105].…”

Section: Discussionmentioning

confidence: 99%

“…Rigid fitting methods fit atomic structures into EM maps of intermediate to low resolution, while flexible fitting improves the quality of the fitting into intermediate resolution EM maps by performing conformational change to the atomic structure to align with the EM map structure [87,88]. The main advantage of fitting methods is that models can be built with relatively inexpensive computational cost.…”

Section: Flexible Fitting Methodsmentioning

confidence: 99%

Advances in Structure Modeling Methods for Cryo-Electron Microscopy Maps

Alnabati

Kihara

2019

Molecules

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Cryo-electron microscopy (cryo-EM) has now become a widely used technique for structure determination of macromolecular complexes. For modeling molecular structures from density maps of different resolutions, many algorithms have been developed. These algorithms can be categorized into rigid fitting, flexible fitting, and de novo modeling methods. It is also observed that machine learning (ML) techniques have been increasingly applied following the rapid progress of the ML field. Here, we review these different categories of macromolecule structure modeling methods and discuss their advances over time.Molecules 2020, 25, 82 2 of 13 methods, in this order. Then, we discuss methods that use machine learning approaches, which are emerging in recent years in the cryo-EM structure modeling field. Figure 1. The number of rigid fitting, flexible fitting, and de novo modeling software published per year. The statistics are based on publication. The plot shows 28 rigid fitting methods [9-36], 33 flexible fitting methods [37-69], and 8 de novo modeling methods [70-77]. Rigid Fitting MethodsIn rigid body fitting, high resolution atomic models which are derived from X-ray crystallography, Nuclear Magnetic Resonance (NMR) spectroscopy, or protein prediction are fitted into a cryo-EM map. One of the earliest rigid fitting methods is EMfit developed by Rossmann et al. in 2001 [12]. In EMfit, a high-resolution structure is placed manually into a specific position in the EM map. Then, a 3-D rotational search is applied to find the best orientation. After that, EMfit optimizes the initial fitting by performing local rotational and translational steps. In general, rigid-body fitting methods search for the best placement of an atomic model in a density map. Search algorithms that have been used for rigid fitting include Fast Fourier transform-based (FFT) [14,32,35], grid-threading Monte Carlo (GTMC) [16], spherical harmonic-based search [20], and geometric hashing [27]. FFT is a fast search scheme that accelerates the 3-D translational search [14]. HermiteFit speeds up the rotation step in the FFT by representing densities as three-dimensional orthogonal Hermite functions and performing rotation in the Hermite space [32]. Fast polar Fourier search is a variation of FFT, which is based on non-uniform SO(3) Fourier Transforms [35]. Its principal advantage is the ability to search efficiently and uniformly over a set of samples of the conformational space. GTMC combines grid search and Monte Carlo sampling [16]. GTMC divides the search space into grid points and uses Monte Carlo to find local maxima near the grid points to identify the global maximum. ADP_EM is a spherical harmonic-based (SH) method which applies exhaustive translational scanning and accelerates the rotational search by representing densities as SH functions [20]. Geometric hashing identifies a set of possible transformations which are stored in a fast-searchable hash map [27]. Later, the set of transformations is searched to find the best fit.While the methods above e...

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“…The resolution of the cryo-EM map dictates the approach to be adopted for model building, tting, re nement and validation to a great extent 4 . Regardless of the resolution of the map, upon model building and/or tting, assessment of the atomistic model is crucial to ensure its overall reliability, and thus should be independent of the score(s) optimised during the tting stage.…”

Section: Introductionmentioning

confidence: 99%

Assessment of protein-protein interfaces in cryo-EM derived assemblies

Malhotra

Joseph

Thiyagalingam

et al. 2020

Preprint

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Structures of macromolecular assemblies derived from cryo-EM maps often contain errors that become more abundant with decreasing resolution. Despite efforts in the cryo-EM community to develop metrics for the map and atomistic model validation, thus far, no specific scoring metrics have been applied systematically to assess the interface between the assembly subunits. Here, we have assessed protein-protein interfaces in macromolecular assemblies derived by cryo-EM. To this end, we developed PI-score, a density-independent machine learning-based metric, trained using protein-protein interfaces’ features in high-resolution crystal structures. Using PI-score, we were able to identify errors at interfaces in the PDB-deposited cryo-EM structures (including SARS-CoV-2 complexes) and in the models submitted for cryo-EM targets in CASP13 and the EM model challenge. Some of the identified errors, especially at medium-to-low resolution structures, were not captured by density-based assessment scores. Our method can therefore provide a powerful complementary assessment tool for the increasing number of complexes solved by cryo-EM.

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Modelling structures in cryo-EM maps

Cited by 57 publications

References 121 publications

What Will Computational Modeling Approaches Have to Say in the Era of Atomistic Cryo-EM Data?

What Will Computational Modeling Approaches Have to Say in the Era of Atomistic Cryo-EM Data?

Advances in Structure Modeling Methods for Cryo-Electron Microscopy Maps

Assessment of protein-protein interfaces in cryo-EM derived assemblies

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