Tools for the cryo-EM gold rush: going from the cryo-EM map to the atomistic model

Kim, Doo Nam; Sanbonmatsu, Karissa Y.

doi:10.1042/bsr20170072

Cited by 19 publications

(20 citation statements)

References 110 publications

(187 reference statements)

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“…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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Section: Flexible Fitting Methodsmentioning

confidence: 99%

Advances in Structure Modeling Methods for Cryo-Electron Microscopy Maps

Alnabati

Kihara

2019

Molecules

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“…Computational methods integrating information obtained from these two complementary sources can, thus, supplement the overall information and provide novel insight into both large-scale, as well as atomic-level, structure and dynamics. Several methods were developed for fitting high-resolution crystal structures to EM density maps (for a recent review, see Kim et al [ 105 ]). These methods rely on different approaches to deform coordinates of known high-resolution crystals or NMR structures to fit low-resolution EM maps, such as normal-mode-based flexible fitting [ 106 , 107 ], coarse-grained model-based flexible fitting [ 108 , 109 ], all-atom molecular-dynamics-based fitting [ 110 ], deformable elastic-network-based fitting [ 111 ], Monte Carlo method-based fitting [ 112 ], or a combination of these methods [ 113 ].…”

Section: Computational Methods For Complementing/supplementing Expmentioning

confidence: 99%

Role of Computational Methods in Going beyond X-ray Crystallography to Explore Protein Structure and Dynamics

Srivastava

Nagai

Srivastava

et al. 2018

IJMS

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Protein structural biology came a long way since the determination of the first three-dimensional structure of myoglobin about six decades ago. Across this period, X-ray crystallography was the most important experimental method for gaining atomic-resolution insight into protein structures. However, as the role of dynamics gained importance in the function of proteins, the limitations of X-ray crystallography in not being able to capture dynamics came to the forefront. Computational methods proved to be immensely successful in understanding protein dynamics in solution, and they continue to improve in terms of both the scale and the types of systems that can be studied. In this review, we briefly discuss the limitations of X-ray crystallography in studying protein dynamics, and then provide an overview of different computational methods that are instrumental in understanding the dynamics of proteins and biomacromolecular complexes.

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“…If elucidated, this can help reveal crucial structure-function relationships involving dynamics 12 . Flexible fitting has been used to capture dynamics 11 with a larger radius of convergence than standard refinement, 13 as large as 34 Å 14 . Additionally, flexible fitting can describe multiple conformational changes (supporting Fig.…”

Section: Introductionmentioning

confidence: 99%

Cryo_fit: Democratization of flexible fitting for cryo-EM

Kim

Moriarty

Kırmızıaltın

et al. 2019

Journal of Structural Biology

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Cryo-electron microscopy (cryo-EM) is becoming a method of choice for describing native conformations of biomolecular complexes at high resolution. The rapid growth of cryo-EM in recent years has created a high demand for automated solutions, both in hardware and software. Flexible fitting of atomic models to three-dimensional (3D) cryo-EM reconstructions by molecular dynamics (MD) simulation is a popular technique but often requires technical expertise in computer simulation. This work introduces cryo_fit, a package for the automatic flexible fitting of atomic models in cryo-EM maps using MD simulation. The package is integrated with the Phenix software suite. The module was designed to automate the multiple steps of MD simulation in a reproducible manner, as well as facilitate refinement and validation through Phenix. Through the use of cryo_fit, scientists with little experience in MD simulation can produce high quality atomic models automatically and better exploit the potential of cryo-EM.

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Tools for the cryo-EM gold rush: going from the cryo-EM map to the atomistic model

Cited by 19 publications

References 110 publications

Advances in Structure Modeling Methods for Cryo-Electron Microscopy Maps

Advances in Structure Modeling Methods for Cryo-Electron Microscopy Maps

Role of Computational Methods in Going beyond X-ray Crystallography to Explore Protein Structure and Dynamics

Cryo_fit: Democratization of flexible fitting for cryo-EM

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