Estimating conformational landscapes from Cryo-EM particles by 3D Zernike polynomials

Herreros, David; Lederman, Roy R.; Krieger, James; Jiménez-Moreno, Amaya; Martínez, Marta; Myška, David; Střelák, David; Filipovič, Jiří; Sorzano, Carlos Óscar S.; Carazo, José-Marı́a

doi:10.1038/s41467-023-35791-y

Cited by 35 publications

(39 citation statements)

References 25 publications

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“…We first analyzed the different options for regularisation of the deformations on a well characterised data set on the yeast Saccharomyces cerevisiae pre-catalytic B complex spliceosome [18](EMPIAR-10180, [19]). The same data, or subsets of it, have also been analyzed using multi-body refinement [5] cryoDRGN [9], Zernike3D [13] and [10]. To minimize computational costs and to ensure structural homogeneity [9], we used 3D classification in RELION [20] to select ∼ 45 k particles with reasonable density for the head region.…”

Section: Resultsmentioning

confidence: 99%

DynaMight: estimating molecular motions with improved reconstruction from cryo-EM images

Schwab,

Kimanius,

Burt

et al. 2023

Preprint

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How to deal with continuously flexing molecules is one of the biggest outstanding challenges in single-particle analysis of proteins from cryo-EM images. Here, we present DynaMight, a new software tool that estimates a continuous space of conformations in a cryo-EM data set by learning 3D deformations of a Gaussian pseudo-atomic model of a consensus structure for every particle image. Inversion of the learnt deformations is then used to obtain an improved reconstruction of the consensus structure. We illustrate the performance of DynaMight for several experimental cryo-EM data sets. We also show how error estimates on the deformations may be obtained by independently training two variational autoencoders (VAEs) on half sets of the cryo-EM data, and how regularisation of the 3D deformations through the use of atomic models may lead to important artefacts due to model bias. DynaMight is distributed as free, open-source software, as part of RELION-5.

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Section: Resultsmentioning

confidence: 99%

DynaMight: estimating molecular motions with improved reconstruction from cryo-EM images

Schwab,

Kimanius,

Burt

et al. 2023

Preprint

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“…The development of HEMNMA-3D was inspired by the SPA methods that describe the continuous conformational variability by displacing atoms of an available molecular structure (reference structure) to match the conformational states in the data 7,12,13,16 . Such methods restrain the conformational exploration to the energy region that preserves the structural elements of the complex, which reduces the risk of overfitting low-SNR data.…”

Section: Introductionmentioning

confidence: 99%

“…HEMNMA-3D is another method for analyzing continuous conformational variability in cryo-ET subtomograms, which is able to use either a reference-volume voxel displacement (as TomoFlow) or a reference-structure atomic displacement to match the conformational states in subtomograms 19 . The development of HEMNMA-3D was inspired by the SPA methods that describe the continuous conformational variability by displacing atoms of an available molecular structure (reference structure) to match the conformational states in the data 7,12,13,16 . Such methods restrain the conformational exploration to the energy region that preserves the structural elements of the complex, which reduces the risk of overfitting low-SNR data.…”

Section: Introductionmentioning

confidence: 99%

“…Such methods restrain the conformational exploration to the energy region that preserves the structural elements of the complex, which reduces the risk of overfitting low-SNR data. They displace atoms of a reference structure using different methods to model the deformation field, such as normal mode analysis 7,12 , MD simulations 13,15 , and Zernike polynomials 16 . HEMNMA 7 and its deep-learning-based extension DeepHEMNMA 12 perform a flexible alignment of the reference structure to single particle images along the direction in the energy landscape that is defined by a set of vectors of collective motions, called normal modes.…”

Section: Introductionmentioning

confidence: 99%

“…However, considering a relatively small number of available subtomograms, discrete classification methods are usually used with a small number of classes, which makes them ill-suited in the context of highly heterogeneous datasets composed of a continuum of conformational states. This heterogeneity issue, known as continuous conformational variability, is well-known in single particle (SPA) cryo electron microscopy (cryo-EM) and is currently an active field of research [7][8][9][10][11][12][13][14][15][16]17 . The number of methods for analyzing continuous conformational variability in SPA datasets has been growing over the last few years, but only a few such methods have been proposed for cryo-ET so far 18,19 .…”

Section: Introductionmentioning

confidence: 99%

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MDTOMO: Continuous conformational variability analysis in cryo electron subtomogram data using flexible fitting based on Molecular Dynamics simulations

Vuillemot

Rouiller

Jonić

2023

Preprint

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Cryo electron tomography (cryo-ET) allows observing macromolecular complexes in their native environment. The common routine of subtomogram averaging (STA) allows obtaining the three-dimensional (3D) structure of abundant macromolecular complexes, and can be coupled with discrete classification to reveal conformational heterogeneity of the sample. However, the number of complexes extracted from cryo-ET data is usually small, which restricts the discrete-classification results to a small number of enough populated states and, thus, results in a largely incomplete conformational landscape. Alternative approaches are currently being investigated to explore the continuity of the conformational landscapes that in situ cryo-ET studies could provide. In this article, we present MDTOMO, a method for analyzing continuous conformational variability in cryo-ET subtomograms based on Molecular Dynamics (MD) simulations. MDTOMO allows obtaining an atomic-scale model of conformational variability and the corresponding free-energy landscape, from a given set of cryo-ET subtomograms. The article presents the performance of MDTOMO on a synthetic ABC exporter dataset and an in situ SARS-CoV-2 spike dataset. MDTOMO allows analyzing dynamic properties of molecular complexes to understand their biological functions, which could also be useful for structure-based drug discovery.

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Computational biophysics meets cryo‐EM revolution in the search for the functional dynamics of biomolecular systems

Costa,

Gur,

Krieger

et al. 2023

WIREs Comput Mol Sci

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There is a variety of experimental and computational techniques available to explore protein dynamics, each presenting advantages and limitations. One promising experimental technique that is driving the development of computational methods is cryo‐electron microscopy (cryo‐EM). Cryo‐EM provides molecular‐level structural data and first estimates of conformational landscape from single particle analysis but cannot track real‐time protein dynamics and may contain uncertainties in atomic positions especially at highly dynamic regions. Molecular simulations offer atomic‐level insights into protein dynamics; however, their computing time requirements limit the conformational sampling accuracy, and it is often hard, to assess by full‐atomic simulations the cooperative movements of biological interest for large assemblies such as those resolved by cryo‐EM. Coarse‐grained (CG) simulations permit us to explore such systems, but at the costs of lower resolution and potentially incomplete sampling of conformational space. On the other hand, analytical methods may circumvent sampling limitations. In particular, elastic network models‐based normal mode analyses (ENM‐NMA) provide unique solutions for the complete mode spectra near equilibrium states, even for systems of megadaltons, and may thus deliver information on mechanisms of motions relevant to biological function. Yet, they lack atomic resolution as well as temporal information for non‐equilibrium systems. Given the complementary nature of these methods, the integration of molecular simulations and ENM‐NMA into hybrid methodologies has gained traction. This review presents the current state‐of‐the‐art in structure‐based computations and how they are helping us gain a deeper understanding of biological mechanisms, with emphasis on the development of hybrid methods accompanying the advances in cryo‐EM.This article is categorized under: Structure and Mechanism > Computational Biochemistry and Biophysics

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Estimating conformational landscapes from Cryo-EM particles by 3D Zernike polynomials

Cited by 35 publications

References 25 publications

DynaMight: estimating molecular motions with improved reconstruction from cryo-EM images

DynaMight: estimating molecular motions with improved reconstruction from cryo-EM images

MDTOMO: Continuous conformational variability analysis in cryo electron subtomogram data using flexible fitting based on Molecular Dynamics simulations

Computational biophysics meets cryo‐EM revolution in the search for the functional dynamics of biomolecular systems

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