MEPSi: A tool for simulating tomograms of membrane-embedded proteins

Francisco, Borja Rodríguez de; Bezault, Armel; Xu, Xiaoping; Hanein, Dorit; Volkmann, Niels

doi:10.1016/j.jsb.2022.107921

Cited by 3 publications

(3 citation statements)

References 38 publications

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“…There are more accurate tools than IMOD for reconstructing cryo-tomograms from synthetic data [27], [29]. They have more complex models for noise, which also take into account solvent contribution.…”

Section: Implementation Detailsmentioning

confidence: 99%

“…Simulations have already been used for particle picking and subtomogram averaging assessment [25], [26]. Recently, a tool specifically designed for membrane-embedded proteins has been proposed [27], however, it is limited to a single spherical membrane with uniformly distributed isolated proteins. Simulated annealing and molecular dynamics are used afterward to improve the packing and avoid volume overlapping by approximating molecules to spheres [26].…”

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confidence: 99%

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Simulating the cellular context in synthetic datasets for cryo-electron tomography

Martinez-Sanchez,

Lamm,

Jasnin

et al. 2024

IEEE Trans. Med. Imaging

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Cryo-electron tomography (cryo-ET) allows to visualize the cellular context at macromolecular level. To date, the impossibility of obtaining a reliable ground truth is limiting the application of deep learning-based image processing algorithms in this field. As a consequence, there is a growing demand of realistic synthetic datasets for training deep learning algorithms. In addition, besides assisting the acquisition and interpretation of experimental data, synthetic tomograms are used as reference models for cellular organization analysis from cellular tomograms. Current simulators in cryo-ET focus on reproducing distortions from image acquisition and tomogram reconstruction, however, they can not generate many of the low order features present in cellular tomograms.Here we propose several geometric and organization models to simulate low order cellular structures imaged by cryo-ET. Specifically, clusters of any known cytosolic or membrane bound macromolecules, membranes with different geometries as well as different filamentous structures such as microtubules or actin-like networks. Moreover, we use parametrizable stochastic models to generate a high diversity of geometries and organizations to simulate representative and generalized datasets, including very crowded environments like those observed in native cells.These models have been implemented in a multiplatform open-source Python package, including scripts to generate cryo-tomograms with adjustable sizes and resolutions. In addition, these scripts provide also distortion-free density maps besides the ground truth in different file formats for efficient access and advanced visualization. We show that such a realistic synthetic dataset can be readily used to train generalizable deep learning algorithms.

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Section: Implementation Detailsmentioning

confidence: 99%

mentioning

confidence: 99%

Simulating the cellular context in synthetic datasets for cryo-electron tomography

Martinez-Sanchez,

Lamm,

Jasnin

et al. 2024

IEEE Trans. Med. Imaging

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show abstract

“…Open databases for 3D structural data of biomolecules, e.g., the Protein Data Bank (PDB) 26 , give access to structures that can serve as templates for template matching. However, despite the remarkable advancements in the physical modeling of cryo-ET data [27][28][29][30] , templates were not used for supervised training of deep learning models on customized particle picking 20,25,31 . Some methods found a use for simulated datasets in training deep learning-based general-purpose particle picking (e.g.…”

Section: Introductionmentioning

confidence: 99%

Template Learning: Deep Learning with Domain Randomization for Particle Picking in Cryo-Electron Tomography

Harastani,

Patra,

Kervrann

et al. 2024

Preprint

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Cryo-electron tomography (cryo-ET) enables the three-dimensional visualization of biomolecules and cellular components in their near-native state. Particle picking, a crucial step in cryo-ET data analysis, is traditionally performed by template matching-a method utilizing cross-correlations with available biomolecular templates. Despite the effectiveness of recent deep learning-based particle picking approaches, their dependence on initial data annotation datasets for supervised training remains a significant limitation. Here, we propose a technique that combines the accuracy of deep learning particle identification with the convenience of the model training on biomolecular templates enabled through a tailored domain randomization approach. Our technique, named Template Learning, automates the simulation of training datasets, incorporating considerations for molecular crowding, structural variabilities, and data acquisition variations. This reduces or even eliminates the dependence of supervised deep learning on annotated experimental datasets. We demonstrate that models trained on simulated datasets, optionally fine-tuned on experimental datasets, outperform those exclusively trained on experimental datasets. Also, we illustrate that Template Learning used as an alternative to template matching, can offer higher precision and better orientational isotropy, especially for picking small non-spherical particles. Template Learning software is open-source, Python-based, and GPU and CPU parallelized.

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Simulating the cellular context in synthetic datasets for cryo-electron tomography

Martínez-Sánchez

Jasnin

Phelippeau³

et al. 2023

Preprint

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Cryo-electron tomography (cryo-ET) allows to visualize the cellular context at macromolecular level. To date, the impossibility of obtaining a reliable ground truth is limiting the application of deep learning-based image processing algorithms in this field. As a consequence, there is a growing demand of realistic synthetic datasets for training deep learning algorithms. In addition, besides assisting the acquisition and interpretation of experimental data, synthetic tomograms are used as reference models for cellular organization analysis from cellular tomograms. Current simulators in cryo-ET focus on reproducing distortions from image acquisition and tomogram reconstruction, however they cannot generate many of the low order features present in cellular tomograms. Here we propose several geometric and organization models to simulate low order cellular structures imaged by cryo-ET. Specifically, clusters of any known cytosolic or membrane-bound macromolecules, membranes with different geometries as well as different filamentous structures such as microtubules or actin networks. Moreover, we use parametrizable stochastic models to generate a high diversity of geometries and organizations to simulate representative and generalized datasets, including very crowded environments like those observed in native cells. These models have been implemented in a multiplatform open-source Python package, including scripts to generate cryo-tomograms with adjustable sizes and resolutions. In addition, these scripts also provide distortion free density maps besides the ground truth in different file formats for an efficient access and advanced visualization. We show that such a realistic synthetic dataset can be used to train generalizable deep learning algorithms.

show abstract

MEPSi: A tool for simulating tomograms of membrane-embedded proteins

Cited by 3 publications

References 38 publications

Simulating the cellular context in synthetic datasets for cryo-electron tomography

Simulating the cellular context in synthetic datasets for cryo-electron tomography

Template Learning: Deep Learning with Domain Randomization for Particle Picking in Cryo-Electron Tomography

Simulating the cellular context in synthetic datasets for cryo-electron tomography

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