3DEM-Methods - Recent changes [en]

2026Silva CryoJax

WikiSysop — Fri, 22 May 2026 06:32:18 GMT

Created page with "== Citation == Silva-Sánchez, D., Berezuk, A.M., Zhu, X., Thiede, E.H., Lederman, R.R. and Cossio, P. 2026. Cryo-Electron Microscopy Structural Ensemble Optimization Using Individual Particles. J. of Chemical Theory and Computation. (2026). == Abstract == Biomolecules are inherently dynamic, transitioning between various conformational states to execute their biological functions; consequently, characterizing their ensemble distributions (the population of these conf..."

New page

== Citation ==

Silva-Sánchez, D., Berezuk, A.M., Zhu, X., Thiede, E.H., Lederman, R.R. and Cossio, P. 2026. Cryo-Electron Microscopy Structural Ensemble Optimization Using Individual Particles. J. of Chemical Theory and Computation. (2026).

== Abstract ==

Biomolecules are inherently dynamic, transitioning between various conformational states to execute their biological functions; consequently, characterizing their ensemble distributions (the population of these conformations) is essential for understanding their biological roles. Cryo-electron microscopy (cryo-EM), a technique that images individual biomolecules frozen in a thin layer of amorphous ice, has emerged as a leading method for determining the structure of biomolecules at atomic resolution. Recent advances in cryo-EM reconstruction have enabled significant progress in characterizing conformational variability around metastable states. In contrast to reconstruction, a different class of techniques has been used to infer population weights, referred to as ensemble reweighting. These methods have yet to be generalized to infer structures and weights simultaneously. Here, we present a method for cryo-EM ensemble optimization that directly infers the optimal set of conformations and their associated population weights from cryo-EM images using Bayesian optimization techniques. Our method iterates between optimizing the structures and weights using a likelihood defined in terms of cryo-EM particle images (not reconstructions) and projecting onto the domain of a physical prior through an approach inspired by projected gradient descent. We test the method on several systems, ranging from a four-atom toy model to two large protein systems with real cryo-EM data. We find that our approach successfully recovers the structures and their associated weights across a wide range of experimental conditions, even when the number of structures does not match the actual number of metastable states. Our method paves the way for cryo-EM structural ensemble optimization of flexible biomolecules exhibiting complex, multimodal conformational landscapes.

== Keywords ==

== Links ==

https://pubs.acs.org/doi/full/10.1021/acs.jctc.6c00053

== Related software ==

== Related methods ==

== Comments ==

Main Page

WikiSysop — Fri, 22 May 2026 06:30:02 GMT

3D Heterogeneity

← Older revision		Revision as of 06:30, 22 May 2026
(2 intermediate revisions by the same user not shown)
Line 68:		Line 68:

	[https://www.ebi.ac.uk/emdb/genealogy CryoEM genealogy]		[https://www.ebi.ac.uk/emdb/genealogy CryoEM genealogy]

			[https://rico.ibs.fr/helixplorer/resolution/ Resolution game]

			[https://rico.ibs.fr/helixplorer/fourier Fourier transform game]

	=== Online courses and Learning material ===		=== Online courses and Learning material ===
Line 3,485:		Line 3,489:
	\| [[2026Evans_Counting]]		\| [[2026Evans_Counting]]
	\| Counting particles in cryo-electron microscopy may result in incorrect population estimates.		\| Counting particles in cryo-electron microscopy may result in incorrect population estimates.
			\|-

			\| Paper
			\| [[2026Silva_CryoJax]]
			\| CryoJax Ensemble: Continuous heterogeneity analysis using atomic models
	\|-		\|-

2026Premaraj DualJet

WikiSysop — Wed, 13 May 2026 06:52:42 GMT

Created page with "== Citation == Premaraj, N., Huysmans, P., Ploum, M., Schijns, L., Peters, P.J., López-Iglesias, C., Ravelli, R.B. and Knoops, K. 2026. An experimental platform for exploring dual-jet vitrification mechanisms in cryo-EM sample preparation. Methods in Microscopy. 0 (2026). == Abstract == Jet vitrification harnesses the exceptionally high cooling potential of rapidly moving liquid cryogens to achieve ultrafast sample freezing with superior cooling efficiency. Building..."

New page

== Citation ==

Premaraj, N., Huysmans, P., Ploum, M., Schijns, L., Peters, P.J., López-Iglesias, C., Ravelli, R.B. and Knoops, K. 2026. An experimental platform for exploring dual-jet vitrification mechanisms in cryo-EM sample preparation. Methods in Microscopy. 0 (2026).

== Abstract ==

Jet vitrification harnesses the exceptionally high cooling potential of rapidly moving liquid cryogens to achieve ultrafast sample freezing with superior cooling efficiency. Building on earlier work, this study provides a comprehensive investigation into the physical and technical principles underlying dual-jet vitrification. Developing ultra-rapid freezing methods that reliably produce vitreous cells, supports thicker samples, and integrate seamlessly into cryo-electron tomography (cryo-ET) workflows do represent important steps forward in the field. Here, we introduce an experimental platform designed to study the dynamics and efficacy of liquid-ethane jet vitrification at −180 °C. The system integrates multiple modules enabling controlled examination of the vitrification process at microsecond timescales, with a focus on quantifying temperature transitions, identifying optimal freezing parameters, and delineating the limitations of dual-jet configurations. The compact design allows operation within a standard biosafety cabinet and supports various sample holders compatible with biological specimens. Furthermore, we demonstrate how this setup can be directly incorporated into existing cryo-ET workflows using EM grids pre-mounted in AutoGrids. Overall, this work establishes a versatile platform for dissecting the mechanisms of ethane jet vitrification and provides methodological insights that support the preparation of thicker, well-preserved biological samples for in situ structural analysis.

== Keywords ==

== Links ==

https://www.degruyterbrill.com/document/doi/10.1515/mim-2025-0035/html

== Related software ==

== Related methods ==

== Comments ==

Main Page

WikiSysop — Wed, 13 May 2026 06:52:09 GMT

Sample preparation

← Older revision		Revision as of 06:52, 13 May 2026
(One intermediate revision by the same user not shown)
Line 1,166:		Line 1,166:
	\| [[2026Eluru_MISO]]		\| [[2026Eluru_MISO]]
	\| MISO: microfluidic protein isolation from a single cell colony		\| MISO: microfluidic protein isolation from a single cell colony
			\|-

			\| Paper
			\| [[2026Premaraj_DualJet]]
			\| Dual jet vitrification
	\|-		\|-

Line 7,479:		Line 7,484:
	\| [[2016Hattne_Correction]]		\| [[2016Hattne_Correction]]
	\| Image correction		\| Image correction
	\|}		\|-

	\| Paper		\| Paper
	\| [[2026Thibodeaux_MR]]		\| [[2026Thibodeaux_MR]]
	\| Fast molecular replacement algorithm for MicroED		\| Fast molecular replacement algorithm for MicroED

	\|}		\|}

2026Thibodeaux MR

WikiSysop — Wed, 13 May 2026 05:45:10 GMT

Created page with "== Citation == Thibodeaux, A., Bu, G., Edwards, L.C. and Rova Danelius, E. 2026. High-throughput automated molecular replacement for small-molecule MicroED data. IUCrJ. 13, 3 (2026). == Abstract == Interest in electron diffraction (ED) for structural characterization of both proteins and small molecules has grown significantly over the last decade. While ab initio phasing methods remain the gold standard for ED data from smallmolecule samples, radiation beam damage du..."

New page

== Citation ==

Thibodeaux, A., Bu, G., Edwards, L.C. and Rova Danelius, E. 2026. High-throughput automated molecular replacement for small-molecule MicroED data. IUCrJ. 13, 3 (2026).

== Abstract ==

Interest in electron diffraction (ED) for structural characterization of both
proteins and small molecules has grown significantly over the last decade. While
ab initio phasing methods remain the gold standard for ED data from smallmolecule
samples, radiation beam damage during data collection and poor
crystallinity of the nanocrystalline sample can make this method unfeasible –
particularly for challenging molecules that exhibit conformational flexibility.
Molecular replacement (MR) is the most commonly used phasing method for
protein ED data and can circumnavigate issues related to diminished data
quality. However, its application to small molecules has been limited due to the
lack of methods for generating optimal trial conformations. Herein, a high-throughput
automated molecular replacement workflow has been developed to
solve a novel ED structure of corilagin, a macrocyclic gallotannin with pharmaceutical
relevance, which could not be solved with ab initio phasing. The
method was validated against three similar macrocycles with known structures
(paritaprevir-, paritaprevir- and grazoprevir) at varying data resolution limits
(1.0, 1.2, 1.4, 1.5, 1.6, 1.8 and 2.0 A ˚ ). At all these resolutions for all three
structures, the developed workflow was successful and produced solutions with
R factors and RMSD values within acceptable limits of the ab initio solved
structure.

== Keywords ==

== Links ==

https://journals.iucr.org/paper?vq5007

== Related software ==

== Related methods ==

== Comments ==

Main Page

WikiSysop — Wed, 13 May 2026 05:44:26 GMT

Data Processing

← Older revision		Revision as of 05:44, 13 May 2026
Line 7,479:		Line 7,479:
	\| [[2016Hattne_Correction]]		\| [[2016Hattne_Correction]]
	\| Image correction		\| Image correction
			\|}

			\| Paper
			\| [[2026Thibodeaux_MR]]
			\| Fast molecular replacement algorithm for MicroED
	\|}		\|}

2025Hu Denoising

WikiSysop — Mon, 04 May 2026 08:36:18 GMT

Created page with "== Citation == Hu, B., Zhang, D.-X., Liu, S.-Q., Xie, X.-L., Zhou, X.-H., Li, H.-J., Zheng, Q.-B., Zhang, F., Hou, Z.-G. and Xia, N.-S. 2025. Particle Restoration: A Novel Image Processing Framework for Improving Real Cryo-EM Image Quality in Single Particle Analysis. IEEE Trans. on Computational Biology and Bioinformatics. (2025). == Abstract == Cryo-electron microscopy single particle analysis (cryo-EM SPA) is the most powerful technique for biomacromolecule structu..."

New page

== Citation ==

Hu, B., Zhang, D.-X., Liu, S.-Q., Xie, X.-L., Zhou, X.-H., Li, H.-J., Zheng, Q.-B., Zhang, F., Hou, Z.-G. and Xia, N.-S. 2025. Particle Restoration: A Novel Image Processing Framework for Improving Real Cryo-EM Image Quality in Single Particle Analysis. IEEE Trans. on Computational Biology and Bioinformatics. (2025).

== Abstract ==

Cryo-electron microscopy single particle analysis (cryo-EM SPA) is the most powerful technique for biomacromolecule structure determination. However, many factors such as complicated noise and radiation damage make the quality of cryo-EM images extremely poor, where high-frequency structure details are submerged, limiting the application of deep learning and suppressing the resolution of reconstruction. Thus, image restoration is of vital importance. Some related works explore micrograph restoration, but the particles in restored micrographs are still of poor quality. Moreover, the training approach of existing methods uses noisy observations or simulated data as supervision, leading to reduced performance on real cryo-EM data. In this paper, we define the task of particle restoration and propose a novel 4-step framework to this end. Labels are created for each particle image and paired data is collected within our framework, compensating for the absence of ground truth. A deep neural network with encoder-decoder architecture is designed to learn the mapping from degraded particles to high-quality ones, while other networks can also be employed as a plug-and-play module. Three datasets are constructed from real cryo-EM data and extensive experiments are carried out. Both quantitative metrics and qualitative visualization indicate that our framework is effective for cryo-EM particle restoration. It becomes easier to extract particle features after restoration, aiding in SPA and the effective application of deep learning on cryo-EM images. The downstream task experiments of cryo-EM SPA are also conducted, showing that the proposed framework has the potential to improve cryo-EM SPA performance.

== Keywords ==

== Links ==

https://ieeexplore.ieee.org/abstract/document/11175693

== Related software ==

== Related methods ==

== Comments ==

Main Page

WikiSysop — Mon, 04 May 2026 08:35:44 GMT

2D Preprocessing

← Older revision		Revision as of 08:35, 4 May 2026
Line 2,023:		Line 2,023:
	\| [[2024Li_Subtraction]]		\| [[2024Li_Subtraction]]
	\| Subtraction of membrane signal in SPA		\| Subtraction of membrane signal in SPA
			\|-

			\| Paper
			\| [[2025Hu_Denoising]]
			\| A neural network to denoise micrographs
	\|-		\|-

2025Costa PERC

WikiSysop — Thu, 30 Apr 2026 06:51:09 GMT

Created page with "== Citation == Costa-Gomes, B., Greer, J., Juraschko, N., Parkhurst, J., Mirecka, J., Famili, M., Rangel-Smith, C., Strickson, O., Lowe, A., Basham, M. and others 2025. PERC: a suite of software tools for the curation of cryoEM data with application to simulation, modeling and machine learning. Structural Biology and Crystallization Communications. 81, 10 (2025). == Abstract == Ease of access to data, tools and models expedites scientific research. In structural biolo..."

New page

== Citation ==

Costa-Gomes, B., Greer, J., Juraschko, N., Parkhurst, J., Mirecka, J., Famili, M., Rangel-Smith, C., Strickson, O., Lowe, A., Basham, M. and others 2025. PERC: a suite of software tools for the curation of cryoEM data with application to simulation, modeling and machine learning. Structural Biology and Crystallization Communications. 81, 10 (2025).

== Abstract ==

Ease of access to data, tools and models expedites scientific research. In
structural biology there are now numerous open repositories of experimental
and simulated data sets. Being able to easily access and utilize these is crucial
to allow researchers to make optimal use of their research effort. The tools
presented here are useful for collating existing public cryoEM data sets and/or
creating new synthetic cryoEM data sets to aid the development of novel data
processing and interpretation algorithms. In recent years, structural biology has
seen the development of a multitude of machine-learning-based algorithms to
aid numerous steps in the processing and reconstruction of experimental data
sets and the use of these approaches has become widespread. Developing such
techniques in structural biology requires access to large data sets, which can be
cumbersome to curate and unwieldy to make use of. In this paper, we present a
suite of Python software packages, which we collectively refer to as PERC
(profet, EMPIARreader and CAKED). These are designed to reduce the burden
which data curation places upon structural biology research. The protein
structure fetcher (profet) package allows users to conveniently download and
cleave sequences or structures from the Protein Data Bank or AlphaFold
databases. EMPIARreader allows lazy loading of Electron Microscopy Public
Image Archive data sets in a machine-learning-compatible structure. The Class
Aggregator for Key Electron-microscopy Data (CAKED) package is designed
to seamlessly facilitate the training of machine-learning models on electron
microscopy data, including electron-cryo-microscopy-specific data augmentation
and labeling. These packages may be utilized independently or as building
blocks in workflows. All are available in open-source repositories and designed
to be easily extensible to facilitate more advanced workflows if required.

== Keywords ==

== Links ==

https://journals.iucr.org/f/issues/2025/10/00/ir5046/index.html

== Related software ==

== Related methods ==

== Comments ==

Main Page

WikiSysop — Thu, 30 Apr 2026 06:50:12 GMT

Software

← Older revision		Revision as of 06:50, 30 April 2026
Line 5,874:		Line 5,874:
	\| [[2025Chen_CryoCRAB]]		\| [[2025Chen_CryoCRAB]]
	\| CryoCRAB: a large database of curated micrographs		\| CryoCRAB: a large database of curated micrographs
			\|-

			\| Paper
			\| [[2025Costa_PERC]]
			\| PERC: a library to interact with CryoEM databases
	\|-		\|-

2025Giri Sharpening

WikiSysop — Thu, 30 Apr 2026 06:42:34 GMT

Created page with "== Citation == Giri, N., Chen, X., Wang, L. and Cheng, J. 2025. A labeled dataset for AI-based cryo-EM map enhancement. Computational and Structural Biotechnology Journal. 27, (2025), 2843–2850. == Abstract == Cryogenic electron microscopy (cryo-EM) has transformed structural biology by enabling near atomic resolution imaging of macromolecular complexes. However, cryo-EM density maps suffer from intrinsic noise arising from structural sources, shot noise, and digita..."

New page

== Citation ==

Giri, N., Chen, X., Wang, L. and Cheng, J. 2025. A labeled dataset for AI-based cryo-EM map enhancement. Computational and Structural Biotechnology Journal. 27, (2025), 2843–2850.

== Abstract ==

Cryogenic electron microscopy (cryo-EM) has transformed structural biology by enabling near atomic resolution
imaging of macromolecular complexes. However, cryo-EM density maps suffer from intrinsic noise arising from
structural sources, shot noise, and digital recording, which complicates accurate model building. While various
methods for denoising cryo-EM density maps exist, there is a lack of standardized datasets for benchmarking
artificial intelligence (AI) approaches. Here, we present an open-source dataset for cryo-EM density map denoising
comprising 650 high-resolution (1-4 Å) experimental maps paired with three types of generated label maps:
regression maps capturing idealized density distributions, binary classification maps distinguishing structural
elements from background, and atom-type classification maps. Each map is standardized to 1 Å voxel size and
validated through Fourier Shell Correlation analysis, demonstrating substantial resolution improvements in label
maps compared to experimental maps. This resource bridges the gap between structural biology and artificial
intelligence communities, allowing researchers to develop and benchmark innovative methods for enhancing
cryo-EM density maps.

== Keywords ==

== Links ==

https://www.sciencedirect.com/science/article/pii/S2001037025002570

== Related software ==

== Related methods ==

== Comments ==

Main Page

WikiSysop — Thu, 30 Apr 2026 06:41:48 GMT

Databases

← Older revision		Revision as of 06:41, 30 April 2026
Line 8,031:		Line 8,031:
	\| [[2024wwPDB_EMDB]]		\| [[2024wwPDB_EMDB]]
	\| Review of EMDB		\| Review of EMDB
			\|-

			\| Paper
			\| [[2025Giri_Sharpening]]
			\| A labeled dataset for map enhancement
	\|-		\|-

2026Lin HDX

WikiSysop — Thu, 30 Apr 2026 05:51:34 GMT

Created page with "== Citation == Lin, X. and Cheng, Y. 2026. Making sense of invisible densities in single-particle cryo-EM. IUCrJ. 13, 3 (2026). == Abstract == In the era of single-particle cryogenic electron microscopy (cryo-EM) and AIdriven protein structure prediction, obtaining high-resolution protein structures, either experimentally or computationally, has become increasingly routine. Yet studying and understanding protein dynamics remains challenging. In singleparticle cryo-EM,..."

New page

== Citation ==

Lin, X. and Cheng, Y. 2026. Making sense of invisible densities in single-particle cryo-EM. IUCrJ. 13, 3 (2026).

== Abstract ==

In the era of single-particle cryogenic electron microscopy (cryo-EM) and AIdriven
protein structure prediction, obtaining high-resolution protein structures,
either experimentally or computationally, has become increasingly routine. Yet
studying and understanding protein dynamics remains challenging. In singleparticle
cryo-EM, protein dynamics are most obviously manifested as poor local
resolution or disappearing densities in specific regions of a reconstruction. No
method is yet available to computationally generate conformational ensembles
that fully deconvolute these experimental observations. When dynamics are key
to understanding protein function, it is clear to us that introducing new
experimental approaches is necessary to close this gap and make sense of
invisible densities in single-particle cryo-EM.

== Keywords ==

== Links ==

https://journals.iucr.org/m/issues/2026/03/00/jt5084/index.html

== Related software ==

== Related methods ==

== Comments ==

Main Page

WikiSysop — Thu, 30 Apr 2026 05:50:37 GMT

Books and reviews

← Older revision		Revision as of 05:50, 30 April 2026
Line 5,628:		Line 5,628:
	\| [[2026Li_Atomic]]		\| [[2026Li_Atomic]]
	\| Review on deep learning algorithms for map sharpening and atomic modelling		\| Review on deep learning algorithms for map sharpening and atomic modelling
			\|-

			\| Paper
			\| [[2026Lin_HDX]]
			\| Comment on the complementarity of HDX-MS studies for heterogeneous particles
	\|-		\|-

2026Chen MPM

WikiSysop — Tue, 28 Apr 2026 06:41:51 GMT

Created page with "== Citation == Chen, J., Leung, V.C., Wang, R., Bubeck, D. and Dragotti, P.L. 2026. Masked Projection Modelling for Sparse-view cryo-EM Reconstruction. ICASSP 2026-2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2026), 11567–11571. == Abstract == Resolving conformational heterogeneity in cryo-electron microscopy (cryo-EM) remains challenging, especially for rare states. Standard reconstruction methods, reliant on abundant simi..."

New page

== Citation ==

Chen, J., Leung, V.C., Wang, R., Bubeck, D. and Dragotti, P.L. 2026. Masked Projection Modelling for Sparse-view cryo-EM Reconstruction. ICASSP 2026-2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2026), 11567–11571.

== Abstract ==

Resolving conformational heterogeneity in cryo-electron microscopy
(cryo-EM) remains challenging, especially for rare
states. Standard reconstruction methods, reliant on abundant
similar particles, bias results toward dominant conformations.
To address this, we present an end-to-end pipeline
that separates orientation estimation from 3D reconstruction.
Our approach starts with self-supervised Masked Projection
Modelling (MPM), which pretrains an encoder to capture
geometric relationships across projections without known
orientations. This encoder drives a supervised Probabilistic
Orientation Estimation (POE) framework for initial orientation
inference. At testing stage on real data, a high-resolution
3D volume is estimated while orientations are further refined
within a Maximum-Likelihood Expectation-Maximization
(ML-EM) algorithm that utilizes a continuous Implicit Neural
Representation (INR), requiring no pretraining data. On
real cryo-EM data, our method achieves high-resolution reconstruction
from a severely reduced number of particle projections,
outperforming traditional methods in low-particlecount
scenarios.

== Keywords ==

== Links ==

https://ieeexplore.ieee.org/abstract/document/11463226/

== Related software ==

== Related methods ==

== Comments ==

Main Page

WikiSysop — Tue, 28 Apr 2026 06:41:07 GMT

3D Reconstruction

← Older revision		Revision as of 06:41, 28 April 2026
Line 2,964:		Line 2,964:
	\| [[2026Barchet_NonUniform]]		\| [[2026Barchet_NonUniform]]
	\| Explicit correction of severely non-uniform distributions in SPA		\| Explicit correction of severely non-uniform distributions in SPA
			\|-

			\| Paper
			\| [[2026Chen_MPM]]
			\| Masked projection modelling for 3D reconstruction
	\|-		\|-