Tom Burnley

The ability of cryo-electron microscopy (cryo-EM) to interrogate the atomic structure and motion of biomolecules has motivated the development of a wide range of algorithms for heterogeneity analysis. However, evaluating and comparing these methods remains challenging because ground-truth structures are generally unknown for experimental samples. Here, we introduce the 2026 Community-Wide Assessment of Heterogeneous Reconstruction Algorithms (CAHRA), a community-wide challenge centered on three benchmark datasets that probe distinct tasks in heterogeneity analysis. These include (1) compositional heterogeneity arising from mixtures of distinct protein complexes, (2) continuous conformational variability and atomic modeling, and (3) entanglement between molecular conformation and image pose. We describe the design, construction, and validation of these datasets, as well as their use in the recently completed CAHRA Challenge.

Here, we describe the first ten years of the CCP-EM Spring Symposium, an annual conference to bring together the cryogenic-sample electron microscopy (cryoEM) community to present and discuss the latest methodological advances and applications. We quantify the growth of the event and provide a detailed breakdown of the demographics of the tenth edition.

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.

Ease of access to data, tools and models expedites scientific research. In structural biology there are now numerous open repositories of experimental and simulated datasets. Being able to easily access and utilise these is crucial for allowing researchers to make optimal use of their research effort. The tools presented here are useful for collating existing public cryoEM datasets and/or creating new synthetic cryoEM datasets 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 for aiding numerous steps in the processing and reconstruction of experimental datasets and the use of these approaches has become widespread. Developing such techniques in structural biology requires access to large datasets 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 datasets 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 labelling. These packages may be utilised 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.

Cell-cell communication underlies all emergent properties of the brain, including cognition, learning and memory. The physical basis for these communications is the synapse, a multi-component structure requiring coordinated interactions between diverse cell types. However, many aspects of three-dimensional (3D) synaptic organization remain poorly understood. Here, we developed an approach, seamless in situ trimming and milling (SISTM), to reliably fabricate sufficiently thin lamellae for mapping of the 3D nanoarchitecture of synapses in mouse, monkey and human brain tissue under near-native conditions via cryo-electron tomography (cryo-ET). We validated SISTM in a mouse model of Huntington’s disease, demonstrating distinct 3D alterations to synaptic vesicles and mitochondria. By successfully applying SISTM to macaque brain, we described the 3D architecture of a tripartite synapse within the cortex. Subtomogram averaging (STA) enabled spatial mapping of astrocyte-neuron contacts within the tripartite synapse, revealing neurexin-neuroligin complexes as potential constituents that tether the two cell types. Finally, we showed that the defining features of synaptic nanoarchitecture were conserved across species and evident in human brain tissue obtained postmortem. Combining SISTM with cryo-ET and STA is a starting point for a new understanding of brain organization, disease-induced structural alterations and the development of rational, structure-guided therapeutics.

With the advent of next-generation modelling methods, such as AlphaFold2, structural biologists are increasingly using predicted structures to obtain structure solutions via molecular replacement (MR) or model fitting in single-particle cryogenic sample electron microscopy (cryoEM). Differences between the domain–domain orientations represented in a predicted model and a crystal structure are often a key limitation when using predicted models. Slice'N'Dice is a software package designed to address this issue by first slicing models into distinct structural units and then automatically placing the slices using either Phaser, MOLREP or PowerFit. The slicing step can use the AlphaFold predicted aligned error (PAE) or can operate via a variety of C^α-atom-based clustering algorithms, extending the applicability to structures of any origin. The number of splits can either be selected by the user or determined automatically. Slice'N'Dice is available for both MR and automated map fitting in the CCP4 and CCP-EM software suites.