The paper introduces EMber, a family of vision transformers trained on over 50 million cryo‑EM particle images and simulated projections, to learn a shared latent representation for multiple inverse problems in cryo‑EM. By employing self‑distillation with global and masked patch objectives, EMber captures spatially localized structural differences and improves performance on conformational and compositional discrimination by roughly 15 percentage points compared to existing methods. The authors demonstrate that these representations enable accurate estimation of conformational populations and yield high‑quality reconstructions in fewer training epochs.
arXiv:2509.05541v2 Announce Type: replace-cross Abstract: Cryo-electron microscopy (Cryo-EM) enables high-resolution imaging of biomolecules, but structural heterogeneity remains a major challenge in...
The paper introduces CARNIVAL, a model for protein annotation in cryo-electron tomography (cryo-ET) volumes that leverages simulated data and a forward model to generate domain‑specific augmented paired views for self‑supervised training. By incorporating simulation‑derived protein positions and identities into the architecture and loss function, the model localises semantic information at protein locations. CARNIVAL is evaluated on real tomograms without finetuning and outperforms a state‑of‑the‑art contrastive model that lacks forward‑model paired views or privileged information.
Fold'EM is an inference-time framework that directly combines protein generative model priors with cryo‑EM particle images to recover atomic structures, bypassing the traditional density reconstruction step. It can determine structures from a small number of particles, jointly infer orientations in an ab‑initio setting, and resolve distinct conformational states in heterogeneous samples without separate map reconstruction. The method demonstrates accurate atomic models on both synthetic and experimental datasets, showing promise for low‑sample and low‑population conformational analysis.
arXiv:2606. 00955v1 Announce Type: new Abstract: Despite the growing availability of cryo-electron microscopy (cryo-EM) density maps, effectively leveraging them for protein representation remains challenging.
arXiv:2606. 10255v1 Announce Type: cross Abstract: Cryo-electron tomography (cryoET) has emerged as a powerful tool in structural and cellular biology by enabling direct visualization of macromolecular structures within intact cells, thereby linking molecular architecture to cellular organization in a native context.
The paper presents CryoCue, a framework that leverages hetero component information to enhance protein reconstruction from cryo-electron microscopy maps. An anchor‑supervised detector learns representations for five hetero classes, and multiscale hetero features guide backbone localization while predicted hetero candidates inform structure refinement. Experiments demonstrate that CryoCue improves backbone localization near hetero components and yields more accurate protein structures.
The paper introduces Scalable Bayesian Optimization of Composite Functions (SBOCF) for efficiently estimating physical parameters from scientific images, specifically targeting electron microscopy PACBED patterns. SBOCF leverages the composite structure of the image-matching objective, reducing modeled outputs from 24,649 to 11 by using patch-level summaries and correction terms. With only 50 simulator evaluations, SBOCF outperformed standard Bayesian optimization, achieving up to 290× lower median SSE on synthetic SrTiO3 benchmarks and producing accurate parameter estimates on experimental data without task-specific pretraining.
arXiv:2608.28838v1 Announce Type: cross Abstract: Cryo-electron microscopy (cryo-EM) determines the structures of proteins and macromolecular assemblies at near-atomic resolution, and the final 3D re...
Atelier is a self‑supervised framework that uses a transformer‑based hypernetwork to generate implicit neural representations (INRs) for cryo‑EM maps, enabling efficient, scale‑agnostic, coordinate‑conditioned feature extraction. Trained on 5,439 maps from the Electron Microscopy Data Bank, the pretrained INR provides continuous local feature fields that can be used as auxiliary channels for a 3D nested U‑Net, improving voxel‑level property prediction across eight tasks compared to a volume‑only baseline. The approach demonstrates that amortized INRs can serve as a geometry‑aware primitive for large‑scale cryo‑EM analysis.
arXiv:2606. 28431v1 Announce Type: cross Abstract: Fluorescence microscopy images are degraded by noise and diffraction-induced blur, which compromise structural fidelity and limit quantitative analysis.
arXiv:2606. 31332v1 Announce Type: new Abstract: Protein automodeling from cryo-EM density maps faces unique challenges in enforcing physicochemical validity and managing conformational heterogeneity.
Existing 3D generative approaches are typically developed for individual imaging modalities, requiring substantial modality-specific data and limiting their ability to exploit shared structure across...