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.
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.
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. 31332v1 Announce Type: new Abstract: Protein automodeling from cryo-EM density maps faces unique challenges in enforcing physicochemical validity and managing conformational heterogeneity.
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.
CryoAtlas is a large, rigorously curated dataset and unified benchmark for density‑guided atomic model building of protein chains from cryo‑EM maps. It compiles 17,809 map‑model‑sequence entries from EMDB and PDB (up to March 2026), with a 16,657‑entry training split, and defines a 618‑target benchmark that evaluates structural accuracy, map fit, and stereochemical validity. The resource enables systematic comparison of learning‑based methods with traditional tools like Phenix and AlphaFold3, and demonstrates that retraining a CNN component on CryoAtlas improves performance.
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.
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.
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.
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...
arXiv:2607. 28553v1 Announce Type: new Abstract: Predicting the 3D structures of atomic systems is fundamental to advancing material science and drug discovery.
arXiv:2605. 01625v3 Announce Type: replace Abstract: Proteins are inherently multiscale physical systems whose functional properties emerge from coordinated structural organization across multiple spatial resolutions, ranging from atomic interactions to global fold topology.
The paper introduces StructEvo, a structure-aware reinforcement learning framework designed to improve protein directed evolution. By using a delta-structure fusion encoder to approximate mutant structure features and a hierarchical action network aligned with protein structure, the method navigates the vast mutation space more effectively. StructEvo outperforms existing machine learning-assisted directed evolution techniques by 9.2% and 16.3% on two benchmarks and uncovers an experimentally validated epistasis pattern in GFP, underscoring the value of structural guidance.