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.
By Jonathan Schwartz, Utz Heinrich Ermel, C. Braxton Owens, Zhuowen Zhao, Ariana Peck, Gus L. W. Hart, Grant J. Jensen, Bridget Carragher, Dari Kimanius
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.
By Dan Luo, Xuan Lin, Peng Zhou, Junwen Zhu, Tengfei Ma, Xiangxiang Zeng, Yiping Liu
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.
By Minzhang Li, Mingrui Li, Weichen Qin, Qihe Chen, Sixian Shen, Yuan Pei, Jiakai Zhang, Jingyi Yu
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.
By Advaith Maddipatla, M\"art-Erik M\"aeots, Marco Pegoraro, Nikolaus Dr\"ager, Roberto Covino, Sanketh Vedula, Martin Pacesa, Alex M. Bronstein
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.
By Mingrui Li, Minzhang Li, Weichen Qin, Yufan Xie, Zhangzhi Xiong, Sixian Shen, Jiakai Zhang, Yuan Pei, Jingyi Yu
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.
By Nithin Chalapathi, Theo Sternlieb, Ethan Han, Sonya M. Hanson, Pilar Cossio, Aditi S. Krishnapriyan, Ritwik Gupta