A meta-algorithm for ab initio reconstruction of complex mixtures in cryo-EM

Alkin Kaz; Arda Kaz; Ellen D. Zhong

q-bio.BMAug 26, 2026

A meta-algorithm for ab initio reconstruction of complex mixtures in cryo-EM

Authors: Alkin Kaz, Arda Kaz, Ellen D. Zhong

Organizations: Princeton University · University of California, Berkeley

Abstract

We describe a systematic approach for spawning and aggregating multi-class cryo-EM reconstruction jobs. This approach formalizes standard ad hoc strategies of iterative classification and filtering typically used by practitioners to sort impure, heterogeneous samples. To our knowledge, this is the first method that can successfully perform ab initio reconstruction on datasets containing dozens of distinct species. We obtain 97% accuracy on ab initio reconstruction of a 45-class subset of Tomotwin-100, 75% accuracy on the full Tomotwin-100 dataset, and demonstrate recovery of ribosomal assembly states from an unfiltered experimental cryo-EM dataset. Our approach's capability scales with compute and lays the foundation for automated cryo-EM workflows in modern experimental settings.

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Aug 28, 2026, q-bio.BMJ/K move · Enter open · S save

Reconstruction-Aware Cryo-EM Particle Picking

Riku Itsuji, Yuanhao Wang, Xingjian Li, Seonghui Min+2

Keio University, Yokohama, Japan · Keio AI Research Center, Yokohama, Japan · Carnegie Mellon University, Pittsburgh, PA, USA

Cryo-Electron Microscopy 3D Reconstruction

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Cryo-electron microscopy (cryo-EM) determines the structures of proteins and macromolecular assemblies at near-atomic resolution, and the final 3D reconstruction depends on extracting a clean particle stack from noisy micrographs. This extraction decomposes into three sub-tasks, namely particle picking, contamination removal, and 2D class selection. Each of them, however, is trained and evaluated in isolation, and none is optimized for the reconstruction. We instead integrate the three sub-tasks into a single pipeline posed against downstream reconstruction quality. We instantiate the pipeline with a state-of-the-art component for each sub-task, CryoTransformer picking permissively, MicrographCleaner masking contamination, and CryoSift selecting 2D classes by a continuous quality score, and close the loop with a fine-tuning step that returns the surviving particles to the picker. The pipeline achieves a better 3D resolution than every picker we compare. We also show that the best 2D F1 is not the best resolution, so particle selection is better treated as one reconstruction-aware pipeline judged by the map it delivers.

Abstract

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