aiqbench analysis
ARCHER introduces a novel machine learning approach using amortized contrastive classification to enable zero-shot pose estimation in cryo-electron microscopy across diverse protein structures. By leveraging Fourier-space information mechanics, the model generalizes without retraining per specimen while preserving critical downstream conformational signals for reconstructing mobile domains. This advancement demonstrates that specimen-dependent inference can be decoupled from structural specifics, offering a scalable solution for high-resolution 3D reconstruction in structural biology.
ARCHER: Amortized cross-specimen pose estimation for cryo-electron microscopy
arXiv:2608.22029v1 Announce Type: new Abstract: Single-particle cryo-electron microscopy (cryo-EM) pose estimation is traditionally solved anew for each dataset, where iterative refinement is done from scratch while the estimator learns to store the molecule in its weights. In this work, we show that pose inference is a generalizable, specimen-agnostic operation when conditioned explicitly on a reference volume. We introduce ARCHER, an amortized contrastive classifier that models the pose posterior over a discrete rotation grid. Trained across a variety of protein structures, it operates zero-shot without retraining per structure. This transferability is grounded in Fourier-space information mechanics, where all specimen dependence is captured by the reference structure's power spectrum and spatial extent. ARCHER achieves a median angular error of 5.0{\deg} on 100 held-out test structures and 2.5{\deg} on experimental particles, matching dedicated estimators within 0.16 {\AA} in 3D reconstruction. Crucially, downstream conformational signal is preserved. The leading conformational coordinate correlates at 0.97 with deposited benchmarks, faithfully reconstructing free-energy basins and mobile domains. These results overall demonstrate that cryo-EM pose estimation can be generalized across different structures.
Abstract:Single-particle cryo-electron microscopy (cryo-EM) pose estimation is traditionally solved anew for each dataset, where iterative refinement is done from scratch while the estimator learns to store the molecule in its weights. In this work, we show that pose inference is a generalizable, specimen-agnostic operation when conditioned explicitly on a reference volume. We introduce ARCHER, an amortized contrastive classifier that models the pose posterior over a discrete rotation grid. Trained across a variety of protein structures, it operates zero-shot without retraining per structure. This transferability is grounded in Fourier-space information mechanics, where all specimen dependence is captured by the reference structure's power spectrum and spatial extent. ARCHER achieves a median angular error of 5.0° on 100 held-out test structures and 2.5° on experimental particles, matching dedicated estimators within 0.16 Å in 3D reconstruction. Crucially, downstream conformational signal is preserved. The leading conformational coordinate correlates at 0.97 with deposited benchmarks, faithfully reconstructing free-energy basins and mobile domains. These results overall demonstrate that cryo-EM pose estimation can be generalized across different structures.
Machine Learning (cs.LG); Mathematical Physics (math-ph); Biomolecules (q-bio.BM); Quantitative Methods (q-bio.QM)
arXiv:2608.22029 [cs.LG]
(or arXiv:2608.22029v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2608.22029 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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