发表机构
Pritzker School of Molecular Engineering, University of Chicago; Department of Computer Science, Rensselaer Polytechnic Institute（芝加哥大学普利兹克分子工程学院; 伦斯勒理工学院计算机科学系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出ARCHER模型，通过离散旋转网格建模姿态后验，实现冷冻电镜跨结构通用的零样本姿态估计，在测试结构和实验颗粒上均达到高精度，且保留下游构象信号。
AI 中文摘要
单颗粒冷冻电子显微镜（cryo-EM）的姿态估计传统上是针对每个数据集单独求解的，其中迭代优化从头开始进行，同时估计器学习将分子存储在其权重中。在这项工作中，我们表明，当明确以参考体积为条件时，姿态推断是一种可泛化、与样本无关的操作。我们引入了ARCHER，这是一种摊销对比分类器，可对离散旋转网格上的姿态后验进行建模。在多种蛋白质结构上进行训练后，它无需针对每个结构重新训练即可零样本运行。这种可迁移性基于傅里叶空间信息力学，其中所有样本依赖性都由参考结构的功率谱和空间范围捕获。ARCHER在100个保留的测试结构上实现了5.0°的中位角误差，在实验颗粒上实现了2.5°的中位角误差，在3D重建中与专用估计器的误差在0.16 Å以内。关键的是，下游构象信号得以保留，主导构象坐标与已发布基准的相关性为0.97，忠实地重建了自由能谷和移动结构域。这些结果总体表明，冷冻电子显微镜姿态估计可以跨不同结构进行泛化。
英文摘要
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.
Comments28 pages, 15 figures
