跨样品通用的冷冻电镜构象估计新方法，无需重新训练即可适用新蛋白
ARCHER: Amortized cross-specimen pose estimation for cryo-electron microscopy
- 基于参考体积显式建模旋转后验，用对比学习实现可迁移姿态推断
- 在100个测试结构上中位角误差5.0°，实验粒子达2.5°，重建精度媲美专用模型
- 零样本迁移，保留构象信号，适合快速分析新蛋白结构的科研人员
单颗粒冷冻电镜姿态估计传统上需为每组数据从头迭代优化，且估计算法通过权重存储分子信息。本文表明，当以参考体积为条件时，姿态推断具有普遍性与样品无关性。我们提出ARCHER，一种基于对比学习的压缩化分类器，对离散旋转网格建模姿态后验。该模型在多种蛋白质结构上训练，可零样本适配新结构，无需重训。其泛化能力源于傅里叶空间中的信息力学机制：所有样品依赖性仅由参考结构的功率谱和空间范围捕获。ARCHER在100个保留测试结构上达到5.0°中位角误差，在实验粒子上达2.5°，3D重构精度与专用估计算法相差仅0.16 Å。关键的是，下游构象信号被完整保留：主要构象坐标与已发表基准的相关系数达0.97，能准确重建自由能盆地与柔性区域。结果表明，冷冻电镜姿态估计可实现跨结构泛化。
原文摘要 · Abstract (English)
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.
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