发表机构
Institute of Science and Technology Austria; University of Zurich; Frankfurt Institute for Advanced Studies; Princeton University; Broad Institute of MIT and Harvard（奥地利科学技术研究所; 苏黎世大学; 法兰克福高等研究院; 普林斯顿大学; 麻省理工学院和哈佛大学博德研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
Fold'EM提出一种推理时框架，将蛋白质生成模型先验与冷冻电镜颗粒图像直接结合，绕过密度重建和模型拟合，从少量颗粒中推断原子结构，并能在异质样本中解析构象状态。
AI 中文摘要
单颗粒冷冻电子显微镜（cryo-EM）已成为生物分子结构测定中广泛采用的技术。传统的冷冻电镜计算流程首先将许多颗粒图像合并以重建静电势（ESP）图，然后将原子模型拟合到恢复的图谱上。密度重建具有较高的样本复杂度，需要大量颗粒图像，这使得结构测定成本高且通量低，尤其是对于异质样本。反过来，随着重建图谱分辨率的下降，下游的原子模型构建变得越来越困难。蛋白质结构预测模型提供了基于序列的强先验知识，而实验引导的方法可以利用这些先验来恢复与实验测量一致的结构。然而，在冷冻电镜中，这些先验通常仅在密度重建后的原子模型拟合阶段才被整合。我们引入了Fold'EM，这是一种推理时框架，它将蛋白质生成模型的先验直接与冷冻电镜颗粒图像相结合，以从少量单颗粒图像中确定原子模型，绕过了中间密度重建和针对重建图谱的下游模型构建。在合成和实验冷冻电镜数据集上，Fold'EM在已知颗粒取向以及从头设置（其中取向与结构联合推断）中均能恢复准确的原子结构。在异质数据集中，Fold'EM进一步从混合颗粒群体中解析出不同的构象状态，而无需分别重建密度图并为每种状态构建原子模型。我们相信这些结果为低样本量下的结构测定以及直接从冷冻电镜颗粒表征低群体构象状态开辟了新途径。
英文摘要
Single-particle cryo-electron microscopy (cryo-EM) has become a widely adopted technique for biomolecular structure determination. The conventional cryo-EM computational pipeline first combines many particle images to reconstruct an electrostatic potential (ESP) map and then fits an atomic model to the recovered map. Density reconstruction has high sample complexity, requiring large numbers of particle images and making structure determination high-cost and low-throughput, particularly for heterogeneous samples. Downstream atomic model building, in turn, becomes increasingly difficult as the resolution of the reconstructed map deteriorates. Protein structure prediction models provide strong sequence-derived priors on atomic structure, and experiment-guided approaches can use these priors to recover structures consistent with experimental measurements. Yet, in cryo-EM, such priors are typically integrated only after density reconstruction during atomic model fitting. We introduce Fold'EM, an inference-time framework that combines priors from protein generative models directly with cryo-EM particle images to determine atomic models from a small number of single particle images, bypassing both intermediate density reconstruction and downstream model building against the reconstructed map. Across synthetic and experimental cryo-EM datasets, Fold'EM recovers accurate atomic structures both with known particle orientations and in an ab-initio setting where orientations are inferred jointly with structure. In heterogeneous datasets, Fold'EM further resolves distinct conformational states from mixed particle populations without separately reconstructing a density map and building an atomic model for each state. We believe these results open new avenues for structure determination in the low-sample regime and for characterizing low-population conformational states directly from cryo-EM particles.
