发表机构
Biohub; University of Chicago（Biohub; 芝加哥大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
Atelier提出基于Transformer超网络的自监督框架，摊销冷冻电镜图谱的隐式神经表示拟合，生成连续局部特征场，提升八个体素级属性预测任务性能。
AI 中文摘要
冷冻电镜图谱解读需要具有空间局部性、跨样本一致性以及跨空间尺度信息量的特征。大多数用于图谱注释的深度学习方法从固定体素网格中提取特征。然而，隐式神经表示（INRs）能够将体积数据建模为与尺度无关、坐标条件化的函数。因此，INRs对于冷冻电镜具有吸引力，但为每个图谱单独拟合一个INR对于大规模特征提取而言过于昂贵，并且产生的表示在样本之间不对齐。我们引入了Atelier，一个自监督框架，它摊销了重建冷冻电镜图谱的INR拟合。Atelier在5,439个电子显微镜数据银行图谱上进行了预训练，是一个基于Transformer的超网络，能够为广泛的蛋白质结构（包括大型多亚基组装体）生成高保真重建。除了重建之外，由预训练Transformer生成的INR通过其任何空间查询点的中间激活暴露了一个连续的局部特征场，这是体素网格和补丁分词器架构自然不具备的特性。当用作从头训练的3D嵌套U-Net注释头的辅助通道时，这些坐标条件化特征在八个体素级属性预测任务上相对于仅使用体积的基线提高了性能。我们的结果表明，摊销隐式神经表示是冷冻电镜数据几何感知分析的有效原语。
英文摘要
CryoEM map interpretation requires features that are spatially localized, consistent across samples, and informative across spatial scales. Most deep learning methods for map annotation extract features from fixed voxel grids. However, implicit neural representations (INRs) are able to model volumetric data as scale-agnostic, coordinate-conditioned functions. INRs are therefore attractive for cryoEM, but fitting a separate INR for each map is too expensive for large-scale feature extraction and produces representations that are not aligned across samples. We introduce Atelier, a self-supervised framework that amortizes INR fitting for reconstructed cryoEM maps. Pretrained on 5,439 Electron Microscopy Data Bank maps, Atelier is a transformer-based hypernetwork that generates high-fidelity reconstructions across a wide range of protein structures, including large multi-subunit assemblies. Beyond reconstruction, the INR generated by the pretrained transformer exposes a continuous, local feature field through its intermediate activations at any spatial query point, a property that voxel grid and patch-tokenizer architectures do not naturally provide. Used as auxiliary channels to a 3D nested U-Net annotation head trained from scratch, these coordinate-conditioned features improve performance on eight voxel-level property prediction tasks over a volume-only baseline. Our results demonstrate that amortized implicit neural representations are an effective primitive for geometry-aware analysis of cryoEM data.
Comments22 pages, 7 figures, 5 tables
