Newest papers

Learning the Shared Latent Structure of Cryo-EM Inverse Problems

Nithin Chalapathi, Theo Sternlieb, Ethan Han, Sonya M. Hanson, Pilar Cossio, Aditi S. Krishnapriyan, Ritwik Gupta · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.22523 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

了解生物分子分子功能需要特征三维结构和合规变量,为此,晶体电子显微镜(CRY-EM)是一个强大的工具。CRY-EM产生噪声的二维粒子图像,其中分子结构,粒子姿势和成像条件共同编码。从这些观测中获取生物信息涵盖了反向问题,包括位置估计,异质性分析和重建。这些任务通常是独立解决的,尽管共享蛋白质结构和图像形成,激励在反向问题上共同表示。由于生物意义上的差异可以被空间地定位,我们研究自蒸发与补充的全球和掩盖的补丁目标。由于实验数据集的歧视可以代表收集特定的实验问题,我们如何制定多种合成和实验性评估,并发现结构性差异性从

Machine translation (Meta M2M100); the English original follows.

Abstract

Understanding biomolecular function requires characterizing three-dimensional structure and conformational variability, for which cryogenic electron microscopy (cryo-EM) is a powerful tool. Cryo-EM produces noisy two-dimensional particle images in which molecular structure, particle pose, and imaging conditions are jointly encoded. Recovering biological information from these observations spans inverse problems including pose estimation, heterogeneity analysis, and reconstruction. These tasks are usually addressed independently despite shared protein structure and image formation, motivating a common representation across inverse problems. Since biologically meaningful differences may be spatially localized, we study self-distillation with complementary global and masked patch objectives. Because experimental dataset discrimination can reflect acquisition-specific cues, we develop controlled synthetic and experimental evaluations and find that structural discrimination benefits from dense spatial features. We introduce the EMber family of vision transformers, pre-trained on >50 million experimental particle images from 214 EMPIAR datasets together with simulated projections from 268,000 molecular assemblies. EMber improves upon existing methods at conformational and compositional discrimination by 14.85 and 14.26 percentage points, estimates conformational populations to within 1.59pp error with a linear probe, and produces particle assignments yielding reference-quality reconstructions in fewer epochs. We demonstrate how to learn representations that capture biological information for use across multiple cryo-EM inverse problems.

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Topics: Representation (politics), Inverse problem, Inverse, Computer science, Artificial intelligence, Function (biology)

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