Bendory Tamir, Boumal Nicolas, Leeb William, Levin Eitan, Singer Amit
The School of Electrical Engineering, Tel Aviv University, Tel Aviv 69978, Israel.
Institute of Mathematics, Ecole Polytechnique Fédérale DE Lausanne EPFL, 1015 Lausanne, Switzerland.
SIAM J Imaging Sci. 2023;16(2):886-910. doi: 10.1137/22m1503828.
Single-particle cryo-electron microscopy (cryo-EM) has recently joined X-ray crystallography and NMR spectroscopy as a high-resolution structural method to resolve biological macromolecules. In a cryo-EM experiment, the microscope produces images called micrographs. Projections of the molecule of interest are embedded in the micrographs at unknown locations, and under unknown viewing directions. Standard imaging techniques first locate these projections (detection) and then reconstruct the 3-D structure from them. Unfortunately, high noise levels hinder detection. When reliable detection is rendered impossible, the standard techniques fail. This is a problem, especially for small molecules. In this paper, we pursue a radically different approach: we contend that the structure could, in principle, be reconstructed directly from the micrographs, without intermediate detection. The aim is to bring small molecules within reach for cryo-EM. To this end, we design an autocorrelation analysis technique that allows one to go directly from the micrographs to the sought structures. This involves only one pass over the micrographs, allowing online, streaming processing for large experiments. We show numerical results and discuss challenges that lay ahead to turn this proof-of-concept into a complementary approach to state-of-the-art algorithms.
单颗粒冷冻电子显微镜技术（cryo-EM）最近已成为一种用于解析生物大分子结构的高分辨率结构方法，与X射线晶体学和核磁共振光谱技术并列。在冷冻电子显微镜实验中，显微镜会生成称为显微照片的图像。感兴趣分子的投影以未知位置和未知观察方向嵌入在显微照片中。标准成像技术首先定位这些投影（检测），然后从它们重建三维结构。不幸的是，高噪声水平阻碍了检测。当无法进行可靠检测时，标准技术就会失效。这是一个问题，尤其是对于小分子而言。在本文中，我们采用了一种截然不同的方法：我们认为原则上可以直接从显微照片重建结构，而无需中间检测步骤。目的是使小分子也能通过冷冻电子显微镜进行研究。为此，我们设计了一种自相关分析技术，该技术可以直接从显微照片得到所需的结构。这只需要对显微照片进行一次遍历，允许对大型实验进行在线流式处理。我们展示了数值结果，并讨论了将这一概念验证转化为现有算法的补充方法所面临的挑战。
