无粒子拾取的单粒子重构:突破检测极限

IF 2.1 3区 数学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE SIAM Journal on Imaging Sciences Pub Date : 2023-06-07 DOI:10.1137/22m1503828
Tamir Bendory, Nicolas Boumal, William Leeb, Eitan Levin, Amit Singer
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引用次数: 1

摘要

单粒子低温电子显微镜(cryo-EM)最近加入了x射线晶体学和核磁共振波谱学,成为一种高分辨率的结构方法来分析生物大分子。在低温电子显微镜实验中,显微镜产生的图像被称为显微照片。感兴趣的分子的投影嵌入在未知位置的显微照片中,在未知的观察方向下。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,高噪音水平阻碍了检测。当可靠的检测变得不可能时,标准技术就失效了。这是一个问题,特别是对于小分子。在本文中,我们采用了一种完全不同的方法:我们认为结构原则上可以直接从显微照片中重建,而无需中间检测。其目的是将小分子带入低温电子显微镜的触手可及范围内。为此,我们设计了一种自相关分析技术,允许人们直接从显微照片到所寻找的结构。这只需要通过一次显微照片,就可以对大型实验进行在线流式处理。我们展示了数值结果,并讨论了将这种概念验证转化为最先进算法的补充方法所面临的挑战。
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Toward Single Particle Reconstruction without Particle Picking: Breaking the Detection Limit
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.
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来源期刊
SIAM Journal on Imaging Sciences
SIAM Journal on Imaging Sciences COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, SOFTWARE ENGINEERING
CiteScore
3.80
自引率
4.80%
发文量
58
审稿时长
>12 weeks
期刊介绍: SIAM Journal on Imaging Sciences (SIIMS) covers all areas of imaging sciences, broadly interpreted. It includes image formation, image processing, image analysis, image interpretation and understanding, imaging-related machine learning, and inverse problems in imaging; leading to applications to diverse areas in science, medicine, engineering, and other fields. The journal’s scope is meant to be broad enough to include areas now organized under the terms image processing, image analysis, computer graphics, computer vision, visual machine learning, and visualization. Formal approaches, at the level of mathematics and/or computations, as well as state-of-the-art practical results, are expected from manuscripts published in SIIMS. SIIMS is mathematically and computationally based, and offers a unique forum to highlight the commonality of methodology, models, and algorithms among diverse application areas of imaging sciences. SIIMS provides a broad authoritative source for fundamental results in imaging sciences, with a unique combination of mathematics and applications. SIIMS covers a broad range of areas, including but not limited to image formation, image processing, image analysis, computer graphics, computer vision, visualization, image understanding, pattern analysis, machine intelligence, remote sensing, geoscience, signal processing, medical and biomedical imaging, and seismic imaging. The fundamental mathematical theories addressing imaging problems covered by SIIMS include, but are not limited to, harmonic analysis, partial differential equations, differential geometry, numerical analysis, information theory, learning, optimization, statistics, and probability. Research papers that innovate both in the fundamentals and in the applications are especially welcome. SIIMS focuses on conceptually new ideas, methods, and fundamentals as applied to all aspects of imaging sciences.
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