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Reconstruction from limited single-particle diffraction data via simultaneous determination of state orientation intensity and phase

机译:通过同时确定状态取向强度和相位从有限的单颗粒衍射数据重建

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摘要

Free-electron lasers now have the ability to collect X-ray diffraction patterns from individual molecules; however, each sample is delivered at unknown orientation and may be in one of several conformational states, each with a different molecular structure. Hit rates are often low, typically around 0.1%, limiting the number of useful images that can be collected. Determining accurate structural information requires classifying and orienting each image, accurately assembling them into a 3D diffraction intensity function, and determining missing phase information. Additionally, single particles typically scatter very few photons, leading to high image noise levels. We develop a multitiered iterative phasing algorithm to reconstruct structural information from single-particle diffraction data by simultaneously determining the states, orientations, intensities, phases, and underlying structure in a single iterative procedure. We leverage real-space constraints on the structure to help guide optimization and reconstruct underlying structure from very few images with excellent global convergence properties. We show that this approach can determine structural resolution beyond what is suggested by standard Shannon sampling arguments for ideal images and is also robust to noise.
机译:现在,自由电子激光器具有从单个分子收集X射线衍射图的能力。但是,每个样品都以未知的方向传递,并且可能处于几种构象状态之一,每个构象状态都具有不同的分子结构。命中率通常很低,通常约为0.1%,从而限制了可以收集的有用图像的数量。要确定准确的结构信息,需要对每个图像进行分类和定向,将它们准确地组装为3D衍射强度函数,并确定缺少的相位信息。此外,单个粒子通常会散射很少的光子,从而导致高图像噪声水平。我们开发了一种多层迭代定相算法,通过在单个迭代过程中同时确定状态,方向,强度,相位和基础结构,从单粒子衍射数据中重建结构信息。我们利用结构上的实际空间约束来帮助指导优化,并从极少数具有出色全局收敛性的图像中重建底层结构。我们表明,这种方法可以确定结构分辨率,而不是标准的Shannon采样参数所建议的理想图像,并且对噪声也很鲁棒。

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