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Bayesian orientation estimate and structure information from sparse single-molecule x-ray diffraction images.

机译:来自稀疏单分子x射线衍射图像的贝叶斯方向估计和结构信息。

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摘要

We developed a Bayesian method to extract macromolecular structure information from sparse single-molecule x-ray free-electron laser diffraction images. The method addresses two possible scenarios. First, using a “seed” structural model, the molecular orientation is determined for each of the provided diffraction images, which are then averaged in three-dimensional reciprocal space. Subsequently, the real space electron density is determined using a relaxed averaged alternating reflections algorithm. In the second approach, the probability that the “seed” model fits to the given set of diffraction images as a whole is determined and used to distinguish between proposed structures. We show that for a given x-ray intensity, unexpectedly, the achievable resolution increases with molecular mass such that structure determination should be more challenging for small molecules than for larger ones. For a sufficiently large number of recorded photons (>200) per diffraction image an M1/6 scaling is seen. Using synthetic diffraction data for a small glutathione molecule as a challenging test case, successful determination of electron density was demonstrated for 20000 diffraction patterns with random orientations and an average of 82 elastically scattered and recorded photons per image, also in the presence of up to 50% background noise. The second scenario is exemplified and assessed for three biomolecules of different sizes. In all cases, determining the probability of a structure given set of diffraction patterns allowed successful discrimination between different conformations of the test molecules. A structure model of the glutathione tripeptide was refined in a Monte Carlo simulation from a random starting conformation. Further, effective distinguishing between three differently arranged immunoglobulin domains of a titin molecule and also different states of a ribosome in a tRNA translocation process was demonstrated. These results show that the proposed method is robust and enables structure determination from sparse and noisy x-ray diffraction images of single molecules spanning a wide range of molecular masses.
机译:我们开发了一种贝叶斯方法从稀疏单分子X射线自由电子激光衍射图像中提取大分子结构信息。该方法解决了两种可能的情况。首先,使用“种子”结构模型,为每个提供的衍射图像确定分子取向,然后将其在三维倒数空间中平均。随后,使用松弛平均交替反射算法确定实际空间电子密度。在第二种方法中,确定“种子”模型总体上适合给定衍射图像集的概率,并将其用于区分建议的结构。我们表明,对于给定的X射线强度,出乎意料的是,可实现的分辨率随着分子量的增加而增加,因此对于大分子而言,结构确定对于大分子而言应更具挑战性。对于每个衍射图像足够多的记录光子(> 200),可以看到M1 / 6缩放。使用一个小谷胱甘肽分子的合成衍射数据作为具有挑战性的测试案例,成功地确定了具有随机方向的20000个衍射图样的电子密度,每个图像平均有82个弹性散射和记录的光子,即使存在多达50个% 背景噪音。以第二种情况为例,并对三种不同大小的生物分子进行了评估。在所有情况下,通过确定一组给定的衍射图样来确定结构的可能性,可以成功区分测试分子的不同构象。谷胱甘肽三肽的结构模型在蒙特卡洛模拟中从随机起始构象中得到了完善。此外,已证明在tRNA易位过程中有效区分了三联蛋白分子的三个不同排列的免疫球蛋白结构域以及核糖体的不同状态。这些结果表明,所提出的方法是鲁棒的,并且能够从跨越大分子质量范围的单个分子的稀疏和嘈杂的X射线衍射图像确定结构。

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    Walczak, M.; Grubmüller, H.;

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  • 年度 2014
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  • 原文格式 PDF
  • 正文语种 eng
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