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Preliminary studies on iterative transform algorithms for 3D reconstruction of 2D crystals

机译:二维晶体3D重建的迭代变换算法的初步研究

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In this paper, we apply iterative transform algorithms, namely the error reduction algorithm and the hybrid input-output algorithm, to retrieve missing data in 3D reconstruction from 2D crystal images. 3D protein structures are determined using cryo-electron microscopy (cryo-EM). Extremely strong noise in cryo-EM brings in unreliable artifacts and a limited number of projections leave missing components in 3D Fourier space. We consider all these cases as missing data problems. The iterative transform algorithms aim at improving 3D resolution through applying constraints in Fourier space and real space. In Fourier space, the "true" signal components, which are either physically measured or assumed, are retained while the delusive components are updated iteratively. In real space, the protein has spatial limitation or "finite support" and possesses non-negativity values. Preliminary results of 3D reconstruction of membrane protein GlpF suggest that the iterative transform process improves 3D resolution.
机译:在本文中,我们应用迭代变换算法,即误差减少算法和混合输入输出算法,从2D晶体图像中检索3D重建中的缺失数据。使用冷冻电子显微镜(cryo-EM)确定3D蛋白结构。 cryo-EM中极强的噪声会带来不可靠的伪影,并且数量有限的投影会在3D傅立叶空间中留下缺失的组件。我们将所有这些情况视为缺少数据问题。迭代变换算法旨在通过在傅立叶空间和实际空间中应用约束来提高3D分辨率。在傅立叶空间中,保留“物理”信号分量,无论是物理测量的还是假定的信号分量,都可以迭代地更新欺骗性分量。在现实空间中,蛋白质具有空间限制或“有限支持”,并且具有非负值。膜蛋白GlpF的3D重建的初步结果表明,迭代转化过程可提高3D分辨率。

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