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

被引:26
|
作者
Donatelli, Jeffrey J. [1 ,2 ]
Sethian, James A. [1 ,2 ,3 ]
Zwart, Peter H. [2 ,4 ]
机构
[1] Lawrence Berkeley Natl Lab, Dept Appl Math, Berkeley, CA 94720 USA
[2] Lawrence Berkeley Natl Lab, Ctr Adv Math Energy Res Applicat, Berkeley, CA 94720 USA
[3] Univ Calif Berkeley, Dept Math, Berkeley, CA 94720 USA
[4] Lawrence Berkeley Natl Lab, Mol Biophys & Integrated Bioimaging Div, Berkeley, CA 94720 USA
基金
美国国家卫生研究院;
关键词
single-particle imaging; multitiered iterative phasing; structure determination; X-RAY LASER; 3-DIMENSIONAL RECONSTRUCTION; SCATTERING; IMAGE; MICROSCOPY;
D O I
10.1073/pnas.1708217114
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
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.
引用
收藏
页码:7222 / 7227
页数:6
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