Sorting algorithms for single-particle imaging experiments at X-ray free-electron lasers

Bobkov, Sergey; Teslyuk, Anton; Kurta, Ruslan P.; Gorobtsov, Oleg; Yefanov, Oleksandr; Ilyin, V. A.; Сенин, Р. А.; Vartanyants, Ivan A.

doi:10.1107/s1600577515017348

Cited by 32 publications

(28 citation statements)

References 43 publications

(41 reference statements)

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“…Substantial technological achievements have now made it possible to perform such measurements [10–13]. Together with recent algorithmic developments for data recognition and classification [14–17], orientation determination [18–23], and phase retrieval [24–26], these achievements have allowed for the advancement of the single-particle coherent x-ray diffraction imaging [12,27] technique at XFELs from 2D applications for rather large samples [28,29] towards 3D imaging of nanoscale objects [11–13]. However, the image resolution of reconstructed biological samples demonstrated so far has been limited; thus, further theoretical and experimental efforts are needed to establish single-particle imaging (SPI) techniques at XFELs [30].…”

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confidence: 99%

Correlations in Scattered X-Ray Laser Pulses Reveal Nanoscale Structural Features of Viruses

et al. 2017

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We use extremely bright and ultrashort pulses from an x-ray free-electron laser (XFEL) to measure correlations in x rays scattered from individual bioparticles. This allows us to go beyond the traditional crystallography and single-particle imaging approaches for structure investigations. We employ angular correlations to recover the three-dimensional (3D) structure of nanoscale viruses from x-ray diffraction data measured at the Linac Coherent Light Source. Correlations provide us with a comprehensive structural fingerprint of a 3D virus, which we use both for model-based and ab initio structure recovery. The analyses reveal a clear indication that the structure of the viruses deviates from the expected perfect icosahedral symmetry. Our results anticipate exciting opportunities for XFEL studies of the structure and dynamics of nanoscale objects by means of angular correlations.

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confidence: 99%

Correlations in Scattered X-Ray Laser Pulses Reveal Nanoscale Structural Features of Viruses

et al. 2017

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“…Classification work includes manifold mapping (6), spectral clustering (7), principal component analysis, and support vector machines (8). Orientation methods include common curve approaches (9)(10)(11)(12), expectation maximization (13)(14)(15), and manifold embedding (16)(17)(18)(19).…”

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confidence: 99%

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

Donatelli

Sethian

Zwart

2017

Proc. Natl. Acad. Sci. U.S.A.

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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 singleparticle 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.single-particle imaging | multitiered iterative phasing | structure determination S ingle-particle X-ray diffraction aims to determine the structures of biological molecules from an ensemble of diffraction patterns, each collected from a single particle per shot. Particles are delivered to the beam at random orientations through either a liquid medium (1) or aerosolization (2). Large-scale experimental facilities, such as the Linac Coherent Light Source (3), have been developed to perform these experiments, with initial results indicating the feasibility of single-particle diffraction as a viable technique to address challenges in the health, material, and energy sciences (4, 5).A fundamental challenge in single-particle imaging is that the orientation of each imaged particle is unknown and must be recovered to determine structural information. Additionally, many biological samples display conformational flexibility and may exist in one of many possible structural states. To account for varying structural states and avoid a loss of resolution due to averaging of states, the diffraction patterns may need to be classified to the correct state. Furthermore, single particles scatter very few photons; hence the images are heavily contaminated by shot noise, often with less than a photon per Shannon pixel at high scattering angles.Current approaches typically determine states, orientations, and structures in separate consecutive steps. Classification work includes manifold mapping (6), spectral clustering (7), principal component analysis, and support vector machines (8). Orientation methods include common curve approaches (9-12), expectation maximization (13-15), and manifold embedding (16)(17)(18)(19). Once images are classified, oriented, and assemb...

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“…The classification is used to select classes with actual particles and filter out the rest. The possibility of machine learning based classification of particle images in random orientations in Fourier space was recently studied [25].…”

Section: 22mentioning

confidence: 99%

Towards on-the-fly Cryo-Electron Microscopy Data Processing by High Performance Data Analysis

Pichkur

Baimukhametov

Teslyuk

et al. 2018

J. Phys.: Conf. Ser.

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Abstract. In this paper we present an approach to the on-the-fly Cryo-EM data acquisition and analysis using High Performance Data Analysis concept. An analysis of bottlenecks in the currently used data processing is presented, and the new iterative scheme for near real time implementation of the Cryo-EM data processing is discussed. The on-the-fly approach is proposed that will considerably reduce the time required for the protein (or other biological objects, e.g. viruses) 3D structure determination and allow eliminating manual interventions in the data processing.

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Sorting algorithms for single-particle imaging experiments at X-ray free-electron lasers

Cited by 32 publications

References 43 publications

Correlations in Scattered X-Ray Laser Pulses Reveal Nanoscale Structural Features of Viruses

Correlations in Scattered X-Ray Laser Pulses Reveal Nanoscale Structural Features of Viruses

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

Towards on-the-fly Cryo-Electron Microscopy Data Processing by High Performance Data Analysis

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