Dynamic X-ray diffraction sampling for protein crystal positioning
A sparse supervised learning approach for dynamic sampling (SLADS) is described for dose reduction in diffraction-based protein crystal positioning. Crystal centering is typically a prerequisite for macromolecular diffraction at synchrotron facilities, with X-ray diffraction mapping growing in popularity as a mechanism for localization. In X-ray raster scanning, diffraction is used to identify the crystal positions based on the detection of Bragg-like peaks in the scattering patterns; however, this additional X-ray exposure may result in detectable damage to the crystal prior to data collection. Dynamic sampling, in which preceding measurements inform the next most information-rich location to probe for image reconstruction, significantly reduced the X-ray dose experienced by protein crystals during positioning by diffraction raster scanning. The SLADS algorithm implemented herein is designed for single-pixel measurements and can select a new location to measure. In each step of SLADS, the algorithm selects the pixel, which, when measured, maximizes the expected reduction in distortion given previous measurements. Ground-truth diffraction data were obtained for a 5 µ m-diameter beam and SLADS reconstructed the image sampling 31% of the total volume and only 9% of the interior of the crystal greatly reducing the X-ray dosage on the crystal. Using in situ two-photon-excited fluorescence microscopy measurements as a surrogate for diffraction imaging with a 1 µ m-diamet...
Source: Journal of Synchrotron Radiation - Category: Physics Authors: Scarborough, N.M. Godaliyadda, G.M.D.P. Ye, D.H. Kissick, D.J. Zhang, S. Newman, J.A. Sheedlo, M.J. Chowdhury, A.U. Fischetti, R.F. Das, C. Buzzard, G.T. Bouman, C.A. Simpson, G.J. Tags: dynamic sampling supervised learning approach X-ray diffraction nonlinear optical microscopy two-photon-excited fluorescence second-harmonic generation research papers Source Type: research