Login / Signup

Reconstructing three-dimensional protein crystal intensities from sparse unoriented two-axis X-ray diffraction patterns.

Ti-Yen LanJennifer L WiermanMark W TateHugh T PhilippVeit ElserSol M Gruner
Published in: Journal of applied crystallography (2017)
Recently, there has been a growing interest in adapting serial microcrystallography (SMX) experiments to existing storage ring (SR) sources. For very small crystals, however, radiation damage occurs before sufficient numbers of photons are diffracted to determine the orientation of the crystal. The challenge is to merge data from a large number of such 'sparse' frames in order to measure the full reciprocal space intensity. To simulate sparse frames, a dataset was collected from a large lysozyme crystal illuminated by a dim X-ray source. The crystal was continuously rotated about two orthogonal axes to sample a subset of the rotation space. With the EMC algorithm [expand-maximize-compress; Loh & Elser (2009). Phys. Rev. E, 80, 026705], it is shown that the diffracted intensity of the crystal can still be reconstructed even without knowledge of the orientation of the crystal in any sparse frame. Moreover, parallel computation implementations were designed to considerably improve the time and memory scaling of the algorithm. The results show that EMC-based SMX experiments should be feasible at SR sources.
Keyphrases
  • neural network
  • machine learning
  • solid state
  • high resolution
  • deep learning
  • high intensity
  • computed tomography
  • artificial intelligence
  • electron microscopy
  • binding protein
  • ionic liquid