BASDet: Bayesian Approach(es) for Structure Determination from Single Molecule X-ray Diffraction Images

Published: 21 Apr 2016 | Version 1 | DOI: 10.17632/22yc6j3vmn.1
  • Michal Walczak,
    Michal Walczak
    Max Planck Institute for Biophysical Chemistry
  • Helmut Grubmuller
    Helmut Grubmuller
    Max Planck Institute for Biophysical Chemistry

Description of this data

X-ray free electron lasers (XFEL) are expected to enable molecular structure determination in single molecule diffraction experiments. In this paper, we describe an implementation of two orthogonal Bayesian approaches, previously introduced in Walczak and Grubmüller (2014), capable of extracting structure information from sparse and noisy diffraction images obtained in these experiments. In the ‘Orientational Bayes’ approach, a ‘seed’ model is used to determine for every recorded diffraction image the underlying molecular orientation. The molecular transform of the irradiated molecule is obtained by aligning and averaging those images in three-dimensional reciprocal space. By contrast, in the ‘Structural Bayes’ approach, a real space structure model is optimized to fit best to an entire set of diffraction images. This approach is used in a Monte Carlo structure refinement procedure.

Both presented approaches were implemented in C; previous tests (Walczak and Grubmüller, 2014) suggest that the algorithms are robust against low signal to noise ratios and can deliver high resolution structural information.

Experiment data files

peer reviewed

This data is associated with the following peer reviewed publication:

BASDet: Bayesian approach(es) for structure determination from single molecule X-ray diffraction images

Published in: Computer Physics Communications

Latest version

  • Version 1


    Published: 2016-04-21

    DOI: 10.17632/22yc6j3vmn.1

    Cite this dataset

    Walczak, Michal; Grubmuller, Helmut (2016), “BASDet: Bayesian Approach(es) for Structure Determination from Single Molecule X-ray Diffraction Images”, Mendeley Data, v1


Natural Sciences


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