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

Walczak, M.; Grubmüller, H.

doi:10.1016/j.cpc.2015.12.014

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

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Walczak, M.
Department of Theoretical and Computational Biophysics, MPI for biophysical chemistry, Max Planck Society;

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Grubmüller, H.
Department of Theoretical and Computational Biophysics, MPI for biophysical chemistry, Max Planck Society;

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http://www.sciencedirect.com/science/article/pii/S001046551500452X/pdfft?md5=7d4c38010d8ec185b6df581856ed8301&pid=1-s2.0-S001046551500452X-main.pdf
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Citation

Walczak, M., & Grubmüller, H. (2016). BASDet: Bayesian approach(es) for structure determination from single molecule X-ray diffration images. Computer Physics Communications, 201, 159-166. doi:10.1016/j.cpc.2015.12.014.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0029-6CE2-1

Abstract

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.