A deep learning solution for crystallographic structure determination

Date
2023
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International Union of Crystallography
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The general de novo solution of the crystallographic phase problem is difficult and only possible under certain conditions. This paper develops an initial pathway to a deep learning neural network approach for the phase problem in protein crystallography, based on a synthetic dataset of small fragments derived from a large well curated subset of solved structures in the Protein Data Bank (PDB). In particular, electron-density estimates of simple artificial systems are produced directly from corresponding Patterson maps using a convolutional neural network architecture as a proof of concept.

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Pan, T., Jin, S., Miller, M. D., et al.. "A deep learning solution for crystallographic structure determination." IUCrJ, 10, no. 4 (2023) International Union of Crystallography: 487-496. https://doi.org/10.1107/S2052252523004293.

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