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A deep learning solution for crystallographic structure determination

By Tom Pan, Shikai Jin, Mitchell Miller1, Anastasios Kyrillidis, George Phillips1

1. Rice University

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Category

BioXFEL Publications

Published on

Type

journal-article

Author

Tom Pan and Shikai Jin and Mitchell D. Miller and Anastasios Kyrillidis and George N. Phillips

Citation

Pan, T., Jin, S., Miller, M. D., Kyrillidis, A., & Phillips, G. N. (2023). A deep learning solution for crystallographic structure determination. IUCrJ, 10(4), 487–496. https://doi.org/10.1107/s2052252523004293

Abstract

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.

DOI

http://dx.doi.org/10.1107/s2052252523004293

Funding

NSF-STC Biology with X-ray Lasers (NSF-1231306)