Login / Signup

A deep learning solution for crystallographic structure determination.

Tom PanShikai JinMitchell D MillerAnastasios KyrillidisGeorge N Philips
Published in: IUCrJ (2023)
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.
Keyphrases