A deep learning solution for crystallographic structure determination.

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.

SUBMITTER: Pan T 

PROVIDER: S-EPMC10324481 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature