Department of Computer Science, Rice University, Houston, Texas, USA.
Department of Biosciences, Rice University, Houston, Texas, USA.
IUCrJ. 2023 Jul 1;10(Pt 4):487-496. doi: 10.1107/S2052252523004293.
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.
晶体学相问题的一般从头解法较为困难，仅在某些条件下可行。本文提出了一种基于蛋白质晶体学相问题的深度学习神经网络方法的初始途径，该方法基于源自蛋白质数据库（PDB）中已解决结构的大型精选子集的小片段的合成数据集。具体来说，使用卷积神经网络架构直接从相应的帕特森图生成简单人工系统的电子密度估计，作为概念验证。
