Scientific Reports (Dec 2021)

Machine learning to estimate the local quality of protein crystal structures

  • Ikuko Miyaguchi,
  • Miwa Sato,
  • Akiko Kashima,
  • Hiroyuki Nakagawa,
  • Yuichi Kokabu,
  • Biao Ma,
  • Shigeyuki Matsumoto,
  • Atsushi Tokuhisa,
  • Masateru Ohta,
  • Mitsunori Ikeguchi

DOI
https://doi.org/10.1038/s41598-021-02948-y
Journal volume & issue
Vol. 11, no. 1
pp. 1 – 13

Abstract

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Abstract Low-resolution electron density maps can pose a major obstacle in the determination and use of protein structures. Herein, we describe a novel method, called quality assessment based on an electron density map (QAEmap), which evaluates local protein structures determined by X-ray crystallography and could be applied to correct structural errors using low-resolution maps. QAEmap uses a three-dimensional deep convolutional neural network with electron density maps and their corresponding coordinates as input and predicts the correlation between the local structure and putative high-resolution experimental electron density map. This correlation could be used as a metric to modify the structure. Further, we propose that this method may be applied to evaluate ligand binding, which can be difficult to determine at low resolution.