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Quantifying and comparing radiation damage in the Protein Data Bank.

Kathryn L ShelleyElspeth F Garman
Published in: Nature communications (2022)
Radiation damage remains one of the major bottlenecks to accurate structure solution in protein crystallography. It can induce structural and chemical changes in protein crystals, and is hence an important consideration when assessing the quality and biological veracity of crystal structures in repositories like the Protein Data Bank (PDB). However, detection of radiation damage artefacts has traditionally proved very challenging. To address this, here we introduce the B net metric. B net summarises in a single value the extent of damage suffered by a crystal structure by comparing the B-factor values of damage-prone and non-damage-prone atoms in a similar local environment. After validating that B net successfully detects damage in 23 different crystal structures previously characterised as damaged, we calculate B net values for 93,978 PDB crystal structures. Our metric highlights a range of damage features, many of which would remain unidentified by the other summary statistics typically calculated for PDB structures.
Keyphrases
  • oxidative stress
  • crystal structure
  • high resolution
  • amino acid
  • electronic health record
  • machine learning
  • mass spectrometry
  • binding protein
  • radiation induced
  • deep learning