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findMySequence : a neural-network-based approach for identification of unknown proteins in X-ray crystallography and cryo-EM.

Grzegorz ChojnowskiAdam J SimpkinDiego A LeonardoWolfram Seifert-DavilaDan E Vivas-RuizRonan M KeeganDaniel J Rigden
Published in: IUCrJ (2021)
Although experimental protein-structure determination usually targets known proteins, chains of unknown sequence are often encountered. They can be purified from natural sources, appear as an unexpected fragment of a well characterized protein or appear as a contaminant. Regardless of the source of the problem, the unknown protein always requires characterization. Here, an automated pipeline is presented for the identification of protein sequences from cryo-EM reconstructions and crystallographic data. The method's application to characterize the crystal structure of an unknown protein purified from a snake venom is presented. It is also shown that the approach can be successfully applied to the identification of protein sequences and validation of sequence assignments in cryo-EM protein structures.
Keyphrases
  • protein protein
  • amino acid
  • small molecule
  • machine learning
  • mass spectrometry
  • deep learning
  • artificial intelligence
  • genetic diversity
  • simultaneous determination
  • contrast enhanced