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

IUCrJ, ISSN: 2052-2525, Vol: 9, Issue: Pt 1, Page: 86-97
2022
  • 45
    Citations
  • 0
    Usage
  • 49
    Captures
  • 1
    Mentions
  • 80
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    45
  • Captures
    49
  • Mentions
    1
    • News Mentions
      1
      • 1
  • Social Media
    80
    • Shares, Likes & Comments
      80
      • Facebook
        80

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Article Description

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.

Bibliographic Details

Chojnowski, Grzegorz; Simpkin, Adam J; Leonardo, Diego A; Seifert-Davila, Wolfram; Vivas-Ruiz, Dan E; Keegan, Ronan M; Rigden, Daniel J

International Union of Crystallography (IUCr)

Chemistry; Biochemistry, Genetics and Molecular Biology; Materials Science; Physics and Astronomy

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