HelixGAN a deep-learning methodology for conditional de novo design of α-helix structures.

Journal: Bioinformatics (Oxford, England)
PMID: 36651657

Abstract

MOTIVATION: Protein and peptide engineering has become an essential field in biomedicine with therapeutics, diagnostics and synthetic biology applications. Helices are both abundant structural feature in proteins and comprise a major portion of bioactive peptides. Precise design of helices for binding or biological activity is still a challenging problem.

Authors

  • Xuezhi Xie
    Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 3E1, Canada.
  • Pedro A Valiente
    Department of Computer Science, University of Toronto, Toronto, ON M5S 3E1, Canada.
  • Philip M Kim
    Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 1AS, Canada; Department of Computer Science, University of Toronto, Toronto, ON M5S 3G4, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1AS, Canada. Electronic address: pi@kimlab.org.

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