Nature Communications (Apr 2021)

Protein design and variant prediction using autoregressive generative models

  • Jung-Eun Shin,
  • Adam J. Riesselman,
  • Aaron W. Kollasch,
  • Conor McMahon,
  • Elana Simon,
  • Chris Sander,
  • Aashish Manglik,
  • Andrew C. Kruse,
  • Debora S. Marks

DOI
https://doi.org/10.1038/s41467-021-22732-w
Journal volume & issue
Vol. 12, no. 1
pp. 1 – 11

Abstract

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The ability to design functional sequences is central to protein engineering and biotherapeutics. Here the authors introduce a deep generative alignment-free model for sequence design applied to highly variable regions and design and test a diverse nanobody library with improved properties for selection experiments.