Protein sequence design with deep generative models.

Journal: Current opinion in chemical biology
Published Date:

Abstract

Protein engineering seeks to identify protein sequences with optimized properties. When guided by machine learning, protein sequence generation methods can draw on prior knowledge and experimental efforts to improve this process. In this review, we highlight recent applications of machine learning to generate protein sequences, focusing on the emerging field of deep generative methods.

Authors

  • Zachary Wu
    Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, USA.
  • Kadina E Johnston
    Division of Biology and Biological Engineering, California Institute of Technology, MC 210-41, 1200 E. California Boulevard, Pasadena, CA 91125, USA.
  • Frances H Arnold
    Division of Biology and Biological Engineering; California Institute of Technology; Pasadena, California; United States of America.
  • Kevin K Yang
    Division of Chemistry and Chemical Engineering; California Institute of Technology; Pasadena, California; United States of America.