Protein sequence design with deep generative models

Zachary Wu, Kadina E. Johnston, Frances H. Arnold, Kevin K. Yang

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.