Protein engineering through machine-learning-guided directed evolution enables the optimization of protein functions. Machine-learning approaches predict how sequence maps to function in a data-driven manner without requiring a detailed model of the underlying physics or biological pathways. Such methods accelerate directed evolution by learning from the properties of characterized variants and using that information to select sequences that are likely to exhibit improved properties. Here we introduce the steps required to build machine-learning sequence–function models and to use those models to guide engineering, making recommendations at each stage. This review covers basic concepts relevant to the use of machine learning for protein engineering, as well as the current literature and applications of this engineering paradigm. We illustrate the process with two case studies. Finally, we look to future opportunities for machine learning to enable the discovery of unknown protein functions and uncover the relationship between protein sequence and function.
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The authors thank Y. Chen, K. Johnston, B. Wittmann, and H. Yang for comments on early versions of the manuscript, as well as members of the Arnold lab, J. Bois, and Y. Yue for general advice and discussions on protein engineering and machine learning. This work was supported by the US Army Research Office Institute for Collaborative Biotechnologies (W911F-09-0001 to F.H.A.), the Donna and Benjamin M. Rosen Bioengineering Center (to K.K.Y.), and the National Science Foundation (GRF2017227007 to Z.W.).
The authors declare no competing interests.
Peer review information: Nina Vogt was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.
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Yang, K.K., Wu, Z. & Arnold, F.H. Machine-learning-guided directed evolution for protein engineering. Nat Methods 16, 687–694 (2019) doi:10.1038/s41592-019-0496-6
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