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Machine-learning-guided directed evolution for protein engineering

Nature Methods volume 16, pages 687–694 (2019)Cite this article

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Abstract

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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Fig. 1: Directed evolution with and without machine learning.
Fig. 2
Fig. 3: GP-UCB algorithm.
Fig. 4: Directed evolution using PLS regression.
Fig. 5: Directed evolution using GPs and Bayesian optimization.
Fig. 6: Autoencoder.

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Acknowledgements

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.).

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  1. Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, USA

    Kevin K. Yang, Zachary Wu & Frances H. Arnold

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K.K.Y., Z.W., and F.H.A. conceptualized the project. K.K.Y. wrote the manuscript with input and editing from all authors.

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Correspondence to Frances H. Arnold.

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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). https://doi.org/10.1038/s41592-019-0496-6

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