Machine learning-guided protein engineering is a new paradigm that enables the optimization of complex protein functions. Machine-learning methods use data to predict protein function without requiring a detailed model of the underlying physics or biological pathways. They accelerate protein engineering by learning from information contained in all measured variants and using it to select variants that are likely to be improved. In this review, we introduce the steps required to collect protein data, train machine-learning models, and use trained models to guide engineering. We make recommendations at each stage and look to future opportunities for machine learning to enable the discovery of new protein functions and uncover the relationship between protein sequence and function.
Machine learning (ML)-guided directed evolution is a new paradigm for biological design that enables optimization of complex functions. ML methods use data to predict how sequence maps to function without requiring a detailed model of the underlying physics or biological pathways. To demonstrate ML-guided directed evolution, we introduce the steps required to build ML sequence-function models and use them to guide engineering, making recommendations at each stage. This review covers basic concepts relevant to using ML for protein engineering as well as the current literature and applications of this new engineering paradigm. ML methods accelerate directed evolution by learning from information contained in all measured variants and using that information to select sequences that are likely to be improved. We then provide two case studies that demonstrate the ML-guided directed evolution process. We also look to future opportunities where ML will enable discovery of new protein functions and uncover the relationship between protein sequence and function.
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learning-guided protein engineering
learning (ML)-guided directed evolution
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They accelerate protein engineering
To demonstrate ML-guided directed evolution, we introduce the steps required to build ML sequence-function models and use them to guide engineering, making recommendations at each stage. This review covers basic concepts relevant to using ML for protein engineering as well as the current literature and applications of this new engineering paradigm. ML methods accelerate directed evolution
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it to select variants
that information to select sequences
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In this review, we introduce the steps required to collect protein data, train machine-learning models, and use trained models to guide engineering. We make recommendations at each stage and
We then provide two case studies that demonstrate the ML-guided directed evolution process. We also
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for machine learning to enable the
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