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Machine learning-assisted directed protein evolution with combinatorial libraries
Open accessAbstract
To reduce experimental effort associated with directed protein evolution and to explore the sequence space encoded by mutating multiple positions simultaneously, we incorporate machine learning into the directed evolution workflow. Combinatorial sequence space can be quite expensive to sample experimentally, but machine-learning models trained on tested variants provide a fast method for testing sequence space computationally. We validated this approach on a large published empirical fitness landscape for human GB1 binding protein, demonstrating that machine learning-guided directed evolution finds variants with higher fitness than those found by other directed evolution approaches. We then provide an example application in evolving an enzyme to produce each of the two possible product enantiomers (i.e., stereodivergence) of a new-to-nature carbene Si–H insertion reaction. The approach predicted libraries enriched in functional enzymes and fixed seven mutations in two rounds of evolution to identify variants for selective catalysis with 93% and 79% ee (enantiomeric excess). By greatly increasing throughput with in silico modeling, machine learning enhances the quality and diversity of sequence solutions for a protein engineering problem.
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Citations
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Background
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Cited by
Machine-learning-guided directed evolution for protein engineering
Yang, K. K., Wu, Z., & Arnold, F. H. (2019) · Nature Methods · doi:10.1038/s41592-019-0496-6 · Cited by 1,130
Self-citationDirected Evolution: Methodologies and Applications
Wang, Y., Xue, P., Cao, M., Yu, T., Lane, S. T., & Zhao, H. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00260 · Cited by 719
The Crucial Role of Methodology Development in Directed Evolution of Selective Enzymes
Qu, G., Li, A., Acevedo‐Rocha, C. G., Sun, Z., & Reetz, M. T. (2020) · Angewandte Chemie International Edition · doi:10.1002/anie.201901491 · Cited by 473
Low-N protein engineering with data-efficient deep learning
Biswas, S., Khimulya, G., Alley, E. C., Esvelt, K. M., & Church, G. M. (2021) · Nature Methods · doi:10.1038/s41592-021-01100-y · Cited by 422
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Self-citation
Metadata
- Authors
- Wu, Z., Kan, S. B. J., Lewis, R. D., Wittmann, B. J., & Arnold, F. H.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 2019-04-12
- Volume
- 116
- Issue
- 18
- Pages
- 8852-8858
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W2917580301
- Cited by
- 589
- References
- 4
- FWCI
- 17.55
- Type
- journal article
- Language
- en
- Update notices
- correction (2019-12-30)
- Open access
- bronze
- Funders
- National Science Foundation (GRF2017227007)
- Rothenberg Innovation Initiative (N/A)
- Jacobs Institute for Molecular Engineering for Medicine (N/A)
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