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Machine learning-assisted directed protein evolution with combinatorial libraries

Proceedings of the National Academy of Sciences - PNAS, 2019-04, Vol.116 (18), p.8852-8858 [Peer Reviewed Journal]

Copyright National Academy of Sciences Apr 30, 2019 ;2019 ;ISSN: 0027-8424 ;EISSN: 1091-6490 ;DOI: 10.1073/pnas.1901979116 ;PMID: 30979809

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Title:
Machine learning-assisted directed protein evolution with combinatorial libraries
Author: Wu, Zachary ; Kan, S. B. Jennifer ; Lewis, Russell D. ; Wittmann, Bruce J. ; Arnold, Frances H.
Subjects: Amino Acid Sequence ; Artificial intelligence ; Biological Sciences ; Catalysis ; Coding ; Combinatorial analysis ; Combinatorial Chemistry Techniques - methods ; Combinatorial libraries ; Directed evolution ; Directed Molecular Evolution ; Enantiomers ; Evolution ; Fitness ; Humans ; Learning algorithms ; Machine Learning ; Models, Molecular ; Mutation ; Oxygenases - genetics ; Oxygenases - metabolism ; Physical Sciences ; PNAS Plus ; Protein Conformation ; Protein engineering ; Proteins ; Rhodothermus - enzymology ; Silicon ; Small Molecule Libraries ; Test procedures ; Workflow
Is Part Of: Proceedings of the National Academy of Sciences - PNAS, 2019-04, Vol.116 (18), p.8852-8858
Description: 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.
Publisher: United States: National Academy of Sciences
Language: English
Identifier: ISSN: 0027-8424
EISSN: 1091-6490
DOI: 10.1073/pnas.1901979116
PMID: 30979809
Source: Geneva Foundation Free Medical Journals at publisher websites
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Machine learning-assisted directed protein evolution with combinatorial libraries

Copyright National Academy of Sciences Apr 30, 2019 ;2019 ;ISSN: 0027-8424 ;EISSN: 1091-6490 ;DOI: 10.1073/pnas.1901979116 ;PMID: 30979809

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