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Evolving Artificial Metalloenzymes via Random Mutagenesis

Random mutagenesis has the potential to optimize the efficiency and selectivity of protein catalysts without requiring detailed knowledge of protein structure; however, introducing synthetic metal cofactors complicates the expression and screening of enzyme libraries, and activity arising from free...

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Autores principales: Yang, Hao, Swartz, Alan M., Park, Hyun June, Srivastava, Poonam, Ellis-Guardiola, Ken, Upp, David M., Lee, Gihoon, Belsare, Ketaki, Gu, Yifan, Zhang, Chen, Moellering, Raymond E., Lewis, Jared C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5891097/
https://www.ncbi.nlm.nih.gov/pubmed/29461523
http://dx.doi.org/10.1038/nchem.2927
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author Yang, Hao
Swartz, Alan M.
Park, Hyun June
Srivastava, Poonam
Ellis-Guardiola, Ken
Upp, David M.
Lee, Gihoon
Belsare, Ketaki
Gu, Yifan
Zhang, Chen
Moellering, Raymond E.
Lewis, Jared C.
author_facet Yang, Hao
Swartz, Alan M.
Park, Hyun June
Srivastava, Poonam
Ellis-Guardiola, Ken
Upp, David M.
Lee, Gihoon
Belsare, Ketaki
Gu, Yifan
Zhang, Chen
Moellering, Raymond E.
Lewis, Jared C.
author_sort Yang, Hao
collection PubMed
description Random mutagenesis has the potential to optimize the efficiency and selectivity of protein catalysts without requiring detailed knowledge of protein structure; however, introducing synthetic metal cofactors complicates the expression and screening of enzyme libraries, and activity arising from free co-factor must be eliminated. Here we report an efficient platform to create and screen libraries of artificial metalloenzymes (ArMs) via random mutagenesis which we use to evolve highly selective dirhodium cyclopropanases. Error-prone PCR and combinatorial codon mutagenesis enabled multiplexed analysis of random mutations, including at sites distal to the putative ArM active site that are difficult to identify using targeted mutagenesis approaches. Variants that exhibited significantly improved selectivity for each of cyclopropane product enantiomers were identified, and higher activity than previously reported ArM cyclopropanases obtained via targeted mutagenesis was also observed. This improved selectivity carried over to other dirhodium catalyzed transformations, including N- H, S-H and Si-H insertion, demonstrating that ArMs evolved for one reaction can serve as starting points to evolve catalysts for others.
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spelling pubmed-58910972018-07-22 Evolving Artificial Metalloenzymes via Random Mutagenesis Yang, Hao Swartz, Alan M. Park, Hyun June Srivastava, Poonam Ellis-Guardiola, Ken Upp, David M. Lee, Gihoon Belsare, Ketaki Gu, Yifan Zhang, Chen Moellering, Raymond E. Lewis, Jared C. Nat Chem Article Random mutagenesis has the potential to optimize the efficiency and selectivity of protein catalysts without requiring detailed knowledge of protein structure; however, introducing synthetic metal cofactors complicates the expression and screening of enzyme libraries, and activity arising from free co-factor must be eliminated. Here we report an efficient platform to create and screen libraries of artificial metalloenzymes (ArMs) via random mutagenesis which we use to evolve highly selective dirhodium cyclopropanases. Error-prone PCR and combinatorial codon mutagenesis enabled multiplexed analysis of random mutations, including at sites distal to the putative ArM active site that are difficult to identify using targeted mutagenesis approaches. Variants that exhibited significantly improved selectivity for each of cyclopropane product enantiomers were identified, and higher activity than previously reported ArM cyclopropanases obtained via targeted mutagenesis was also observed. This improved selectivity carried over to other dirhodium catalyzed transformations, including N- H, S-H and Si-H insertion, demonstrating that ArMs evolved for one reaction can serve as starting points to evolve catalysts for others. 2018-01-22 2018-03 /pmc/articles/PMC5891097/ /pubmed/29461523 http://dx.doi.org/10.1038/nchem.2927 Text en Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms
spellingShingle Article
Yang, Hao
Swartz, Alan M.
Park, Hyun June
Srivastava, Poonam
Ellis-Guardiola, Ken
Upp, David M.
Lee, Gihoon
Belsare, Ketaki
Gu, Yifan
Zhang, Chen
Moellering, Raymond E.
Lewis, Jared C.
Evolving Artificial Metalloenzymes via Random Mutagenesis
title Evolving Artificial Metalloenzymes via Random Mutagenesis
title_full Evolving Artificial Metalloenzymes via Random Mutagenesis
title_fullStr Evolving Artificial Metalloenzymes via Random Mutagenesis
title_full_unstemmed Evolving Artificial Metalloenzymes via Random Mutagenesis
title_short Evolving Artificial Metalloenzymes via Random Mutagenesis
title_sort evolving artificial metalloenzymes via random mutagenesis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5891097/
https://www.ncbi.nlm.nih.gov/pubmed/29461523
http://dx.doi.org/10.1038/nchem.2927
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