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Connecting chemical and protein sequence space to predict biocatalytic reactions by Alexandra E. Paton & Daniil A. Boiko & Jonathan C. Perkins & Nicholas I. Cemalovic & Thiago Reschützegger & Gabe Gomes & Alison R. H. Narayan instant download

  • SKU: EBN-239571240
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Instant download (eBook) Connecting chemical and protein sequence space to predict biocatalytic reactions after payment.
Authors:Alexandra E. Paton & Daniil A. Boiko & Jonathan C. Perkins & Nicholas I. Cemalovic & Thiago Reschützegger & Gabe Gomes & Alison R. H. Narayan
Pages:updating ...
Year:2025
Publisher:x
Language:english
File Size:5.19 MB
Format:pdf
Categories: Ebooks

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Connecting chemical and protein sequence space to predict biocatalytic reactions by Alexandra E. Paton & Daniil A. Boiko & Jonathan C. Perkins & Nicholas I. Cemalovic & Thiago Reschützegger & Gabe Gomes & Alison R. H. Narayan instant download

Nature, doi:10.1038/s41586-025-09519-5

Accepted: 12 August 2025Published online: 1 October 2025The application of biocatalysis in synthesis has the potential to ofer streamlined routes towards target molecules1, tunable catalyst-controlled selectivity2, as well Open accessas processes with improved sustainability3. Despite these advantages, biocatalysis Check for updatesis often a high-risk strategy to implement, as identifying an enzyme capable of performing chemistry on a specifc intermediate required for a synthesis can be a roadblock that requires extensive screening of enzymes and protein engineering to overcome4. Strategies for predicting which enzyme and small molecule are compatible have been hindered by the lack of well-studied biocatalytic reaction datasets5. The underexploration of connections between chemical and protein sequence space constrains navigation between these two landscapes. Here we report a two-phase efort relying on high-throughput experimentation to populate connections between productive substrate and enzyme pairs and the subsequent development of a tool, CATNIP, for predicting compatible α-ketoglutarate (α-KG)/Fe(ii)-dependent enzymes for a given substrate or, conversely, for ranking potential substrates for a given α-KG/Fe(ii)-dependent enzyme sequence. We anticipate that our approach can be readily expanded to further enzyme and transformation classes and will derisk the investigation and application of biocatalytic methods.

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