<p>The application of biocatalysis in synthesis has the potential to offer streamlined routes towards target molecules<sup><CitationRef CitationID="CR1">1</CitationRef></sup>, tunable catalyst-controlled selectivity<sup><CitationRef CitationID="CR2">2</CitationRef></sup>, as well as processes with improved sustainability<sup><CitationRef CitationID="CR3">3</CitationRef></sup>. Despite these advantages, biocatalysis is often a high-risk strategy to implement, as identifying an enzyme capable of performing chemistry on a specific intermediate required for a synthesis can be a roadblock that requires extensive screening of enzymes and protein engineering to overcome<sup><CitationRef CitationID="CR4">4</CitationRef></sup>. Strategies for predicting which enzyme and small molecule are compatible have been hindered by the lack of well-studied biocatalytic reaction datasets<sup><CitationRef CitationID="CR5">5</CitationRef></sup>. The underexploration of connections between chemical and protein sequence space constrains navigation between these two landscapes. Here we report a two-phase effort 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(<span>ii</span>)-dependent enzymes for a given substrate or, conversely, for ranking potential substrates for a given&#xa0;α-KG/Fe(<span>ii</span>)-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.</p>

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Connecting chemical and protein sequence space to predict biocatalytic reactions

  • Alexandra E. Paton,
  • Daniil A. Boiko,
  • Jonathan C. Perkins,
  • Nicholas I. Cemalovic,
  • Thiago Reschützegger,
  • Gabe Gomes,
  • Alison R. H. Narayan

摘要

The application of biocatalysis in synthesis has the potential to offer streamlined routes towards target molecules1, tunable catalyst-controlled selectivity2, as well as processes with improved sustainability3. Despite these advantages, biocatalysis is often a high-risk strategy to implement, as identifying an enzyme capable of performing chemistry on a specific 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 effort 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.