Connecting chemical and protein sequence space to predict biocatalytic reactions

• 成果类型: Article
• 署名作者: Paton, Alexandra E.; Boiko, Daniil A.; Perkins, Jonathan C.; Cemalovic, Nicholas I.; Reschutzegger, Thiago; Gomes, Gabe; Narayan, Alison R. H.
• 署名单位: University of Michigan System; University of Michigan; Carnegie Mellon University; University of Michigan System; University of Michigan; Universidade Federal de Santa Maria (UFSM); Carnegie Mellon University; Carnegie Mellon University; Carnegie Mellon University
• 刊物名称: NATURE
• ISSN/ISSBN: 0028-0836; 1476-4687
• DOI: 10.1038/s41586-025-09519-5
• 发表日期: 2025-10-02

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 alpha-ketoglutarate (alpha-KG)/Fe(ii)-dependent enzymes for a given substrate or, conversely, for ranking potential substrates for a given alpha-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.