SOURCE-LINKED INTELLIGENCE

Tailoring Rieske Oxygenase Function through Electron Transfer Mapping and ML-Guided Design for Enhanced Biocatalysis

CORDIS · observation · Publication date unknown

incomplete understanding of ET mechanisms and protein–protein interactions (PPIs). TROF-EM will address these challenges by integrating enzyme discovery, structural biology, computational modelling, machine learning (ML), protein engineering, and biochemical analysis to decipher how ET and PPIs govern RO activity and selectivity, thereby producing a first-in-class RO biocatalyst tailored for industrial application. To achieve this, TROF-EM pursues three objectives: (i) investigate how molecular recognition, active-site architecture, and PPIs affect ET efficiency and catalysis; (ii) apply ML-guided mutagenesis to generate RO variants with improved efficiency and reduced uncoupling; and (iii) develop a self-sufficient fused RO with enhanced catalytic performance. TROF-EM will foster interdisciplinary collaboration, supported by leading experts in biocatalysis, structural biology, and computational chemistry, to generate insights into ET in multi-component enzymes and deliver novel biocatalysts for efficient late-stage functionalisation of complex drug scaffolds, offering greener, cost

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recordType

award

status

SIGNED

region

EU

value

232916.16

unit

EUR