Chemical neighborhood exploration for substrate discovery in biocatalysis

Predicting the substrate reactivity strength for a given biocatalyst remains a central challenge in computational biocatalysis. Here, we present Subdate, a modular workflow that combines descriptor-guided organization of substrate analogs with ab initio metadynamics simulations to prioritize reactive candidates. The workflow integrates i) substrate library construction, ii) conformer generation and descriptors set definition, iii) library clustering, iv) representative-substrate selection, and v) reaction-barrier quantitative prediction. Applied to selected biocatalysts (human butyrylcholinesterase and the catalytic antibody A17) sharing an SN2 reaction mechanism, Subdate quantitatively identifies reactivity trends that match experimental kinetic measurements. The developed workflow provides a mechanism-aware strategy for reactive substrate prioritization for efficient sampling through the chemical library in biocatalysis.