Data-Driven Digital Twin for Amylosucrase from Deinococcus geothermalis: Accelerating Acceptor Discovery in Transglycosylation Reactions.
TL;DR
A data-driven "digital twin" using deep learning-based Molecular Embeddings (MolE) and L1-regularized logistic regression to decode these structural determinants of enzymatic transglycosylation, providing a promising ligand-based virtual screening approach for identifying novel acceptors in enzymatic transglycosylation.
Abstract
Enzymatic transglycosylation by Deinococcus geothermalis amylosucrase (DgAS) offers a cost-effective route for enhancing the physicochemical properties of bioactive flavonoids. However, predicting DgAS acceptor specificity remains challenging due to complex structure-activity relationships. Here, we developed a data-driven "digital twin" using deep learning-based Molecular Embeddings (MolE) and L1-regularized logistic regression to decode these structural determinants. Training on experimentally validated substrates and physicochemical decoys, the MolE-based classifier achieved a 98.4% F1-score and 97.6% accuracy, outperforming traditional molecular fingerprints. SHAP analysis revealed a push-pull recognition mechanism, rewarding planar aglycone cores while penalizing steric constraints. Predictive power was validated through in vitro screening, identifying novel active acceptors (e.g., morin, 65.56% yield) and filtering out nonbinders like pinocembrin. Though constrained by substrate chemical stability, this acceptor-centric framework reduces trial-and-error, providing a promising ligand-based virtual screening approach for identifying novel acceptors in enzymatic transglycosylation.