Discovery of Surface-Engineered Nanoparticles That Boost Enzyme Activity via High-Throughput Screening and Machine Learning

Abstract

Nanoparticles (NPs) are known to enhance the activity of enzymes, but such findings remain largely empirical, lacking predictive design principles. Here, the first high-throughput platform for the discovery of surface-engineered nanoparticles (SENs) that modulate enzyme function is introduced. Guided by the hypothesis that surface ligands are primary drivers of activity enhancement, a library of 194 gold- and palladium-based SENs functionalized with diverse peptide ligands is synthesized. These SENs are screened against three model enzymes: cytochrome c, lactoperoxidase (LPO), and lipase. Multiple SENs substantially increased enzymatic activity, with the most effective achieving ≈19-fold increase. The resulting dataset enabled the training of a machine learning model that identified key ligand features associated with high-performing SENs, establishing a predictive framework for designing activity-enhancing NPs. Mechanistic studies confirm that the ligand shell plays a dominant role in driving enhancement, suggesting that effective ligands identified via this approach can be readily transferred across NP platforms. To demonstrate functional relevance, it is shown that an optimized SEN/LPO pair outperforms LPO in inhibiting the growth of multidrug-resistant bacteria and disrupting biofilm formation. Collectively, this work offers a scalable and generalizable method to map and harness nanoscale structure-function relationships at biointerfaces, with applications in biocatalysis, biosensing, and beyond.

Keywords: antibacterial activity; enzyme activity; high‐throughput; machine learning; nanoparticle.