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Discovery of Surface-Engineered Nanoparticles That Boost Enzyme Activity via High-Throughput Screening and Machine
Yuanjie Sun1, Subrata Pandit1, Neha Satish1
1Department of Chemistry, The University of Texas at Austin, 105 E 24th St., Austin, TX, 78712, USA.
Small (Weinheim an Der Bergstrasse, Germany)
|August 26, 2025
Summary
Researchers developed a high-throughput platform to design surface-engineered nanoparticles (SENs) that boost enzyme activity. Machine learning identified key features for predictive design, enabling new applications in biocatalysis and combating drug-resistant bacteria.
Area of Science:
- Nanotechnology and Materials Science
- Biochemistry and Enzymology
- Machine Learning and Computational Biology
Background:
- Nanoparticles (NPs) can enhance enzyme activity, but current methods are empirical, lacking predictive design principles.
- Understanding the structure-function relationships at bio-nanoparticle interfaces is crucial for developing advanced biocatalysts and biosensors.
Purpose of the Study:
- To introduce the first high-throughput platform for discovering surface-engineered nanoparticles (SENs) that modulate enzyme function.
- To establish a predictive framework for designing activity-enhancing NPs by identifying key surface ligand features.
- To demonstrate the functional relevance of optimized SEN-enzyme pairs in combating multidrug-resistant bacteria.
Main Methods:
- Synthesized a library of 194 gold- and palladium-based SENs functionalized with diverse peptide ligands.
- Screened SENs against three model enzymes (cytochrome c, lactoperoxidase (LPO), and lipase) using a high-throughput platform.
- Trained a machine learning model on the resulting dataset to identify features correlating with enhanced enzymatic activity.
Main Results:
- Identified multiple SENs that substantially increased enzymatic activity, with the most effective achieving a ≈19-fold enhancement.
- The machine learning model successfully identified key ligand features predictive of high-performing SENs.
- Mechanistic studies confirmed the dominant role of the ligand shell in driving activity enhancement, transferable across NP platforms.
Conclusions:
- Developed a scalable and generalizable method for mapping and harnessing nanoscale structure-function relationships at biointerfaces.
- The predictive framework enables rational design of NPs for enhanced enzyme activity, with broad applications in biocatalysis and biosensing.
- An optimized SEN/LPO pair demonstrated superior efficacy against multidrug-resistant bacteria and biofilm formation compared to LPO alone.
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