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Related Concept Videos

Introduction to Enzymes01:22

Introduction to Enzymes

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The use of enzymes by humans dates to 7000 BCE. Humans first used enzymes to ferment sugars and produce alcohol without knowing that this was an enzyme-catalyzed reaction. Wilhelm Kuhne coined the term 'enzyme' in 1877 from the Greek words ‘en’ meaning ‘in’ or ‘within’ and ‘zyme’ meaning ‘yeast.’
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Catalytically Perfect Enzymes01:07

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The theory of catalytically perfect enzymes was first proposed by W.J. Albery and J. R. Knowles in 1976. These enzymes catalyze biochemical reactions at high-speed. Their catalytic efficiency values range from 108-109 M-1s-1. These enzymes are also called 'diffusion-controlled' as the only rate-limiting step in the catalysis is that of the substrate diffusion into the active site. Examples include triose phosphate isomerase, fumarase, and superoxide dismutase.
 
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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)
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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.

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antibacterial activityenzyme activityhigh‐throughputmachine learningnanoparticle

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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.