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.

Figure 1

Illustration of high‐throughput discovery of SENs that boost enzyme activity. A) Overview of the process: synthesis of a SEN library followed by evaluation of its effects on enzyme activity. B) Relative sizes of NP core (with GNP as an example), enzymes, and the PGNPs used in this study. Proteins shown in this figure correspond to crystal structures of CytC (PDB ID: 1HRC), LPO (PDB ID: 3GC1), and LIP (PDB ID: 1YS1). C) Schematic representation of the enzyme catalytic reactions used in this study.

Figure 2

Characterization of the PGNP Library. UV–vis absorbance spectra of representative PGNPs before A) and after B) freeze‐thaw treatment, the data is normalized by setting the maximum absorbance of the SPR peak to a value of 1. C) Comparative ζ‐potential of GNPs and representative PGNPs functionalized by different peptides (PGNP#1: EEEEWGC, pI = 3.58; PGNP#27: LLLLWGC, pI = 5.52; PGNP#55: RRWCGRR, pI = 12.00). Data represent the mean of three independent replicates, with error bars indicating standard deviation. TEM image of D) GNPs, E) PGNP#1, F) PGNP#27, and G) PGNP#55. The scale bar represents 20 nm.

Figure 3

High‐throughput screening of enzyme activity by PGNPs. Absorbance over time curves obtained using top‐performing PGNPs that enhanced the activity of A) CytC, B) LPO, and C) LIP, respectively. The absorbance values at t = 0 are non‐zero in the presence of PGNPs due to the intrinsic absorbance of the GNP core. Representative heat maps of high‐throughput screening results for D) CytC, E) LPO, and F) LIP. Each well corresponds to a unique PGNP. Well G12 represents the native enzyme control (activity set as 1), and well H12 is the substrate‐only control. Blue indicates enhanced activity, red indicates inhibited activity, and white represents activity comparable to the native enzyme.

Figure 4

Mechanistic investigation of activity enhancement by PGNPs. A) ζ‐potential measurements of GNPs, PGNP#55, and PGNP#55 mixed with increasing concentrations of LPO, showing the gradual change in surface charge upon enzyme binding. Data represent the mean of three independent replicates, with error bars indicating standard deviation. B) CD spectra of LPO before and after incubation with PGNP#55 for 1 and 30 min, respectively. Data represent the average of three independent measurements, collected in 0.1 × acetate buffer. C). Quantification of LPO secondary structure components using the BeStSel algorithm based on CD data.^{[ 41 ]} D) 3D structure of LPO (PDB ID: 3GC1), highlighting the asymmetric distribution of surface charge. E) Structure of the heme cavity in LPO. Residues involved in the substrate diffusion channel are shown in green, substrate binding residues in pink, and the heme moiety in blue. Yellow highlights H351, which is located in the proximal heme cavity. F) Heat map of enzyme activity screening for PGNP#95–#100 and additional PGNPs with similar peptide sequences. Each well corresponds to a distinct PGNP. Wells C4 and D4 represent the native enzyme and substrate‐only controls, respectively. In the heap map, blue indicates enhanced activity, red indicates inhibition, and white indicates activity similar to the native enzyme.

Figure 5

Evaluation of the PGNP/LPO system as a bactericidal agent. A) Schematic showing the antibacterial mechanism of the LPO system. B) Representative photographs showing the turbidity of kanamycin‐resistant E. coli cultures under different treatment conditions, 24 h after incubation. C) Representative photographs showing colony formation resulting from bacterial plating from suspensions in (B). D) Growth curves of kanamycin‐resistant E. coli monitored over 24 h under different treatment conditions, measured by OD₆₀₀. E) Quantification of biofilm formation by multidrug‐resistant E. coli under different treatment conditions. Biofilm formation was evaluated using the crystal violet (CV) assay. Biofilms were formed over 24 h, exposed to different treatments after an additional 12 h, and then stained with 0.1% CV for 15 min. The absorbance was measured at 570 nm. (D,E): Data represent the mean of three independent replicates, with error bars indicating standard deviation. Statistical analysis was performed using the one‐tailed Student's t test to evaluate a directional hypothesis. A Bonferroni correction was applied for three comparisons, adjusting the significance threshold to p < 0.0167 to maintain an overall 95% confidence level (^* p < 0.0167). F) Photographs corresponding to different treatment groups in (E). G) SEM images showing morphological changes in bacteria following various treatments.