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When nanoparticles and nanoplastics enter biological fluids, their surfaces are rapidly coated with proteins, forming a corona that governs biological responses. However, understanding protein- surface interaction energetics remains a significant challenge. Here, we examine how protein charge distribution affects adsorption to polystyrene nanoparticles (PSNPs) by generating a series of lysine-to-alanine variants of the GB3 protein. Using isothermal titration calorimetry (ITC), we found that the K19A variant binds most strongly to both non-functionalized and carboxylate- functionalized PSNPs. ITC thermograms indicate that K19A forms a stable monolayer, while other variants exhibit multilayer adsorption. We hypothesize that removing lysine at position 19 creates a flatter, more neutral interaction surface that promotes efficient initial binding. Fluorescence denaturation experiments show that PSNPs destabilize GB3 protein variants, and binding correlates strongly with protein unfolding (r = 0.82, p < 0.01 for COOH-PSNPs and r = 0.76, p < 0.03 for non-functionalized PSNPs). These results reveal how protein stability and charge distribution shape adsorption thermodynamics, offering a framework for predicting protein-surface interactions. 

Abstract Each year, bovine respiratory disease (BRD) results in significant economic loss in the cattle sector, and novel metabolic profiling for early diagnosis represents a promising tool for developing effective measures for disease management. Here, 1 H-nuclear magnetic resonance ( 1 H-NMR) spectra were used to characterize metabolites from blood plasma collected from male dairy calves (n = 10) intentionally infected with two of the main BRD causal agents, bovine respiratory syncytial virus (BRSV) and Mannheimia haemolytica (MH), to generate a well-defined metabolomic profile under controlled conditions. In response to infection, 46 metabolites (BRSV = 32, MH = 33) changed in concentration compared to the uninfected state. Fuel substrates and products exhibited a particularly strong effect, reflecting imbalances that occur during the immune response. Furthermore, 1 H-NMR spectra from samples from the uninfected and infected stages were discriminated with an accuracy, sensitivity, and specificity ≥ 95% using chemometrics to model the changes associated with disease, suggesting that metabolic profiles can be used for further development, understanding, and validation of novel diagnostic tools. 

Herein, we focus on the design, synthesis, and characterization of thienothiadiazole (TTD)-based near-infrared II (NIR-II) theranostic fluorophores and their nanoparticles. 

Abstract The orientation adopted by proteins on nanoparticle surfaces determines the nanoparticle’s bioactivity and its interactions with living systems. Here, we present a residue-based affinity scale for predicting protein orientation on citrate-gold nanoparticles (AuNPs). Competitive binding between protein variants accounts for thermodynamic and kinetic aspects of adsorption in this scale. For hydrophobic residues, the steric considerations dominate, whereas electrostatic interactions are critical for hydrophilic residues. The scale rationalizes the well-defined binding orientation of the small GB3 protein, and it subsequently predicts the orientation and active site accessibility of two enzymes on AuNPs. Additionally, our approach accounts for the AuNP-bound activity of five out of six additional enzymes from the literature. The model developed here enables high-throughput predictions of protein behavior on nanoparticles, and it enhances our understanding of protein orientation in the biomolecular corona, which should greatly enhance the performance and safety of nanomedicines used in vivo.