Polyethylene glycol (PEG) surface conjugations are widely employed to render passivating properties to nanoparticles in biological applications. The benefits of surface passivation by PEG are reduced protein adsorption, diminished non-specific interactions, and improvement in pharmacokinetics. However, the limitations of PEG passivation remain an active area of research, and recent examples from the literature demonstrate how PEG passivation can fail. Here, we study the adsorption amount of biomolecules to PEGylated gold nanoparticles (AuNPs), focusing on how different protein properties influence binding. The AuNPs are PEGylated with three different sizes of conjugated PEG chains, and we examine interactions with proteins of different sizes, charges, and surface cysteine content. The experiments are carried out in vitro at physiologically relevant timescales to obtain the adsorption amounts and rates of each biomolecule on AuNP-PEGs of varying compositions. Our findings are relevant in understanding how protein size and the surface cysteine content affect binding, and our work reveals that cysteine residues can dramatically increase adsorption rates on PEGylated AuNPs. Moreover, shorter chain PEG molecules passivate the AuNP surface more effectively against all protein types.
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Predicting protein function and orientation on a gold nanoparticle surface using a residue-based affinity scale
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.
more » « less- Award ID(s):
- 1818090
- NSF-PAR ID:
- 10382047
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Nature Communications
- Volume:
- 13
- Issue:
- 1
- ISSN:
- 2041-1723
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Results We propose a novel, accurate and fast sequence-based predictor of PBRs that minimizes the cross-predictions. Our SCRIBER (SeleCtive pRoteIn-Binding rEsidue pRedictor) method takes advantage of three innovations: comprehensive dataset that covers multiple types of binding residues, novel types of inputs that are relevant to the prediction of PBRs, and an architecture that is tailored to reduce the cross-predictions. The dataset includes complete protein chains and offers improved coverage of binding annotations that are transferred from multiple protein–protein complexes. We utilize innovative two-layer architecture where the first layer generates a prediction of protein-binding, RNA-binding, DNA-binding and small ligand-binding residues. The second layer re-predicts PBRs by reducing overlap between PBRs and the other types of binding residues produced in the first layer. Empirical tests on an independent test dataset reveal that SCRIBER significantly outperforms current predictors and that all three innovations contribute to its high predictive performance. SCRIBER reduces cross-predictions by between 41% and 69% and our conservative estimates show that it is at least 3 times faster. We provide putative PBRs produced by SCRIBER for the entire human proteome and use these results to hypothesize that about 14% of currently known human protein domains bind proteins.
Availability and implementation SCRIBER webserver is available at http://biomine.cs.vcu.edu/servers/SCRIBER/.
Supplementary information Supplementary data are available at Bioinformatics online.
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