A Host-Guest System for Determining Residue Contributions to Protein Adsorption on Nanoparticles by NMR

Friday, June 12, 2020

Md. Siddik Alom

Department of Chemistry

Mississippi State University

Friday, June 12, 2020
10:00 AM
Hand Lab 1144 and Webex


Nanoparticles have become increasingly useful in the fields of drug delivery and biosensing. In these applications, nanoparticles are frequently exposed to biological fluids, where proteins will spontaneously adsorb to the nanoparticle surface. This adsorption can interfere with the intended nanoparticle function, and rigorous, predictive models for understanding protein-nanoparticle adsorption do not currently exist. This is especially true when nanoparticles are exposed to a mixture of proteins. Our group has recently developed a novel NMR-based approach to study the mechanism of competitive protein-nanoparticle binding. In this current project, we present a predictive host-guest system for quantifying each residue’s contribution to nanoparticle binding. This system uses GB3, a small model protein whose binding properties have been studied extensively. Initial studies revealed that lysine at position 13 plays a crucial role in the adsorption of GB3 to 15 nm citrate-coated spherical gold nanoparticles (AuNPs). Therefore, we have constructed a library of K13X GB3 variants, where X has been replaced with each of the 19 amino acid residues. To measure each residue’s relative binding propensity, we examine its affinity for AuNPs relative to K13G GB3, a variant with effectively no side chain. Our initial findings confirm that basic residues (Arg) interact more favorably with AuNPs than other polar (Asn) and acidic side (Glu) chains, and a rank-ordering of side-chain affinity for AuNP surfaces could be inferred in further studies. A simple mechanism can be used to interpret these rankings with respect to thermodynamic vs. kinetic control in future studies. Ultimately, this work will improve our ability to predict adsorption behavior, and future work will test whether this host-guest approach can be extended to make quantitative predictions for other proteins.

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