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Table · dataset · 2020

Data for "Tuning aggregative versus non-aggregative lectin binding with glycosylated nanoparticles by the nature of the polymer ligand"

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Glycan–lectin interactions drive a diverse range of biological signaling and recognition processes.

Description

The display of glycans in multivalent format enables their intrinsically weak binding affinity to lectins to be overcome by the cluster glycoside effect, which results in a non-linear increase in binding affinity. As many lectins have multiple binding sites, upon interaction with glycosylated nanomaterials either aggregation or surface binding without aggregation can occur.

Depending on the application area, either one of these responses are desirable (or undesirable) but methods to tune the aggregation state, independently from the overall extent/affinity of binding are currently missing. Herein, we use gold nanoparticles decorated with galactose-terminated polymer ligands, obtained by photo-initiated RAFT polymerization to ensure high end-group fidelity, to show the dramatic impact on agglutination behaviour due to the chemistry of the polymer linker.

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Poly(N-hydroxyethyl acrylamide) (PHEA)-coated gold nanoparticles, a polymer widely used as a non-ionic stabilizer, showed preference for aggregation with lectins compared to poly(N-(2-hydroxypropyl)methacrylamide) (PHPMA)-coated nanoparticles which retained colloidal stability, across a wide range of polymer lengths and particle core sizes. Using biolayer interferometry, it was observed that both coatings gave rise to similar binding affinity and hence provided conclusive evidence that aggregation rate alone cannot be used to measure affinity between nanoparticle systems with different stabilizing linkers.

This is significant, as turbidimetry is widely used to demonstrate glycomaterial activity, although this work shows the most aggregating may not be the most avid, when comparing different polymer backbones/coating. Overall, our findings underline the potential of PHPMA as the coating of choice for applications where aggregation upon lectin binding would be problematic, such as in vivo imaging or drug delivery.<br><br>--> Software required: Microsoft excel, .vms files, .dsw files, LOG files, OPJU files, ASP files -->Data organisation: Original data for analytical methods is included here.

This is in format of excel spreadsheets where possible.

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Inferred from text
Imaging 75% · Medicinal and biomolecular chemistry 75%
Provenance · 1 source records, 18 field assertions
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