Quantification of binding affinity of glyconanomaterials with lectins

Abstract

Carbohydrate-mediated interactions are involved in many cellular activities including immune responses and infections. These interactions are relatively weak, and as such, cells employ multivalency, i.e., the presentation of multiple monovalent carbohydrate ligands within a close proximity, for cooperative binding thus drastically enhanced binding affinity. In the past two decades, the field of glyconanomaterials has emerged where nanomaterials are used as multivalent scaffolds to present multiple copies of carbohydrate ligands on the nanomaterial surface. At the core of glyconanomaterial research is the ability to control and modulate multivalency through ligand display. For the quantitative evaluation of multivalency, the binding affinity must be determined. Quantification of the binding parameters provides insights for not only the fundamental glyconanomaterial-lectin interactions, but also the rational design of effective diagnostics and therapeutics. Several methods have been developed to determine the binding affinity of glyconanomaterials with lectins, including fluorescence competitive assays in solution or on microarrays, Förster resonance energy transfer, fluorescence quenching, isothermal titration calorimetry, surface plasmon resonance spectroscopy, quartz crystal microbalance and dynamic light scattering. This Feature Article discusses each of these techniques, as well as how each technique is applied to determine the binding affinity of glyconanomaterials with lectins, and the data analysis. Although the results differed depending on the specific method used, collectively, they showed that nanomaterials as multivalent scaffolds could amplify the binding affinity of carbohydrate-lectin interactions by several orders of magnitude, the extent of which depending on the structure of the carbohydrate ligand, the ligand density, the linker length and the particle size.

Fig. 1

Interaction of glyconanomaterials with lectins.

Fig. 2

(a) Fluorescence competition assay to measure the binding affinity of Man-AuNPs with Con A. Man-AuNPs together with Man were incubated with FITC-Con A for 1 h. Fluorescence of the supernatant was measured after removing the bound FITC-Con A by centrifugation. (b) Two equilibria established in the system: Con A with Man-AuNPs and Con A with Man. (c) Cheng-Prusoff equation. IC₅₀: concentration of Man-AuNPs displaying 50% of Man-FITC-Con A binding; [M]: concentration of Man, K_D: dissociation constant of Man with Con A, and K_d: dissociation constant of Man-AuNPs with Con A. (d) Fluorescence spectra vs. concentration of Man-AuNPs. (e) Dose response curve of fluorescence vs. concentration of Man-AuNPs. Reproduced from ref. with permission from The American Chemical Society, copyright 2010.

Fig. 3

(A) Fabrication of super-microarray. (B) Fluorescence competition assays on the super-microarray: (a) fluorescence image after incubating the lectin super-microarray with Man2-FSNPs and varying concentrations of Man2; fluorescence intensity vs. Man2 concentration for (b) CVN^MutDB, and (d) Con A. The insert in (b) was the fluorescence image of the spots corresponding to the data points on the graph. Reproduced from ref. with permission from ELSEVIER, copyright 2013.

Fig. 4

(a) FRET probing multivalent interactions between Man-QDs and DC-SIGN/R. CRD: carbohydrate recognition domain. (b) FRET ratio vs. concentration of DC-SIGN/R, and fitting to the Hill equation. (c-f) Fluorescence spectra of Man-QDs after binding with Atto 594-labeled DC-SIGN/R. Reproduced from ref. with permission from Wiley-VCH, copyright 2016.

Fig. 5

(a) (a) Fluorescence spectra of varying concentrations of Atto-594-labeled DC-SIGN without (solid lines) and with (dotted lines) 1 equivalent of DiMan-AuNP at 590 nm excitation. (b) K_d of different DC-SIGN/R and glyco-AuNP pairs. (c) QE (%) vs. DC-SIGN concentration fitted to the Hill equation. (d) QE (%) vs. DC-SIGNR concentration fitted to the Hill equation. Reproduced from ref. with permission from The American Chemical Society, copyright 2020.

Fig. 6

ITC titration graphs of Man-AuNP with (a) Con A, (b) PNA. The solid squares were experimental data, and lines were theoretical fits. Reproduced from ref. with permission from The American Chemical Society, copyright 2012.

Fig. 7

SPR sensograms monitoring the association (0–180 s) and dissociation of (180 – 360 s) of GNP-6 binding with PA-IL. Reproduced from ref. with permission from Wiley-VCH, copyright 2012.

Fig. 8

(a) Synthesis of glycol-AuNPs. (b) Con A immobilized on the sensor surface. (c) Binding kinetics monitoring the changes in frequency (ΔF) vs. time with different concentrations of Man-AuNPs. (d) Changes in frequency (ΔF) with the build-up of the multilayer. Reproduced from ref. and with permission from The Royal Society of Chemistry, copyright 2010 and 2013.

Fig. 9

(a) Synthesis of Man-PNIPAm NPs. (b) QCM sensor chip configuration. (c) QCM binding parameters. Reproduced from ref. with permission from The American Chemical Society, copyright 2012.

Fig. 10

(a) Principle of using DLS to study glyco-NPs and lectin interactions. (b) DLS graphs and TEM images (scale bars: 50 nm) of Man-SNPs treated with varying concentrations of Con A. (c) Increase in particle size (ΔD) of Man-SNPs after adding varying concentration of Con A. (d) Increase in particle size (ΔD) of Gal-SNPs after adding varying concentration of RCA₁₂₀. In (c) and (d), the circles were experimental data and lines were the Hill fitting curves. Reproduced from ref. with permission from The Royal Society of Chemistry, copyright, 2011.