Engineering DNA-Functionalized Nanostructures to Bind Nucleic Acid Targets Heteromultivalently with Enhanced Avidity

Abstract

Improving the affinity of nucleic acids to their complements is an important goal for many fields spanning from genomics to antisense therapy and diagnostics. One potential approach to achieving this goal is to use multivalent binding, which often boosts the affinity between ligands and receptors, as exemplified by virus-cell binding and antibody-antigen interactions. Herein, we investigate the binding of heteromultivalent DNA-nanoparticle conjugates, where multiple unique oligonucleotides displayed on a nanoparticle form a multivalent complex with a long DNA target containing the complementary sequences. By developing a strategy to spatially pattern oligonucleotides on a nanoparticle, we demonstrate that the molecular organization of heteromultivalent nanostructures is critical for effective binding; patterned particles have a ∼23 order-of-magnitude improvement in affinity compared to chemically identical particles patterned incorrectly. We envision that nanostructures presenting spatially patterned heteromultivalent DNA will offer important biomedical applications given the utility of DNA-functionalized nanostructures in diagnostics and therapeutics.

Figure 1.. Binding of target to random and patterned heteroMV SNAs.

(a) Schematic illustration depicting homoMV SNAs, heteroMV SNAs, and patterned SNAs binding a target. (b) Top-down perspective of target binding an n=4 heteroMV SNA with random segment arrangement (numbered circles = binding segments, gray line = target). (c) Top-down perspective of target binding an idealized n=6 patterned heteroMV SNA. (d) Modeling results showing the impact of individual segment binding affinity on predicted binding valency for random heteroMV SNAs assuming target concentration is 0.01 (arbitrary units). (e) Modeling results showing the impact of target concentration on predicted binding valency for random heteroMV SNAs assuming K_d = 1. (f) Modeling results showing the impact of segment spatial patterning on binding valency assuming target concentration is 0.01 and K_d = 1.

Figure 2.. Effect of random heteromultivalency on T_m, binding uniformity, and capacity.

(a) Schematic illustration describing melting experiment where excess FAM-labeled target binds to random n=1–6 SNAs and fluorescence is measured as complex is thermally melted. The inset illustrates how T_m and the full width at half maximum (FWHM) is calculated. (b-c) The impact of increasing n on melting temperature (b) and full width half maximum (c) after hybridizing target to SNA in non-stringent buffer (1x PBS). (d-e) The impact of increasing n on melting temperature (d) and full width half maximum (e) after hybridizing target to SNA in stringent buffer (0.1x SSC, 0.2% Tween20). Both sets of melting curves were measured in 4x SSC, 0.2% Tween20 buffer. Error bars represent standard error of the mean. Values were compared using unpaired student T-tests (*P < 0.05; **P < 0.01; ***P < 0.001).

Figure 3.. Effect of random heteromultivalency on thermodynamics and affinity.

(a) Schematic describing experiment to obtain thermodynamic parameters and affinity. Random n=1–6 SNAs are bound to Cy5-labeled target in 1x SSC, 0.2% Tween20 and T_m is measured across a range of [SNA + target] (C_T) values. (b) linear van’t Hoff plots from which thermodynamic values are extracted. (c-e) ΔH (c), -TΔS (d), and -ΔG and log(K_eq) (e) values of random n=1–6 SNAs binding to target. Error bars represent standard error of the mean. Values were compared using one-way ANOVA (****P < 0.0001).

Figure 4.. Characterization and binding analysis of patterned heteroMV SNAs.

(a) Schematic for synthesis of patterned SNAs. After pre-annealing segments 1–6 to the template, the complex is incubated with the AuNP. Next, salt-aging is performed and then the template is dehybridized. (b) Quantifying templates bound per SNA before and after dehybridizing. (c) Targets bound per AuNP for patterned and mispatterned SNAs after high stringency washes. (d-e) van’t Hoff plots (d) and -ΔG and log(K_eq) (e) for patterned, random, and mispatterned n=6 SNAs binding the no-spacer target. The dashed line in (e) represents the predicted -ΔG value (123 kcal/mol) for the non-nicked 81 bp duplex binding in solution. Error bars represent standard error of the mean. Templates/targets bound values were compared using an unpaired student T-test and -ΔG and log(K_eq) values were compared with one-way ANOVA (*P < 0.05; **P < 0.01; ****P < 0.0001).