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Supramolecular polymerization of sulfated dendritic peptide amphiphiles into multivalent L-selectin binders.

ABSTRACT: The synthesis of a sulfate-modified dendritic peptide amphiphile and its self-assembly into one-dimensional rod-like architectures in aqueous medium is reported. The influence of the ionic strength on the supramolecular polymerization was probed via circular dichroism spectroscopy and cryogenic transmission electron microscopy. Physiological salt concentrations efficiently screen the charges of the dendritic building block equipped with eight sulfate groups and trigger the formation of rigid supramolecular polymers. Since multivalent sulfated supramolecular structures mimic naturally occurring L-selectin ligands, the corresponding affinity was evaluated using a competitive SPR binding assay and benchmarked to an ethylene glycol-decorated supramolecular polymer.

SUBMITTER: Straßburger D

PROVIDER: S-EPMC7814183 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Supramolecular polymerization of sulfated dendritic peptide amphiphiles into multivalent L-selectin binders.

Straßburger David D Herziger Svenja S Huth Katharina K Urschbach Moritz M Haag Rainer R Besenius Pol P

Beilstein journal of organic chemistry 20210112

The synthesis of a sulfate-modified dendritic peptide amphiphile and its self-assembly into one-dimensional rod-like architectures in aqueous medium is reported. The influence of the ionic strength on the supramolecular polymerization was probed via circular dichroism spectroscopy and cryogenic transmission electron microscopy. Physiological salt concentrations efficiently screen the charges of the dendritic building block equipped with eight sulfate groups and trigger the formation of rigid sup ...[more]

PMID: 33519996

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Project description:Multivalent display on linear platforms is used by many biomolecular systems to effectively interact with their corresponding binding partners in a dose-responsive and ultrasensitive manner appropriate to the biological system at hand. Synthetic supramolecular multivalent displays offer a matching approach for the modular and bottom-up construction and systematic study of dynamic 1D materials. Fundamental studies into multivalent interactions between such linear, 1D materials have been lacking because of the absence of appropriate modular nanoplatforms. In this work we interfaced two synthetic multivalent linear nanoplatforms based on a dynamic supramolecular polymer, formed by hybrid discotic-oligonucleotide monomers, and a series of complementary DNA-duplex-based multivalent ligands, also with appended short oligonucleotides. The combination of these two multivalent nanoplatforms provides for the first time entry to study multivalent effects in dynamic 1D systems, of relevance for the conceptual understanding of multivalency in biology and for the generation of novel multivalent biomaterials. Together the two nanoscaffolds provide easy access to libraries of multivalent ligands with tunable affinities. The DNA scaffold allows for exact control over valency and spatial ligand distribution, and the discotic supramolecular polymer allows for dynamic adaptation and control over receptor density. The interaction between the two nanoplatforms was studied as a function of ligand interaction strength, valency, and density. Usage of the enhancement parameter β allowed quantification of the effects of ligand valency and affinity. The results reveal a generalized principle of additive binding increments. Receptor density is shown to be crucially and nonlinearly correlated to complex formation, leading to ultrasensitive responses. The results reveal that, not unlike biomolecular signaling, high density multivalent display of receptors is crucial for functionally increased affinities.

Dataset Information

Supramolecular polymerization of sulfated dendritic peptide amphiphiles into multivalent L-selectin binders.

Publications

Supramolecular polymerization of sulfated dendritic peptide amphiphiles into multivalent L-selectin binders.

Similar Datasets

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