Exploiting Receptors Competition to Enhance Nanoparticles Binding Selectivity

TL;DR

This paper presents a statistical mechanical model demonstrating how receptor competition can be exploited to improve the selectivity of ligand-functionalized nanoparticles, reducing non-specific binding in biological applications.

Contribution

It introduces a novel model showing how receptor competition and multivalent effects can be used to design nanoparticles with enhanced binding selectivity.

Findings

Receptor competition can be harnessed to improve nanoparticle selectivity.

Multivalent effects influence binding strength and specificity.

The model predicts conditions for minimizing non-specific interactions.

Abstract

Nanoparticles functionalized with multiple ligands can be programmed to bind biological targets depending on the receptors they express, providing a general mechanism exploited in various technologies, from selective drug-delivery to biosensing. For binding to be highly selective, ligands should exclusively interact with specific targeted receptors, because formation of bonds with other, untargeted ones would lead to non-specific binding and potentially harmful behaviour. This poses a particular problem for multivalent nanoparticles, because even very weak bonds can collectively lead to strong binding. A statistical mechanical model is used here to describe how competition between different receptors together with multivalent effects can be harnessed to design ligand-functionalized nanoparticles insensitive to the presence of untargeted receptors, are insensitive to these problems, preventing non-specific binding.