PubAg

Main content area

Multivalent Recognition at Fluid Surfaces: The Interplay of Receptor Clustering and Superselectivity

Author:
Dubacheva, Galina V., Curk, Tine, Frenkel, Daan, Richter, Ralf P.
Source:
Journal of the American Chemical Society 2019 v.141 no.6 pp. 2577-2588
ISSN:
1520-5126
Subject:
binding capacity, ligands, mathematical models, physicochemical properties, prediction, receptors
Abstract:
The interaction between a biological membrane and its environment is a complex process, as it involves multivalent binding between ligand/receptor pairs, which can self-organize in patches. Any description of the specific binding of biomolecules to membranes must account for the key characteristics of multivalent binding, namely, its unique ability to discriminate sharply between high and low receptor densities (superselectivity), but also for the effect of the lateral mobility of membrane-bound receptors to cluster upon binding. Here we present an experimental model system that allows us to compare systematically the effects of multivalent interactions on fluid and immobile surfaces. A crucial feature of our model system is that it allows us to control the membrane surface chemistry, the properties of the multivalent binder, and the binding affinity. We find that multivalent probes retain their superselective binding behavior at fluid interfaces. Supported by numerical simulations, we demonstrate that, as a consequence of receptor clustering, superselective binding is enhanced and shifted to lower receptor densities at fluid interfaces. To translate our findings into a simple, predictive tool, we propose an analytical model that enables rapid predictions of how the superselective binding behavior is affected by the lateral receptor mobility as a function of the physicochemical characteristics of the multivalent probe. We believe that our model, which captures the key physical mechanisms underpinning multivalent binding to biological membranes, will greatly facilitate the rational design of nanoprobes for the superselective targeting of cells.
Agid:
6308318