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research article

Simulation and Theory of Antibody Binding to Crowded Antigen-Covered Surfaces

De Michele, Cristiano
•
De Los Rios, Paolo  
•
Foffi, Giuseppe  
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2016
Plos Computational Biology

In this paper we introduce a fully flexible coarse-grained model of immunoglobulin G (IgG) antibodies parametrized directly on cryo-EM data and simulate the binding dynamics of many IgGs to antigens adsorbed on a surface at increasing densities. Moreover, we work out a theoretical model that allows to explain all the features observed in the simulations. Our combined computational and theoretical framework is in excellent agreement with surface-plasmon resonance data and allows us to establish a number of important results. (i) Internal flexibility is key to maximize bivalent binding, flexible IgGs being able to explore the surface with their second arm in search for an available hapten. This is made clear by the strongly reduced ability to bind with both arms displayed by artificial IgGs designed to rigidly keep a prescribed shape. (ii) The large size of IgGs is instrumental to keep neighboring molecules at a certain distance (surface repulsion), which essentially makes antigens within reach of the second Fab always unoccupied on average. (iii) One needs to account independently for the thermodynamic and geometric factors that regulate the binding equilibrium. The key geometrical parameters, besides excluded-volume repulsion, describe the screening of free haptens by neighboring bound antibodies. We prove that the thermodynamic parameters govern the low-antigen-concentration regime, while the surface screening and repulsion only affect the binding at high hapten densities. Importantly, we prove that screening effects are concealed in relative measures, such as the fraction of bivalently bound antibodies. Overall, our model provides a valuable, accurate theoretical paradigm beyond existing frameworks to interpret experimental profiles of antibodies binding to multivalent surfaces of different sorts in many contexts.

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Type
research article
DOI
10.1371/journal.pcbi.1004752
Web of Science ID

WOS:000376583800040

Author(s)
De Michele, Cristiano
De Los Rios, Paolo  
Foffi, Giuseppe  
Piazza, Francesco
Date Issued

2016

Publisher

Public Library Science

Published in
Plos Computational Biology
Volume

12

Issue

3

Article Number

e1004752

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
GR-FO  
Available on Infoscience
July 19, 2016
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/127436
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