When two are better than one: Modeling the mechanisms of antibody mixtures

It is difficult to predict how antibodies will behave when mixed together, even after each has been independently characterized. Here, we present a statistical mechanical model for the activity of antibody mixtures that accounts for whether pairs of antibodies bind to distinct or overlapping epitope...

Full description

Saved in:
Bibliographic Details
Published inPLoS computational biology Vol. 16; no. 5; p. e1007830
Main Authors Einav, Tal, Bloom, Jesse D
Format Journal Article
LanguageEnglish
Published United States Public Library of Science 01.05.2020
Public Library of Science (PLoS)
Subjects
Amalgams
Antibodies
Antigen-antibody reactions
Biology and Life Sciences
Cancer
Cell adhesion & migration
Competition
Engineering and Technology
Epidermal growth factor
Epidermal growth factor receptors
Epitopes
Experiments
Growth factor receptors
Growth factors
Health aspects
Influenza
Kinases
Ligands
Mathematical models
Medical research
Medicine and Health Sciences
Molecular interactions
Monoclonal antibodies
Physiological aspects
Research and Analysis Methods
Structure
United States
United States > US
Online AccessGet full text

Cover

More Information
Summary:It is difficult to predict how antibodies will behave when mixed together, even after each has been independently characterized. Here, we present a statistical mechanical model for the activity of antibody mixtures that accounts for whether pairs of antibodies bind to distinct or overlapping epitopes. This model requires measuring n individual antibodies and their [Formula: see text] pairwise interactions to predict the 2n potential combinations. We apply this model to epidermal growth factor receptor (EGFR) antibodies and find that the activity of antibody mixtures can be predicted without positing synergy at the molecular level. In addition, we demonstrate how the model can be used in reverse, where straightforward experiments measuring the activity of antibody mixtures can be used to infer the molecular interactions between antibodies. Lastly, we generalize this model to analyze engineered multidomain antibodies, where components of different antibodies are tethered together to form novel amalgams, and characterize how well it predicts recently designed influenza antibodies.
Bibliography:new_version
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
The authors have declared that no competing interests exist.
ISSN:1553-7358
1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1007830