Academic Journal
When two are better than one: Modeling the mechanisms of antibody mixtures.
| Title: | When two are better than one: Modeling the mechanisms of antibody mixtures. |
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| Authors: | Tal Einav, Jesse D Bloom |
| Source: | PLoS Computational Biology, Vol 16, Iss 5, p e1007830 (2020) |
| Publisher Information: | Public Library of Science (PLoS), 2020. |
| Publication Year: | 2020 |
| Collection: | LCC:Biology (General) |
| Subject Terms: | Biology (General), QH301-705.5 |
| More Details: | 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. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 1553-734X 1553-7358 |
| Relation: | https://doaj.org/toc/1553-734X; https://doaj.org/toc/1553-7358 |
| DOI: | 10.1371/journal.pcbi.1007830 |
| Access URL: | https://doaj.org/article/992e534f31ec40588d56c8a25ceb5d1a |
| Accession Number: | edsdoj.992e534f31ec40588d56c8a25ceb5d1a |
| Database: | Directory of Open Access Journals |
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| ISSN: | 1553734X 15537358 |
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| DOI: | 10.1371/journal.pcbi.1007830 |
| Published in: | PLoS Computational Biology |
| Language: | English |