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Cell Rep. 2018 Nov 20;25(8):2121-2131.e5. doi: 10.1016/j.celrep.2018.10.081.

Computational Design of Epitope-Specific Functional Antibodies.

Author information

1
Biolojic Design, Ltd., 12 Hamada Street, Rehovot 7670314, Israel.
2
Antibody Discovery and Protein Engineering, MedImmune, Granta Park, Cambridge CB21 6GH, UK.
3
AstraZeneca R&D, Darwin Building Cambridge Science Park, Milton Road, Cambridge CB4 0WG, UK.
4
Biolojic Design, Ltd., 12 Hamada Street, Rehovot 7670314, Israel; The Goodman Faculty of Life Sciences, Nanotechnology Building, Bar Ilan University, Ramat Gan 52900, Israel. Electronic address: yanay@ofranlab.org.

Abstract

The ultimate goal of protein design is to introduce new biological activity. We propose a computational approach for designing functional antibodies by focusing on functional epitopes, integrating large-scale statistical analysis with multiple structural models. Machine learning is used to analyze these models and predict specific residue-residue contacts. We use this approach to design a functional antibody to counter the proinflammatory effect of the cytokine interleukin-17A (IL-17A). X-ray crystallography confirms that the designed antibody binds the targeted epitope and the interaction is mediated by the designed contacts. Cell-based assays confirm that the antibody is functional. Importantly, this approach does not rely on a high-quality 3D model of the designed complex or even a solved structure of the target. As demonstrated here, this approach can be used to design biologically active antibodies, removing some of the main hurdles in antibody design and in drug discovery.

KEYWORDS:

IL-17A; antibody design; in silico protein design; protein modeling

PMID:
30463010
DOI:
10.1016/j.celrep.2018.10.081
[Indexed for MEDLINE]
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