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Article . 2016
License: Elsevier Non-Commercial
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Article . 2016 . Peer-reviewed
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Article . 2016
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Computationally Designed Bispecific Antibodies using Negative State Repertoires

Authors: Andrew Leaver-Fay; Karen Froning; Brian Kuhlman; F. Huang; Saleema Hassanali; S.J. Demarest; Aaron Chamberlain; +6 Authors

Computationally Designed Bispecific Antibodies using Negative State Repertoires

Abstract

A challenge in the structure-based design of specificity is modeling the negative states, i.e., the complexes that you do not want to form. This is a difficult problem because mutations predicted to destabilize the negative state might be accommodated by small conformational rearrangements. To overcome this challenge, we employ an iterative strategy that cycles between sequence design and protein docking in order to build up an ensemble of alternative negative state conformations for use in specificity prediction. We have applied our technique to the design of heterodimeric CH3 interfaces in the Fc region of antibodies. Combining computationally and rationally designed mutations produced unique designs with heterodimer purities greater than 90%. Asymmetric Fc crystallization was able to resolve the interface mutations; the heterodimer structures confirmed that the interfaces formed as designed. With these CH3 mutations, and those made at the heavy-/light-chain interface, we demonstrate one-step synthesis of four fully IgG-bispecific antibodies.

Keywords

Models, Molecular, Computational Biology, Crystallography, X-Ray, Protein Engineering, Immunoglobulin Fc Fragments, Molecular Docking Simulation, Protein Domains, Structural Biology, Immunoglobulin G, Antibodies, Bispecific, Mutation, Protein Multimerization, Immunoglobulin Heavy Chains, Molecular Biology

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    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    57
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
Powered by OpenAIRE graph
citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
57
Top 10%
Top 10%
Top 10%
hybrid