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.
journal_name
Cell Repjournal_title
Cell reportsauthors
Nimrod G,Fischman S,Austin M,Herman A,Keyes F,Leiderman O,Hargreaves D,Strajbl M,Breed J,Klompus S,Minton K,Spooner J,Buchanan A,Vaughan TJ,Ofran Ydoi
10.1016/j.celrep.2018.10.081subject
Has Abstractpub_date
2018-11-20 00:00:00pages
2121-2131.e5issue
8issn
2211-1247pii
S2211-1247(18)31685-1journal_volume
25pub_type
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