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Elife. 2017 Dec 4;6. pii: e29023. doi: 10.7554/eLife.29023.

Computationally-driven identification of antibody epitopes.

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Thayer School of Engineering, Dartmouth College, Hanover, United States.
Department of Microbiology and Immunology, Geisel School of Medicine, Dartmouth College, Lebanon, United States.
Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.
Department of Computer Science, Dartmouth College, Hanover, United States.


Understanding where antibodies recognize antigens can help define mechanisms of action and provide insights into progression of immune responses. We investigate the extent to which information about binding specificity implicitly encoded in amino acid sequence can be leveraged to identify antibody epitopes. In computationally-driven epitope localization, possible antibody-antigen binding modes are modeled, and targeted panels of antigen variants are designed to experimentally test these hypotheses. Prospective application of this approach to two antibodies enabled epitope localization using five or fewer variants per antibody, or alternatively, a six-variant panel for both simultaneously. Retrospective analysis of a variety of antibodies and antigens demonstrated an almost 90% success rate with an average of three antigen variants, further supporting the observation that the combination of computational modeling and protein design can reveal key determinants of antibody-antigen binding and enable efficient studies of collections of antibodies identified from polyclonal samples or engineered libraries.


antibody; biophysics; epitope mapping; immunology; none; protein design; protein docking; structural biology

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