Computationally-driven identification of antibody epitopes

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Abstract

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.

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Casey K Hua

Thayer School of Engineering, Dartmouth College, Hanover, United States

Competing interests
No competing interests declared.
Albert T Gacerez

Department of Microbiology and Immunology, Geisel School of Medicine, Dartmouth College, Lebanon, United States

Competing interests
No competing interests declared.
Charles L Sentman

Department of Microbiology and Immunology, Geisel School of Medicine, Dartmouth College, Lebanon, United States

Competing interests
No competing interests declared.
Margaret E Ackerman

Thayer School of Engineering, Dartmouth College, Hanover, United States

Competing interests
No competing interests declared.
Yoonjoo Choi

Department of Biological Sciences, Korea Advanced Institute for Science and Technology, Daejeon, Republic of Korea

For correspondence
yoonjoo.choi@kaist.ac.kr

Competing interests
No competing interests declared.

"This ORCID iD identifies the author of this article:" 0000-0001-9687-8093
Chris Bailey-Kellogg

Department of Computer Science, Dartmouth College, Hanover, United States

For correspondence
cbk@cs.dartmouth.edu

Competing interests
Chris Bailey-Kellogg, Dartmouth faculty and a co-member of Stealth Biologics, LLC, a Delaware biotechnology company. This author acknowledges that there is a potential financial conflict of interest related to his associations with this company, and he hereby affirms that the data presented in this paper is free of any bias. This work has been reviewed and approved as specified in Chris Bailey-Kellogg's Dartmouth conflict of interest management plans..

"This ORCID iD identifies the author of this article:" 0000-0003-1860-0912

Funding

National Institutes of Health (R01 GM098977)

Chris Bailey-Kellogg

National Research Foundation of Korea (2016H1D3A1938246)

Yoonjoo Choi

National Science Foundation (CNS-1205521)

Chris Bailey-Kellogg

National Institutes of Health (5F30 AI122970-02)

Casey K Hua

National Institutes of Health (1R01AI102691)

Margaret E Ackerman

Center of Biomedical Research Excellence (8P30GM103415)

Charles L Sentman
Margaret E Ackerman

Allan U. Munck Education and Research Fund at Dartmouth

Charles L Sentman
Margaret E Ackerman

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.