1. Structural Biology and Molecular Biophysics
  2. Immunology and Inflammation

Computationally-driven identification of antibody epitopes

  1. Casey K Hua
  2. Albert T Gacerez
  3. Charles L Sentman
  4. Margaret E Ackerman
  5. Yoonjoo Choi  Is a corresponding author
  6. Chris Bailey-Kellogg  Is a corresponding author
  1. Dartmouth College, United States
  2. Korea Advanced Institute for Science and Technology, Republic of Korea
https://doi.org/10.7554/eLife.29023
Share this article
Cite this article
  1. Casey K Hua
  2. Albert T Gacerez
  3. Charles L Sentman
  4. Margaret E Ackerman
  5. Yoonjoo Choi
  6. Chris Bailey-Kellogg
(2017)
Computationally-driven identification of antibody epitopes
eLife 6:e29023.
https://doi.org/10.7554/eLife.29023
  2. Download BibTeX
  3. Download .RIS

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.

Article and author information

Author details

  1. Casey K Hua

    Thayer School of Engineering, Dartmouth College, Hanover, United States
    Competing interests
    No competing interests declared.

  2. Albert T Gacerez

    Department of Microbiology and Immunology, Geisel School of Medicine, Dartmouth College, Lebanon, United States
    Competing interests
    No competing interests declared.

  3. Charles L Sentman

    Department of Microbiology and Immunology, Geisel School of Medicine, Dartmouth College, Lebanon, United States
    Competing interests
    No competing interests declared.

  4. Margaret E Ackerman

    Thayer School of Engineering, Dartmouth College, Hanover, United States
    Competing interests
    No competing interests declared.

  5. 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.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9687-8093

  6. 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..
    ORCID icon "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.

Reviewing Editor

  1. Max Vasquez, Adimab Inc., United States

Version history

  1. Received: May 26, 2017
  2. Accepted: December 2, 2017
  3. Accepted Manuscript published: December 4, 2017 (version 1)
  4. Version of Record published: December 21, 2017 (version 2)

Copyright

© 2017, Hua et al.

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.

Metrics

  • 6,020
    views
  • 880
    downloads
  • 32
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Casey K Hua
  2. Albert T Gacerez
  3. Charles L Sentman
  4. Margaret E Ackerman
  5. Yoonjoo Choi
  6. Chris Bailey-Kellogg
(2017)
Computationally-driven identification of antibody epitopes
eLife 6:e29023.
https://doi.org/10.7554/eLife.29023

Share this article

https://doi.org/10.7554/eLife.29023

Categories and tags

Further reading

    1. Structural Biology and Molecular Biophysics

    Plasticity of the proteasome-targeting signal Fat10 enhances substrate degradation

    Hitendra Negi, Aravind Ravichandran ... Ranabir Das
    Research Article

    The proteasome controls levels of most cellular proteins, and its activity is regulated under stress, quiescence, and inflammation. However, factors determining the proteasomal degradation rate remain poorly understood. Proteasome substrates are conjugated with small proteins (tags) like ubiquitin and Fat10 to target them to the proteasome. It is unclear if the structural plasticity of proteasome-targeting tags can influence substrate degradation. Fat10 is upregulated during inflammation, and its substrates undergo rapid proteasomal degradation. We report that the degradation rate of Fat10 substrates critically depends on the structural plasticity of Fat10. While the ubiquitin tag is recycled at the proteasome, Fat10 is degraded with the substrate. Our results suggest significantly lower thermodynamic stability and faster mechanical unfolding in Fat10 compared to ubiquitin. Long-range salt bridges are absent in the Fat10 structure, creating a plastic protein with partially unstructured regions suitable for proteasome engagement. Fat10 plasticity destabilizes substrates significantly and creates partially unstructured regions in the substrate to enhance degradation. NMR-relaxation-derived order parameters and temperature dependence of chemical shifts identify the Fat10-induced partially unstructured regions in the substrate, which correlated excellently to Fat10-substrate contacts, suggesting that the tag-substrate collision destabilizes the substrate. These results highlight a strong dependence of proteasomal degradation on the structural plasticity and thermodynamic properties of the proteasome-targeting tags.

    1. Biochemistry and Chemical Biology
    2. Structural Biology and Molecular Biophysics

    Special Issue: Allosteric regulation of kinase activity

    Amy H Andreotti, Volker Dötsch
    Editorial

    The articles in this special issue highlight how modern cellular, biochemical, biophysical and computational techniques are allowing deeper and more detailed studies of allosteric kinase regulation.

    1. Developmental Biology
    2. Structural Biology and Molecular Biophysics

    Structure and function of the ROR2 cysteine-rich domain in vertebrate noncanonical WNT5A signaling

    Samuel C Griffiths, Jia Tan ... Hsin-Yi Henry Ho
    Research Article Updated

    The receptor tyrosine kinase ROR2 mediates noncanonical WNT5A signaling to orchestrate tissue morphogenetic processes, and dysfunction of the pathway causes Robinow syndrome, brachydactyly B, and metastatic diseases. The domain(s) and mechanisms required for ROR2 function, however, remain unclear. We solved the crystal structure of the extracellular cysteine-rich (CRD) and Kringle (Kr) domains of ROR2 and found that, unlike other CRDs, the ROR2 CRD lacks the signature hydrophobic pocket that binds lipids/lipid-modified proteins, such as WNTs, suggesting a novel mechanism of ligand reception. Functionally, we showed that the ROR2 CRD, but not other domains, is required and minimally sufficient to promote WNT5A signaling, and Robinow mutations in the CRD and the adjacent Kr impair ROR2 secretion and function. Moreover, using function-activating and -perturbing antibodies against the Frizzled (FZ) family of WNT receptors, we demonstrate the involvement of FZ in WNT5A-ROR signaling. Thus, ROR2 acts via its CRD to potentiate the function of a receptor super-complex that includes FZ to transduce WNT5A signals.