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.

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

  • 6,157
    views
  • 901
    downloads
  • 34
    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. Developmental Biology
    2. Structural Biology and Molecular Biophysics

    The co-receptor Tetraspanin12 directly captures Norrin to promote ligand-specific β-catenin signaling

    Elise S Bruguera, Jacob P Mahoney, William I Weis
    Research Article

    Wnt/β-catenin signaling directs animal development and tissue renewal in a tightly controlled, cell- and tissue-specific manner. In the mammalian central nervous system, the atypical ligand Norrin controls angiogenesis and maintenance of the blood-brain barrier and blood-retina barrier through the Wnt/β-catenin pathway. Like Wnt, Norrin activates signaling by binding and heterodimerizing the receptors Frizzled (Fzd) and low-density lipoprotein receptor-related protein 5 or 6 (LRP5/6), leading to membrane recruitment of the intracellular transducer Dishevelled (Dvl) and ultimately stabilizing the transcriptional coactivator β-catenin. Unlike Wnt, the cystine knot ligand Norrin only signals through Fzd4 and additionally requires the co-receptor Tetraspanin12 (Tspan12); however, the mechanism underlying Tspan12-mediated signal enhancement is unclear. It has been proposed that Tspan12 integrates into the Norrin-Fzd4 complex to enhance Norrin-Fzd4 affinity or otherwise allosterically modulate Fzd4 signaling. Here, we measure direct, high-affinity binding between purified Norrin and Tspan12 in a lipid environment and use AlphaFold models to interrogate this interaction interface. We find that Tspan12 and Fzd4 can simultaneously bind Norrin and that a pre-formed Tspan12/Fzd4 heterodimer, as well as cells co-expressing Tspan12 and Fzd4, more efficiently capture low concentrations of Norrin than Fzd4 alone. We also show that Tspan12 competes with both heparan sulfate proteoglycans and LRP6 for Norrin binding and that Tspan12 does not impact Fzd4-Dvl affinity in the presence or absence of Norrin. Our findings suggest that Tspan12 does not allosterically enhance Fzd4 binding to Norrin or Dvl, but instead functions to directly capture Norrin upstream of signaling.

    1. Structural Biology and Molecular Biophysics

    The known unknowns of the Hsp90 chaperone

    Laura-Marie Silbermann, Benjamin Vermeer ... Katarzyna Tych
    Review Article

    Molecular chaperones are vital proteins that maintain protein homeostasis by assisting in protein folding, activation, degradation, and stress protection. Among them, heat-shock protein 90 (Hsp90) stands out as an essential proteostasis hub in eukaryotes, chaperoning hundreds of ‘clients’ (substrates). After decades of research, several ‘known unknowns’ about the molecular function of Hsp90 remain unanswered, hampering rational drug design for the treatment of cancers, neurodegenerative, and other diseases. We highlight three fundamental open questions, reviewing the current state of the field for each, and discuss new opportunities, including single-molecule technologies, to answer the known unknowns of the Hsp90 chaperone.

    1. Structural Biology and Molecular Biophysics

    N-acetylation of α-synuclein enhances synaptic vesicle clustering mediated by α-synuclein and lysophosphatidylcholine

    Chuchu Wang, Chunyu Zhao ... Cong Liu
    Research Advance

    Previously, we reported that α-synuclein (α-syn) clusters synaptic vesicles (SV) Diao et al., 2013, and neutral phospholipid lysophosphatidylcholine (LPC) can mediate this clustering Lai et al., 2023. Meanwhile, post-translational modifications (PTMs) of α-syn such as acetylation and phosphorylation play important yet distinct roles in regulating α-syn conformation, membrane binding, and amyloid aggregation. However, how PTMs regulate α-syn function in presynaptic terminals remains unclear. Here, based on our previous findings, we further demonstrate that N-terminal acetylation, which occurs under physiological conditions and is irreversible in mammalian cells, significantly enhances the functional activity of α-syn in clustering SVs. Mechanistic studies reveal that this enhancement is caused by the N-acetylation-promoted insertion of α-syn’s N-terminus and increased intermolecular interactions on the LPC-containing membrane. N-acetylation in our work is shown to fine-tune the interaction between α-syn and LPC, mediating α-syn’s role in synaptic vesicle clustering.