A New in Silico Antibody Similarity Measure Both Identifies Large Sets of Epitope Binders with Distinct CDRs and Accurately Predicts Off-Target Reactivity

Astrid Musnier, Thomas Bourquard, Amandine Vallet, Laetitia Mathias, Gilles Bruneau, Mohammed Akli Ayoub, Ophélie Travert, Yannick Corde, Nathalie Gallay, Thomas Boulo, Sandra Cortes, Hervé Watier, Pascale Crépieux, Eric Reiter, Anne Poupon

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Developing a therapeutic antibody is a long, tedious, and expensive process. Many obstacles need to be overcome, such as biophysical properties (issues of solubility, stability, weak production yields, etc.), as well as cross-reactivity and subsequent toxicity, which are major issues. No in silico method exists today to solve such issues. We hypothesized that if we were able to properly measure the similarity between the CDRs of antibodies (Ab) by considering not only their evolutionary proximity (sequence identity) but also their structural features, we would be able to identify families of Ab recognizing similar epitopes. As a consequence, Ab within the family would share the property to recognize their targets, which would allow (i) to identify off-targets and forecast the cross-reactions, and (ii) to identify new Ab specific for a given target. Testing our method on 238D2, an antagonistic anti-CXCR4 nanobody, we were able to find new nanobodies against CXCR4 and to identify influenza hemagglutinin as an off-target of 238D2.

Original languageEnglish
Article number9765
JournalInternational journal of molecular sciences
Volume23
Issue number17
DOIs
Publication statusPublished - Sep 2022
Externally publishedYes

Keywords

  • antibody repurposing
  • in silico method
  • off-target
  • poly-specificity
  • therapeutic antibody

ASJC Scopus subject areas

  • Catalysis
  • Molecular Biology
  • Spectroscopy
  • Computer Science Applications
  • Physical and Theoretical Chemistry
  • Organic Chemistry
  • Inorganic Chemistry

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