EBioMedicine (Feb 2024)

Integrating artificial intelligence-based epitope prediction in a SARS-CoV-2 antibody discovery pipeline: caution is warrantedResearch in context

  • Delphine Diana Acar,
  • Wojciech Witkowski,
  • Magdalena Wejda,
  • Ruifang Wei,
  • Tim Desmet,
  • Bert Schepens,
  • Sieglinde De Cae,
  • Koen Sedeyn,
  • Hannah Eeckhaut,
  • Daria Fijalkowska,
  • Kenny Roose,
  • Sandrine Vanmarcke,
  • Anne Poupon,
  • Dirk Jochmans,
  • Xin Zhang,
  • Rana Abdelnabi,
  • Caroline S. Foo,
  • Birgit Weynand,
  • Dirk Reiter,
  • Nico Callewaert,
  • Han Remaut,
  • Johan Neyts,
  • Xavier Saelens,
  • Sarah Gerlo,
  • Linos Vandekerckhove

Journal volume & issue
Vol. 100
p. 104960

Abstract

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Summary: Background: SARS-CoV-2-neutralizing antibodies (nABs) showed great promise in the early phases of the COVID-19 pandemic. The emergence of resistant strains, however, quickly rendered the majority of clinically approved nABs ineffective. This underscored the imperative to develop nAB cocktails targeting non-overlapping epitopes. Methods: Undertaking a nAB discovery program, we employed a classical workflow, while integrating artificial intelligence (AI)-based prediction to select non-competing nABs very early in the pipeline. We identified and in vivo validated (in female Syrian hamsters) two highly potent nABs. Findings: Despite the promising results, in depth cryo-EM structural analysis demonstrated that the AI-based prediction employed with the intention to ensure non-overlapping epitopes was inaccurate. The two nABs in fact bound to the same receptor-binding epitope in a remarkably similar manner. Interpretation: Our findings indicate that, even in the Alphafold era, AI-based predictions of paratope-epitope interactions are rough and experimental validation of epitopes remains an essential cornerstone of a successful nAB lead selection. Funding: Full list of funders is provided at the end of the manuscript.

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