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

• DOI: 10.1016/j.ebiom.2023.104960
• Publication Date: 2024-01-21T01:22:29Z
• Authors: 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
• Keywords: Epitope mapping; Paratope

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.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.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.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.Full list of funders is provided at the end of the manuscript.