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

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. Copyright (c) 2023 Published by Elsevier B.V. This is an open access article under the CC BY -NC -ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/).