Integrating artificial intelligence-based epitope prediction in a SARS-CoV-2 antibody discovery pipeline: caution is warranted - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue EBioMedicine Année : 2024

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

Tim Desmet
Anne Poupon
Linos Vandekerckhove
  • Fonction : Auteur
  • PersonId : 1350277

Résumé

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.
Fichier principal
Vignette du fichier
PIIS2352396423005261.pdf (3.43 Mo) Télécharger le fichier
Origine : Fichiers éditeurs autorisés sur une archive ouverte

Dates et versions

hal-04449723 , version 1 (09-02-2024)

Identifiants

Citer

Delphine Diana Acar, Wojciech Witkowski, Magdalena Wejda, Ruifang Wei, Tim Desmet, et al.. Integrating artificial intelligence-based epitope prediction in a SARS-CoV-2 antibody discovery pipeline: caution is warranted. EBioMedicine, 2024, 100, pp.104960. ⟨10.1016/j.ebiom.2023.104960⟩. ⟨hal-04449723⟩
37 Consultations
18 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More