Integrating artificial intelligence-based epitope prediction in a SARS-CoV-2 antibody discovery pipeline: caution is warranted.
EBioMedicine
; 100: 104960, 2024 Feb.
Article
em En
| MEDLINE
| ID: mdl-38232633
ABSTRACT
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.Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
SARS-CoV-2
/
COVID-19
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Limite:
Animals
/
Female
/
Humans
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article