Integrating artificial intelligence-based epitope prediction in a SARS-CoV-2 antibody discovery pipeline: caution is warranted.
Covid-19
Epitope mapping
In silico prediction
Neutralizing antibody
SARS-CoV-2
Journal
EBioMedicine
ISSN: 2352-3964
Titre abrégé: EBioMedicine
Pays: Netherlands
ID NLM: 101647039
Informations de publication
Date de publication:
16 Jan 2024
16 Jan 2024
Historique:
received:
01
05
2023
revised:
22
12
2023
accepted:
22
12
2023
medline:
18
1
2024
pubmed:
18
1
2024
entrez:
17
1
2024
Statut:
aheadofprint
Résumé
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.
Sections du résumé
BACKGROUND
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
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
RESULTS
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
CONCLUSIONS
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
BACKGROUND
Full list of funders is provided at the end of the manuscript.
Identifiants
pubmed: 38232633
pii: S2352-3964(23)00526-1
doi: 10.1016/j.ebiom.2023.104960
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
104960Informations de copyright
Copyright © 2023. Published by Elsevier B.V.
Déclaration de conflit d'intérêts
Declaration of interests Ghent University has filed for patent protection on the antibody sequences described herein, and D.D.A., M.W., R.W., W.W., S.G. and L.V. are named as co-inventors on this patent (European Patent Application: 21186206.5). A.P. is employee of the MAbSilico, H.R. holds a patent regarding neutralizing VHH antibodies binding the Spike RBD (PCT/EP2021/052885) and has filed a priority application for neutralizing VHH antibodies binding Spike S2 (EP 23160838.1). X.S. is a recipient of FWO research project COVID-19 (G0G4920N) and FWO-FNRS project VIREOS (EOS ID: 30981113) grants.