EpiScan: accurate high-throughput mapping of antibody-specific epitopes using sequence information.
Journal
NPJ systems biology and applications
ISSN: 2056-7189
Titre abrégé: NPJ Syst Biol Appl
Pays: England
ID NLM: 101677786
Informations de publication
Date de publication:
09 Sep 2024
09 Sep 2024
Historique:
received:
14
10
2023
accepted:
27
08
2024
medline:
10
9
2024
pubmed:
10
9
2024
entrez:
9
9
2024
Statut:
epublish
Résumé
The identification of antibody-specific epitopes on virus proteins is crucial for vaccine development and drug design. Nonetheless, traditional wet-lab approaches for the identification of epitopes are both costly and labor-intensive, underscoring the need for the development of efficient and cost-effective computational tools. Here, EpiScan, an attention-based deep learning framework for predicting antibody-specific epitopes, is presented. EpiScan adopts a multi-input and single-output strategy by designing independent blocks for different parts of antibodies, including variable heavy chain (V
Identifiants
pubmed: 39251627
doi: 10.1038/s41540-024-00432-7
pii: 10.1038/s41540-024-00432-7
doi:
Substances chimiques
Epitopes
0
Antibodies, Viral
0
Spike Glycoprotein, Coronavirus
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
101Informations de copyright
© 2024. The Author(s).
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