AbAgIntPre: A deep learning method for predicting antibody-antigen interactions based on sequence information.
Front Immunol
; 13: 1053617, 2022.
Artigo
em Inglês
| MEDLINE | ID: covidwho-2198894
ABSTRACT
Introduction:
Antibody-mediated immunity is an essential part of the immune system in vertebrates. The ability to specifically bind to antigens allows antibodies to be widely used in the therapy of cancers and other critical diseases. A key step in antibody therapeutics is the experimental identification of antibody-antigen interactions, which is generally time-consuming, costly, and laborious. Although some computational methods have been proposed to screen potential antibodies, the dependence on 3D structures still limits the application of these methods.Methods:
Here, we developed a deep learning-assisted prediction method (i.e., AbAgIntPre) for fast identification of antibody-antigen interactions that only relies on amino acid sequences. A Siamese-like convolutional neural network architecture was established with the amino acid composition encoding scheme for both antigens and antibodies. Results andDiscussion:
The generic model of AbAgIntPre achieved satisfactory performance with the Area Under Curve (AUC) of 0.82 on a high-quality generic independent test dataset. Besides, this approach also showed competitive performance on the more specific SARS-CoV dataset. We expect that AbAgIntPre can serve as an important complement to traditional experimental methods for antibody screening and effectively reduce the workload of antibody design. The web server of AbAgIntPre is freely available at http//www.zzdlab.com/AbAgIntPre.Palavras-chave
Texto completo:
Disponível
Coleções:
Bases de dados internacionais
Base de dados:
MEDLINE
Assunto principal:
Aprendizado Profundo
Tipo de estudo:
Estudo prognóstico
Limite:
Animais
Idioma:
Inglês
Revista:
Front Immunol
Ano de publicação:
2022
Tipo de documento:
Artigo
País de afiliação:
Fimmu.2022.1053617
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