Optimization of therapeutic antibodies by predicting antigen specificity from antibody sequence via deep learning.
Nat Biomed Eng
; 5(6): 600-612, 2021 06.
Article
em En
| MEDLINE
| ID: mdl-33859386
ABSTRACT
The optimization of therapeutic antibodies is time-intensive and resource-demanding, largely because of the low-throughput screening of full-length antibodies (approximately 1 × 103 variants) expressed in mammalian cells, which typically results in few optimized leads. Here we show that optimized antibody variants can be identified by predicting antigen specificity via deep learning from a massively diverse space of antibody sequences. To produce data for training deep neural networks, we deep-sequenced libraries of the therapeutic antibody trastuzumab (about 1 × 104 variants), expressed in a mammalian cell line through site-directed mutagenesis via CRISPR-Cas9-mediated homology-directed repair, and screened the libraries for specificity to human epidermal growth factor receptor 2 (HER2). We then used the trained neural networks to screen a computational library of approximately 1 × 108 trastuzumab variants and predict the HER2-specific subset (approximately 1 × 106 variants), which can then be filtered for viscosity, clearance, solubility and immunogenicity to generate thousands of highly optimized lead candidates. Recombinant expression and experimental testing of 30 randomly selected variants from the unfiltered library showed that all 30 retained specificity for HER2. Deep learning may facilitate antibody engineering and optimization.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Engenharia de Proteínas
/
Receptor ErbB-2
/
Trastuzumab
/
Aprendizado Profundo
/
Antígenos
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Limite:
Animals
/
Humans
Idioma:
En
Revista:
Nat Biomed Eng
Ano de publicação:
2021
Tipo de documento:
Article
País de afiliação:
Suíça