Cited by 157

Animal immunization, in vitro display technologies, and machine learning for antibody discovery

Laustsen, Andreas H., Greiff, Victor, Karatt-Vellatt, Aneesh, Muyldermans, Serge, Jenkins, Timothy P.·2021·Trends in Biotechnology

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Crossref · 10.1016/j.tibtech.2021.03.003

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  • Hybridoma technology has relatively long development times when animals must be immunized, compared to in vitro phage display technologies.

    “including relatively long development times when animals must be immunized, compared to in vitro phage display technologies (Laustsen et al., 2021)”

    From Challenges and solutions in transitioning to animal-free standards: a comprehensive analysis of components in human cell-based developmental neurotoxicity assays (2026) · cites this paper as Laustsen et al., 2021

    The 'Antibodies May Derive from Plasma, Hybridomas, and Recombinant Cell Lines' section says hybridomas are derived from immunized animals, and Figure 1 shows immunization in the hybridoma workflow. Neither passage, nor the paper’s comparison of discovery strategies in Table 1, says hybridoma development takes longer than in vitro phage display. (LLM, conf=0.82)

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Research works printed but not linked: 9

  1. Brüggemann, M. et al. (1989) A repertoire of monoclonal antibodies with human heavy chains from transgenic mice. Proc. Natl. Acad. Sci. U. S. A. 86, 6709-6713
  2. Muyldermans, S. (2020) A guide to: generation and design of nanobodies. FEBS J. Published online August 11, 2020. https://doi.org/10.1111/febs.15515 · DOI
  3. Mason, D.M. et al. (2019) Deep learning enables therapeutic antibody optimization in mammalian cells by deciphering high- dimensional protein sequence space. bioRxiv Published online June 2, 2019. https://doi.org/10.1101/617860 · DOI
  4. Chen, X. et al. (2020) Predicting antibody developability from sequence using machine learning. bioRxiv Published online June 20, 2020. https://doi.org/10.1101/2020.06.18.159798 · DOI
  5. Bowley, D.R. et al. (2009) Libraries against libraries for combina- torial selection of replicating antigen-antibody pairs. Proc. Natl. Acad. Sci. U. S. A. 106, 1380-1385
  6. Younger, D. et al. (2017) High-throughput characterization of protein-protein interactions by reprogramming yeast mating. Proc. Natl. Acad. Sci. U. S. A. 114, 12166-12171
  7. Widrich, M. et al. (2020) Modern Hopfield networks and attention for immune repertoire classification. In Advances in Neural Information Processing Systems (Vol. 33), MIT Press
  8. Friedensohn, S. et al. (2020) Convergent selection in antibody repertoires is revealed by deep learning. bioRxiv Published online February 26, 2020. https://doi.org/10.1101/2020.02.25.965673 · DOI
  9. Amimeur, T. et al. (2020) Designing feature-controlled humanoid antibody discovery libraries using generative adversarial networks. bioRxiv Published online April 23, 2020. https://doi.org/10.1101/ 2020.04.12.024844 · DOI

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Authors
Laustsen, A. H., Greiff, V., Karatt-Vellatt, A., Muyldermans, S., & Jenkins, T. P.
Venue
Trends in Biotechnology
Publisher
Elsevier BV
Published
2021-12
Volume
39
Issue
12
Pages
1263-1273
ISSN
0167-7799
Cited by
157
References
8
Language
en
Funders
  • Leona M. and Harry B. Helmsley Charitable Trust
  • Villum Fonden (00025302)
  • European Research Council (850974)
  • Research Council of Norway
  • Horizon 2020

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