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Sequence enrichment profiles enable target-agnostic antibody generation for a broad range of antigens

Mattsson, Jenny LU ; Ljungars, Anne LU ; Carlsson, Anders ; Svensson, Carolin ; Nilsson, Björn LU ; Ohlin, Mats LU orcid and Frendéus, Björn (2023) In Cell reports methods 3(5).
Abstract

Phenotypic drug discovery (PDD) enables the target-agnostic generation of therapeutic drugs with novel mechanisms of action. However, realizing its full potential for biologics discovery requires new technologies to produce antibodies to all, a priori unknown, disease-associated biomolecules. We present a methodology that helps achieve this by integrating computational modeling, differential antibody display selection, and massive parallel sequencing. The method uses the law of mass action-based computational modeling to optimize antibody display selection and, by matching computationally modeled and experimentally selected sequence enrichment profiles, predict which antibody sequences encode specificity for disease-associated... (More)

Phenotypic drug discovery (PDD) enables the target-agnostic generation of therapeutic drugs with novel mechanisms of action. However, realizing its full potential for biologics discovery requires new technologies to produce antibodies to all, a priori unknown, disease-associated biomolecules. We present a methodology that helps achieve this by integrating computational modeling, differential antibody display selection, and massive parallel sequencing. The method uses the law of mass action-based computational modeling to optimize antibody display selection and, by matching computationally modeled and experimentally selected sequence enrichment profiles, predict which antibody sequences encode specificity for disease-associated biomolecules. Applied to a phage display antibody library and cell-based antibody selection, ∼105 antibody sequences encoding specificity for tumor cell surface receptors expressed at 103–106 receptors/cell were discovered. We anticipate that this approach will be broadly applicable to molecular libraries coupling genotype to phenotype and to the screening of complex antigen populations for identification of antibodies to unknown disease-associated targets.

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author
; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
biomarkers, computational biology, CP: immunology, mathematical modeling, phage display, phenotypic antibody discovery, specificity predictions, therapeutic antibodies
in
Cell reports methods
volume
3
issue
5
article number
100475
publisher
Cell Press
external identifiers
  • pmid:37323567
  • scopus:85159579321
ISSN
2667-2375
DOI
10.1016/j.crmeth.2023.100475
language
English
LU publication?
yes
id
051e22dc-8d3f-47fa-88a4-52f2fcbab824
date added to LUP
2023-08-22 10:33:38
date last changed
2024-04-20 01:12:54
@article{051e22dc-8d3f-47fa-88a4-52f2fcbab824,
  abstract     = {{<p>Phenotypic drug discovery (PDD) enables the target-agnostic generation of therapeutic drugs with novel mechanisms of action. However, realizing its full potential for biologics discovery requires new technologies to produce antibodies to all, a priori unknown, disease-associated biomolecules. We present a methodology that helps achieve this by integrating computational modeling, differential antibody display selection, and massive parallel sequencing. The method uses the law of mass action-based computational modeling to optimize antibody display selection and, by matching computationally modeled and experimentally selected sequence enrichment profiles, predict which antibody sequences encode specificity for disease-associated biomolecules. Applied to a phage display antibody library and cell-based antibody selection, ∼10<sup>5</sup> antibody sequences encoding specificity for tumor cell surface receptors expressed at 10<sup>3</sup>–10<sup>6</sup> receptors/cell were discovered. We anticipate that this approach will be broadly applicable to molecular libraries coupling genotype to phenotype and to the screening of complex antigen populations for identification of antibodies to unknown disease-associated targets.</p>}},
  author       = {{Mattsson, Jenny and Ljungars, Anne and Carlsson, Anders and Svensson, Carolin and Nilsson, Björn and Ohlin, Mats and Frendéus, Björn}},
  issn         = {{2667-2375}},
  keywords     = {{biomarkers; computational biology; CP: immunology; mathematical modeling; phage display; phenotypic antibody discovery; specificity predictions; therapeutic antibodies}},
  language     = {{eng}},
  number       = {{5}},
  publisher    = {{Cell Press}},
  series       = {{Cell reports methods}},
  title        = {{Sequence enrichment profiles enable target-agnostic antibody generation for a broad range of antigens}},
  url          = {{http://dx.doi.org/10.1016/j.crmeth.2023.100475}},
  doi          = {{10.1016/j.crmeth.2023.100475}},
  volume       = {{3}},
  year         = {{2023}},
}