Sequence enrichment profiles enable target-agnostic antibody generation for a broad range of antigens
(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.
(Less)
- author
- Mattsson, Jenny
LU
; Ljungars, Anne
LU
; Carlsson, Anders
; Svensson, Carolin
; Nilsson, Björn
LU
; Ohlin, Mats
LU
and Frendéus, Björn
- organization
- publishing date
- 2023
- 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
-
- scopus:85159579321
- pmid:37323567
- 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
- 2025-01-26 05:22:14
@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}}, }