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Stochastic modeling of antibody binding predicts programmable migration on antigen patterns
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology. Division of Biomaterials, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Solna, Sweden.ORCID iD: 0000-0001-6941-4576
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2022 (English)In: Nature Computational Science, ISSN 2662-8457, Vol. 2, no 3, p. 179-192Article in journal (Refereed) Published
Abstract [en]

Viruses and bacteria commonly exhibit spatial repetition of the surface molecules that directly interface with the host immune system. However, the complex interaction of patterned surfaces with immune molecules containing multiple binding domains is poorly understood. We developed a pipeline for constructing mechanistic models of antibody interactions with patterned antigen substrates. Our framework relies on immobilized DNA origami nanostructures decorated with precisely placed antigens. The results revealed that antigen spacing is a spatial control parameter that can be tuned to influence the antibody residence time and migration speed. The model predicts that gradients in antigen spacing can drive persistent, directed antibody migration in the direction of more stable spacing. These results depict antibody–antigen interactions as a computational system where antigen geometry constrains and potentially directs the antibody movement. We propose that this form of molecular programmability could be exploited during the co-evolution of pathogens and immune systems or in the design of molecular machines. 

Place, publisher, year, edition, pages
Springer Nature , 2022. Vol. 2, no 3, p. 179-192
Keywords [en]
Antigens, Computational geometry, Immune system, Molecules, Stochastic systems, Viruses, Antibody binding, Antibody-antigen interactions, Binding domain, Control parameters, Mechanistic models, Patterned surface, Residence time, Spatial control, Stochastic-modeling, Surface molecules, Antibodies
National Category
Biochemistry Molecular Biology
Identifiers
URN: urn:nbn:se:kth:diva-322399DOI: 10.1038/s43588-022-00218-zISI: 000888203500014PubMedID: 36311262Scopus ID: 2-s2.0-85127251590OAI: oai:DiVA.org:kth-322399DiVA, id: diva2:1718921
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QC 20221214

Available from: 2022-12-14 Created: 2022-12-14 Last updated: 2025-02-20Bibliographically approved

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Hoffecker, Ian T.

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CiteExportLink to record
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