Covalent Dynamic DNA Networks to Translate Multiple Inputs into Programmable Outputs

Inspired by naturally occurring protein dimerization networks, in which a set of proteins interact with each other to achieve highly complex input-output behaviors, we demonstrate here a fully synthetic DNA-based dimerization network that enables highly programmable input-output computations. Our DNA-based dimerization network consists of DNA oligonucleotide monomers modified with reactive moieties that can covalently bond with each other to form dimer outputs in an all-to-all or many-to-many fashion. By designing DNA-based input strands that can specifically sequester DNA monomers, we can control the size of the reaction network and thus fine-tune the yield of each DNA dimer output in a predictable manner. Thanks to the programmability and specificity of DNA-DNA interactions, we show that this approach can be used to control the yield of different dimer outputs using different inputs. The approach is also versatile and we demonstrate dimerization networks based on two distinct covalent reactions: thiol-disulfide and strain-promoted azide-alkyne cycloaddition (SPAAC) reactions. Finally, we show here that the DNA-based dimerization network can be used to control the yield of a functional dimer output, ultimately controlling the assembly and disassembly of DNA nanostructures. The covalent dynamic DNA networks shown here provide a way to convert multiple inputs into programmable outputs that can control a broader range of functions, including ones that mimic those of living cells.

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Publication Details

Journal
Journal of the American Chemical Society
Published
2025-02-05
DOI
https://doi.org/10.1021/jacs.4c13854
Citations
6
Primary Topic
Advanced biosensing and bioanalysis techniques
Type
article
Field-Weighted Citation Impact
4.84

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article

Covalent Dynamic DNA Networks to Translate Multiple Inputs into Programmable Outputs

Serena Gentile, Sijbren Otto, Francesco Ricci, Simone Brannetti et al.
6 citations
Journal of the American Chemical Society
Advanced biosensing and bioanalysis techniques
4.84
article

Covalent Dynamic DNA Networks to Translate Multiple Inputs into Programmable Outputs

Serena Gentile, Sijbren Otto, Francesco Ricci, Simone Brannetti, Erica Del Grosso
article en
6 citations

Abstract

Inspired by naturally occurring protein dimerization networks, in which a set of proteins interact with each other to achieve highly complex input-output behaviors, we demonstrate here a fully synthetic DNA-based dimerization network that enables highly programmable input-output computations. Our DNA-based dimerization network consists of DNA oligonucleotide monomers modified with reactive moieties that can covalently bond with each other to form dimer outputs in an all-to-all or many-to-many fashion. By designing DNA-based input strands that can specifically sequester DNA monomers, we can control the size of the reaction network and thus fine-tune the yield of each DNA dimer output in a predictable manner. Thanks to the programmability and specificity of DNA-DNA interactions, we show that this approach can be used to control the yield of different dimer outputs using different inputs. The approach is also versatile and we demonstrate dimerization networks based on two distinct covalent reactions: thiol-disulfide and strain-promoted azide-alkyne cycloaddition (SPAAC) reactions. Finally, we show here that the DNA-based dimerization network can be used to control the yield of a functional dimer output, ultimately controlling the assembly and disassembly of DNA nanostructures. The covalent dynamic DNA networks shown here provide a way to convert multiple inputs into programmable outputs that can control a broader range of functions, including ones that mimic those of living cells.

Journal of the American Chemical SocietyVol. 147(7)
University of Rome Tor Vergata (IT), University of Groningen (NL)
Associazione Italiana per la Ricerca sul Cancro, Ministero degli Affari Esteri e della Cooperazione Internazionale, H2020 European Research Council
Openalex Percentile: Top 5%
Advanced biosensing and bioanalysis techniques
4.84
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