Plug-and-play protein biosensors using aptamer-regulated in vitro transcription.


Journal

bioRxiv : the preprint server for biology
Titre abrégé: bioRxiv
Pays: United States
ID NLM: 101680187

Informations de publication

Date de publication:
14 Aug 2023
Historique:
pubmed: 30 8 2023
medline: 30 8 2023
entrez: 30 8 2023
Statut: epublish

Résumé

Molecular biosensors that accurately measure protein concentrations without external equipment are critical for solving numerous problems in diagnostics and therapeutics. Modularly transducing the binding of protein antibodies, protein switches or aptamers into a useful output remains challenging. Here, we develop a biosensing platform based on aptamer-regulated transcription in which aptamers integrated into transcription templates serve as inputs to molecular circuits that can be programmed to a produce a variety of responses. We modularly design molecular biosensors using this platform by swapping aptamer domains for specific proteins and downstream domains that encode different RNA transcripts. By coupling aptamer-regulated transcription with diverse transduction circuits, we rapidly construct analog protein biosensors or digital protein biosensors with detection ranges that can be tuned over two orders of magnitude. Aptamer-regulated transcription is a straightforward and inexpensive approach for constructing programmable protein biosensors suitable for diverse research and diagnostic applications.

Identifiants

pubmed: 37645783
doi: 10.1101/2023.08.10.552680
pmc: PMC10461910
pii:
doi:

Types de publication

Preprint

Langues

eng

Déclaration de conflit d'intérêts

Competing Interests The authors declare no competing interests.

Auteurs

Heonjoon Lee (H)

Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21218.

Tian Xie (T)

Biochemistry and Molecular Biology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205.

Xinjie Yu (X)

Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218.

Samuel W Schaffter (SW)

National Institute of Standards and Technology, Gaithersburg, MD 20899, USA.

Rebecca Schulman (R)

Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218.
Computer Science, Johns Hopkins University, Baltimore, MD 21218.

Classifications MeSH