Rapid discovery and evolution of nanosensors containing fluorogenic amino acids

Binding-activated optical sensors are powerful tools for imaging, diagnostics, and biomolecular sensing. However, biosensor discovery is slow and requires tedious steps in rational design, screening, and characterization. Here we report on a platform that streamlines biosensor discovery and unlocks directed nanosensor evolution through genetically encodable fluorogenic amino acids (FgAAs). Building on the classical knowledge-based semisynthetic approach, we engineer ~15 kDa nanosensors that recognize specific proteins, peptides, and small molecules with up to 100-fold fluorescence increases and subsecond kinetics, allowing real-time and wash-free target sensing and live-cell bioimaging. An optimized genetic code expansion chemistry with FgAAs further enables rapid (~3 h) ribosomal nanosensor discovery via the cell-free translation of hundreds of candidates in parallel and directed nanosensor evolution with improved variant-specific sensitivities (up to ~250-fold) for SARS-CoV-2 antigens. Altogether, this platform could accelerate the discovery of fluorogenic nanosensors and pave the way to modify proteins with other non-standard functionalities for diverse applications.

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Publication Details

Journal
Nature Communications
Published
2024-09-05
DOI
https://doi.org/10.1038/s41467-024-50956-z
Citations
7
Primary Topic
Advanced biosensing and bioanalysis techniques
Type
article
Field-Weighted Citation Impact
1.64

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article

Rapid discovery and evolution of nanosensors containing fluorogenic amino acids

Jason D. Galpin, William Bedell, Thomas M. Bartlett, Fabio De Moliner et al.
7 citations
Nature Communications
Advanced biosensing and bioanalysis techniques
1.64
article

Rapid discovery and evolution of nanosensors containing fluorogenic amino acids

Jason D. Galpin, William Bedell, Thomas M. Bartlett, Fabio De Moliner, Isaac Han, Marc Vendrell, Helena de Puig, Subhrajit Rout, Abigail E Reese, Lindsey R. Robinson‐McCarthy, Christopher A. Ahern, Jonathan Rittichier, Allison Flores, Jorge A. Marchand, James J. Collins, David Z. Rudner, Thomas G. Bernhardt, Erkin Kuru, George M. Church, Sylvie G. Bernier
article en
7 citations

Abstract

Binding-activated optical sensors are powerful tools for imaging, diagnostics, and biomolecular sensing. However, biosensor discovery is slow and requires tedious steps in rational design, screening, and characterization. Here we report on a platform that streamlines biosensor discovery and unlocks directed nanosensor evolution through genetically encodable fluorogenic amino acids (FgAAs). Building on the classical knowledge-based semisynthetic approach, we engineer ~15 kDa nanosensors that recognize specific proteins, peptides, and small molecules with up to 100-fold fluorescence increases and subsecond kinetics, allowing real-time and wash-free target sensing and live-cell bioimaging. An optimized genetic code expansion chemistry with FgAAs further enables rapid (~3 h) ribosomal nanosensor discovery via the cell-free translation of hundreds of candidates in parallel and directed nanosensor evolution with improved variant-specific sensitivities (up to ~250-fold) for SARS-CoV-2 antigens. Altogether, this platform could accelerate the discovery of fluorogenic nanosensors and pave the way to modify proteins with other non-standard functionalities for diverse applications.

Nature CommunicationsVol. 15(1)
Broad Institute (US), University of Iowa (US), New York State Department of Health (US), Wadsworth Center (US), Howard Hughes Medical Institute (US), Harvard University (US), University of Washington (US), Dana-Farber/Harvard Cancer Center (US), Pediatrics and Genetics (US), Centre for Inflammation Research (GB), Center for Systems Biology (US), Massachusetts Institute of Technology (US)
U.S. Department of Energy, Life Sciences Research Foundation, Hansjörg Wyss Institute for Biologically Inspired Engineering, Harvard University
Zero hunger
Openalex Percentile: Top 16%
Advanced biosensing and bioanalysis techniques
1.64
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