Rapid discovery and evolution of nanosensors containing fluorogenic amino acids

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.

Fig. 1. A platform to discover, evolve, and produce FgAA-containing NS.

The multi-staged NS engineering platform enables rapid discovery, evolution, characterization, and cost-effective manufacturing of optical NS via FgAAs. a Hundreds of NS candidates are produced and screened in around 3 weeks by derivatizing protein binders with fluorogens. b This process also identifies key structural features for designing and synthesizing retrosynthetic FgAAs. c An optimized genetic code expansion chemistry enables cell-free, ribosomal production of functional NS by site-specific protein incorporation of FgAAs and simultaneous characterization of NS candidates without purification. d The genetic NS construction offers a rapid (~3 h) discovery strategy utilizing parallelized screening of nanobody scan libraries containing FgAAs (indicated as X) at every position. e Cell-free translation of NS also allows the development of a directed evolution pipeline to select improved NS for specific targets. Retrosynthetic nsAA designs allow hits from a, d, and e to be produced at scale in a two-step semisynthetic NS production approach (>20 mg from 1 L Escherichia coli culture). Figure created with help from BioRender.com released under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International license (https://creativecommons.org/licenses/by-nc-nd/4.0/deed.en).

Fig. 2. A streamlined knowledge-based pipeline to discover and produce binding-activated NS via retrosynthetic FgAAs.

a Crystal structure of the VHH72 nanobody (gray) bound to its target, RBD_CoV19 (purple). The VHH72 cysteine or lysine substitutions are color-coded in a, c, and d. b Structures and abbreviated names of reactive fluorogenic probes used in this work. c, d Fold fluorescence increases from the screening of the VHH72 variants conjugated with the library of cysteine (c) and lysine (d) reactive probes. e This knowledge-based discovery approach is generalizable to other protein scaffolds and molecular targets. Maximum fold increases of different NS in the presence of their corresponding antigens over buffer controls: H11-NS is a biosensor for RBD_CoV19, sdAb-NS for SARS-CoV-2 nucleocapsid, LCB3-NS for RBD_CoV19, ALFA-NS for ALFA peptide, EGFR-NS for human epidermal growth factor receptor (EGFR), Cortisol-NS for the small molecule cortisol. Bars and error bars represent the average and standard deviation of independent triplicate experiments. f Chemical structures of privileged retrosynthetic FgAAs. Source data are provided as a Source Data file.

Fig. 3. Cov19-NS and ALFA-NS enable rapid detection of specific antigens and their imaging in live cells.

a CoV19-NS detects RBD_CoV19 in both serum and PBS. The dose-response curve of the second-best performing CoV19 nanosensor, VHH72 G56MDCcC is like that of CoV19-NS. Each of the relative fluorescence units in the graph was normalized relative to the maximum response of CoV19-NS. b Upon subtraction of buffer blank values, CoV19-NS exhibited >100-fold fluorescence increases at λ_exc/λ_em 420 nm/492 nm after binding RBD_CoV19. Raw fluorescence values are shown in Supplementary Fig. 4. c CoV19-NS ratiometrically detects its target in <1 second with no significant fluorescence increases observed in PBS or BSA. Lines and dashed lines represent the average and standard deviation of independent triplicate experiments. d CoV19-NS enables wash-free imaging of fixed and permeabilized HEK 293 T cells specifically expressing RBD_CoV19 in situ. Scale bar: 20 μm. e, f ALFA-NS can be used for live-cell imaging of ALFA-tag labeled protein A in Staphylococcus aureus (e) or real-time imaging of OmpX export in E. coli (f). Scale bar: 2 μm. Dots represent independent measurements. Lines represent a 4PL fit of the dose-response curves. Shaded areas and dashed lines represent the 95% confidence intervals of the fits. Bars and error bars represent the average and standard deviation of independent triplicate experiments. Data from d–f are representative of two, and three biological replicates, respectively. Data from d–f are representative of more than triplicate independent measurements. The microscopy images are adjusted so that a quantitative comparison can be made within each panel. Source data are provided as a Source Data file.

Fig. 4. Cell-free site-specific translation of FgAAs can accelerate the discovery and functional characterization of NS.

a The optimized chemical synthesis of the nsAA-pdCpA intermediate as compared to standard methods. b Cell-free translated ALFA-NS (via chemoenzymatically synthesized aNBDC-tRNA_CUA) enables direct, live-cell imaging of ALFA-tag labeled OmpX in E. coli despite the presence of excess aNBDC-tRNA_CUA. The same with DHFR 2aNBDC does not result in a similar uniform envelope labeling. c Ribosomally synthesized CoV19-NS can detect RBD_CoV19 in real-time in the reaction mixture. Panel created with help from BioRender.com released under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International license (https://creativecommons.org/licenses/by-nc-nd/4.0/deed.en). d Target-dependent fluorescence increase of ribosomally synthesized NS variants. Cell-free translation reactions were set up with a linear DNA library encoding the TAG scan of VHH72, NBDxK-tRNA_CUA, and RBD_CoV19. Fluorescence measurements of these variants were carried out without purification, and fluorescence fold increases were calculated by comparing values at the 2-hour time point to those at time zero. This unbiased screen recapitulated CoV19-NS and led to the discovery of additional RBD_CoV19 nanosensors, such as VHH72 Y32NBDxK or VHH72 S57NBDxK. Lines and shaded areas represent the average and standard deviation of triplicate measurements. Scale bar: 4 μm. Source data are provided as a Source Data file.

Fig. 5. Selection of improved NS from FgAA-containing nanobody libraries.

a The experimental strategy to evolve nanobodies containing nsAAs by mRNA/cDNA display. Panel created with help from BioRender.com released under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International license (https://creativecommons.org/licenses/by-nc-nd/4.0/deed.en). b NS sequences were enriched when selecting for the RBD_OB.1 antigen. c Dose response curves with evolved NS (Omicron-NS-1, 2, and 3) showed high affinity and fluorescence fold improvements relative to CoV19-NS when exposed to RBD_OB.1. Each of the relative fluorescence units in the graph were normalized relative to the maximum response of Omicron-NS-2 for comparisons of relative NS brightness. Lines represent a 4PL fit of the dose-response curves. Shaded areas and dashed lines represent the 95% confidence intervals of the fits. Bars and error bars represent the average and standard deviation of independent triplicate experiments. Source data are provided as a Source Data file.