We report here a colorimetric method for rapid detection of norovirus based on the valence-driven peptide-AuNP interactions. We engineered a peptide sequence named K1 with a cleavage sequence in between two lysine residues. The positively charged lysine groups aggregated the negatively charged nanoparticles leading to a purple color change. There was a red color when the cleavage sequence was digested by the Southampton norovirus 3C-like protease (SV3CP)-a protease involved in the life cycle of Human norovirus (HNV). The limit of detection was determined to be 320 nM in Tris buffer. We further show that the sensor has good performance in exhaled breath condensate, urine, and faecal matter. This research provides a potential easy and quick way to selectively detect HNV.
Colorimetric biosensors based on gold nanoparticle (AuNP) aggregation are often challenged by matrix interference in biofluids, poor specificity, and limited utility with clinical samples. Here, we propose a peptide-driven nanoscale disassembly approach, where AuNP aggregates induced by electrostatic attractions are dissociated in response to proteolytic cleavage. Initially, citrate-coated AuNPs were assembled via a short cationic peptide (RRK) and characterized by experiments and simulations. The dissociation peptides were then used to reversibly dissociate the AuNP aggregates as a function of target protease detection, i.e., main protease (Mpro), a biomarker for severe acute respiratory syndrome coronavirus 2. The dissociation propensity depends on peptide length, hydrophilicity, charge, and ligand architecture. Finally, our dissociation strategy provides a rapid and distinct optical signal through Mpro cleavage with a detection limit of 12.3 nM in saliva. Our dissociation peptide effectively dissociates plasmonic assemblies in diverse matrices including 100% human saliva, urine, plasma, and seawater, as well as other types of plasmonic nanoparticles such as silver. Our peptide-enabled dissociation platform provides a simple, matrix-insensitive, and versatile method for protease sensing.
Aromatic interactions are commonly involved in the assembly of naturally occurring building blocks, and these interactions can be replicated in an artificial setting to produce functional materials. Here we describe a colorimetric biosensor using co-assembly experiments with plasmonic gold and surfactant-like peptides (SLPs) spanning a wide range of aromatic residues, polar stretches, and interfacial affinities. The SLPs programmed in DDD-(ZZ)
Noroviruses are highly contagious and are one of the leading causes of acute gastroenteritis worldwide. Due to a lack of effective antiviral therapies, there is a need to diagnose and surveil norovirus infections to implement quarantine protocols and prevent large outbreaks. Currently, the gold standard of diagnosis uses reverse transcription polymerase chain reaction (RT-PCR), but PCR can have limited availability. Here, we propose a combination of a tunable peptide substrate and gold nanoparticles (AuNPs) to colorimetrically detect the Southampton norovirus 3C-like protease (SV3CP), a key protease in viral replication. Careful design of the substrate employs a zwitterionic peptide with opposite charged moieties on the C- and N- termini to induce a rapid color change visible to the naked eye; thus, this color change is indicative of SV3CP activity. This work expands on existing zwitterionic peptide strategies for protease detection by systematically evaluating the effects of lysine and arginine on nanoparticle charge screening. We also determine a limit of detection for SV3CP of 28.0 nM with comparable results in external breath condensate, urine, and fecal matter for 100 nM of SV3CP. The key advantage of this system is its simplicity and accessibility, thus making it an attractive tool for qualitative point-of-care diagnostics.
Abstract Aromatic interactions are commonly involved in the assembly of naturally occurring building blocks, and these interactions can be replicated in an artificial setting to produce functional materials. Here we describe a colorimetric biosensor using co‐assembly experiments with plasmonic gold and surfactant‐like peptides (SLPs) spanning a wide range of aromatic residues, polar stretches, and interfacial affinities. The SLPs programmed in DDD−(ZZ) x −FFPC self‐assemble into higher‐order structures in response to a protease and subsequently modulate the colloidal dispersity of gold leading to a colorimetric readout. Results show the strong aggregation propensity of the FFPC tail without polar DDD head. The SLPs were specific to the target protease, i.e., M pro , a biomarker for SARS‐CoV‐2. This system is a simple and visual tool that senses M pro in phosphate buffer, exhaled breath condensate, and saliva with detection limits of 15.7, 20.8, and 26.1 nM, respectively. These results may have value in designing other protease testing methods.
Better insights into the fate of membraneless organelles could strengthen the understanding of the transition from prebiotic components to multicellular organisms. Compartmentalized enzyme reactions in a synthetic coacervate have been investigated, yet there remains a gap in understanding the enzyme interactions with coacervate as a substrate hub. Here, we study how the molecularly crowded nature of the coacervate affects the interactions of the embedded substrate with a protease. We design oligopeptide-based coacervates that comprise an anionic Asp-peptide (D10) and a cationic Arg-peptide (R5R5) with a proteolytic cleavage site. The coacervates dissolve in the presence of the main protease (Mpro) implicated in the coronavirus lifecycle. We capitalize on the condensed structure, introduce a self-quenching mechanism, and model the enzyme kinetics by using Cy5.5-labeled peptides. The determined specificity constant (kcat/KM) is 5817 M–1 s–1 and is similar to that of the free substrate. We further show that the enzyme kinetics depend on the type and quantity of dye incorporated into the coacervates. Our work presents a simple design for enzyme-responsive coacervates and provides insights into the interactions between the enzyme and coacervates as a whole.
This study aims to restore grating lobe artifacts and improve the image resolution of sparse array ultrasonography via a deep learning predictive model. A deep learning assisted sparse array was developed using only 64 or 16 channels out of the 128 channels in which the pitch is two or eight times the original array. The deep learning assisted sparse array imaging system was demonstrated on ex vivo porcine teeth. 64- and 16-channel sparse array images were used as the input and corresponding 128-channel dense array images were used as the ground truth. The structural similarity index measure, mean squared error, and peak signal-to-noise ratio of predicted images improved significantly (p < 0.0001). The resolution of predicted images presented close values to ground truth images (0.18 mm and 0.15 mm versus 0.15 mm). The gingival thickness measurement showed a high level of agreement between the predicted sparse array images and the ground truth images, as indicated with a bias of -0.01 mm and 0.02 mm for the 64- and 16-channel predicted images, respectively, and a Pearson’s r = 0.99 (p < 0.0001) for both. The gingival thickness bias measured by deep learning assisted sparse array imaging and clinical probing needle was found to be <0.05 mm. Additionally, the deep learning model showed capability of generalization. To conclude, the deep learning assisted sparse array can reconstruct high-resolution ultrasound image using only 16 channels of 128 channels. The deep learning model performed generalization capability for the 64-channel array, while the 16-channel array generalization would require further optimization.
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