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Analysis of single nucleotide variations (SNVs) in DNA and RNA sequences is instrumental in healthcare for the detection of genetic and infectious diseases and drug-resistant pathogens. Here we took advantage of the developments in DNA nanotechnology to design a hybridization sensor, named the ‘owl sensor’, which produces a fluorescence signal only when it complexes with fully complementary DNA or RNA analytes. The novelty of the owl sensor operation is that the selectivity of analyte recognition is, at least in part, determined by the structural rigidity and stability of the entire DNA nanostructure rather than exclusively by the stability of the analyte–probe duplex, as is the case for conventional hybridization probes. Using two DNA and two RNA analytes we demonstrated that owl sensors differentiate SNVs in a wide temperature range of 5 °C–32 °C, a performance unachievable by conventional hybridization probes including the molecular beacon probe. The owl sensor reliably detects cognate analytes even in the presence of 100 times excess of single base mismatched sequences. The approach, therefore, promises to add to the toolbox for the diagnosis of SNVs at ambient temperatures.
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Molecular simulations of analyte partitioning and diffusion in liquid crystal sensors
Chemoresponsive liquid crystal (LC) sensors are promising platforms for the detection of vapor-phase analytes. Understanding the transport of analyte molecules within LC films could guide the design of LC sensors with improved selectivity. In this work, we use molecular dynamics simulations to quantify the partitioning and diffusion of nine small-molecule analytes, including four common atmospheric pollutants, in model systems representative of LC sensors. We first parameterize all-atom models for 4-cyano-4′-pentylbiphenyl (5CB), a mesogen typically used for LC sensors, and all analytes. We validate these models by reproducing experimentally determined 5CB structural parameters, 5CB diffusivity, and analyte Henry's law constants in 5CB. Using the all-atom models, we calculate analyte solvation free energies and diffusivities in bulk 5CB. These simulation-derived quantities are then used to parameterize an analytical mass-transport model to predict sensor activation times. These results demonstrate that differences in analyte–LC interactions can translate into distinct activation times to distinguish activation by different analytes. Finally, we quantify the effect of LC composition by calculating analyte solvation free energies in TL205, a proprietary LC mixture. These calculations indicate that varying the LC composition can modulate activation times to further improve sensor selectivity. These results thus provide a computational framework for guiding LC sensor design by using molecular simulations to predict analyte transport as a function of LC composition.
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- Award ID(s):
- 1837812
- PAR ID:
- 10188704
- Date Published:
- Journal Name:
- Molecular Systems Design & Engineering
- Volume:
- 5
- Issue:
- 1
- ISSN:
- 2058-9689
- Page Range / eLocation ID:
- 304 to 316
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1039/C9ME00126C
