Tuning molecular fluctuation to boost the conductance in DNA based molecular wires

Hydrogel-based thermosensor using peptide nucleic acid and PEGylated graphene oxide

Developing a ready-to-use miniaturized thermosensor is a great challenge due to its individual use on a large scale for daily business such as food industry and healthcare. Herein, a polyethylene glycol (PEG)-modified graphene oxide (GO)-based hydrogel thermosensor was established with a fluorescent dye-labeled peptide nucleic acid (F-PNA). The size-tunable hydrogel with high water content and sufficient solidity allowed free movement of the oligonucleotides through the pores and improved usability for handling the sensor. In the PEG-GO hydrogel, the DNA/F-PNA duplex could be denatured by increasing the temperature, followed by selective PNA capture on the PEG-GO. Using this principle, the PEG-GO hydrogel exhibited a change in the fluorescence signal of F-PNA in a temperature-dependent manner, allowing real-time visualization of temperature on a large scale. The temperature detection range of this system can be adjusted by designing the PNA strands based on the melting temperature of the DNAzyme/PNA duplex. Its sensing specificity and detection range could be increased and broadened by observing multi-color detection using PNA probes labeled with different fluorescent dyes of different lengths in a single hydrogel. In addition, the hydrogel platform is easy to store for long time periods via dehydration and can be restored with the addition of water, allowing easy transport, storage, and use of the thermosensor in everyday life.

Effects of Structural Dynamics on Charge Carrier Transfer in B-DNA: A Combined MD and RT-TDDFT Study

Hole transfer along the axis of duplex DNA has been the focus of physical chemistry research for decades, with implications in diverse fields, from nanotechnology to cell oxidative damage. Computational approaches are particularly amenable for this problem, to complement experimental data for interpretation of transfer mechanisms. To be predictive, computational results need to account for the inherent mobility of biological molecules during the time frame of experimental measurements. Here, we address the structural variability of B-DNA and its effects on hole transfer in a combined molecular dynamics (MD) and real-time time-dependent density functional theory (RT-TDDFT) study. Our results show that quantities that characterize the charge transfer process, such as the time-dependent dipole moment and hole population at a specific site, are sensitive to structural changes that occur on the nanosecond time scale. We extend the range of physical properties for which such a correlation has been observed, further establishing the fact that quantitative computational data on charge transfer properties should include statistical averages. Furthermore, we use the RT-TDDFT results to assess an efficient tight-binding method suitable for high-throughput predictions. We demonstrate that charge transfer, although affected by structural variability, on average, remains strong in AA and GG dimers.

Predicting the DNA Conductance Using a Deep Feedforward Neural Network Model

Double-stranded DNA (dsDNA) has been established as an efficient medium for charge migration, bringing it to the forefront of the field of molecular electronics and biological research. The charge migration rate is controlled by the electronic couplings between the two nucleobases of DNA/RNA. These electronic couplings strongly depend on the intermolecular geometry and orientation. Estimating these electronic couplings for all the possible relative geometries of molecules using the computationally demanding first-principles calculations requires a lot of time and computational resources. In this article, we present a machine learning (ML)-based model to calculate the electronic coupling between any two bases of dsDNA/dsRNA and bypass the computationally expensive first-principles calculations. Using the Coulomb matrix representation which encodes the atomic identities and coordinates of the DNA base pairs to prepare the input dataset, we train a feedforward neural network model. Our neural network (NN) model can predict the electronic couplings between dsDNA base pairs with any structural orientation with a mean absolute error (MAE) of less than 0.014 eV. We further use the NN-predicted electronic coupling values to compute the dsDNA/dsRNA conductance.