Electronic Properties of DNA Origami Nanostructures Revealed by In Silico Calculations.

DNA origami is a pioneering approach for producing complex 2- or 3-D shapes for use in molecular electronics due to its inherent self-assembly and programmability properties. The electronic properties of DNA origami structures are not yet fully understood, limiting the potential applications. Here, we conduct a theoretical study with a combination of molecular dynamics, first-principles, and charge transmission calculations. We use four separate single strand DNAs, each having 8 bases (4 × G4C4 and 4 × A4T4), to form two different DNA nanostructures, each having two helices bundled together with one crossover. We also generated double-stranded DNAs to compare electronic properties to decipher the effects of crossovers and bundle formations. We demonstrate that density of states and band gap of DNA origami depend on its sequence and structure. The crossover regions could reduce the conductance due to a lack of available states near the HOMO level. Furthermore, we reveal that, despite having the same sequence, the two helices in the DNA origami structure could exhibit different electronic properties, and electrode position can affect the resulting conductance values. Our study provides better understanding of the electronic properties of DNA origamis and enables us to tune these properties for electronic applications such as nanowires, switches, and logic gates.

Identifying SARS-CoV-2 Variants Using Single-Molecule Conductance Measurements.

The global COVID-19 pandemic has highlighted the need for rapid, reliable, and efficient detection of biological agents and the necessity of tracking changes in genetic material as new SARS-CoV-2 variants emerge. Here, we demonstrate that RNA-based, single-molecule conductance experiments can be used to identify specific variants of SARS-CoV-2. To this end, we (i) select target sequences of interest for specific variants, (ii) utilize single-molecule break junction measurements to obtain conductance histograms for each sequence and its potential mutations, and (iii) employ the XGBoost machine learning classifier to rapidly identify the presence of target molecules in solution with a limited number of conductance traces. This approach allows high-specificity and high-sensitivity detection of RNA target sequences less than 20 base pairs in length by utilizing a complementary DNA probe capable of binding to the specific target. We use this approach to directly detect SARS-CoV-2 variants of concerns B.1.1.7 (Alpha), B.1.351 (Beta), B.1.617.2 (Delta), and B.1.1.529 (Omicron) and further demonstrate that the specific sequence conductance is sensitive to nucleotide mismatches, thus broadening the identification capabilities of the system. Thus, our experimental methodology detects specific SARS-CoV-2 variants, as well as recognizes the emergence of new variants as they arise.