Advancement in DNA quantum computing using electric field gradients and nuclear spins

A recent study by researchers from Peking University demonstrates the potential of nuclear electric resonance to control the nuclear spins of nitrogen atoms in DNA using electric field gradients, thereby achieving artificial intervention to manipulate DNA for computation. Utilizing molecular dynamics simulations, quantum chemical computations and theoretical analyses, the research reveals how electric field gradient orientation patterns vary with DNA bases and nitrogen atom sites, encoding genetic and structural information into the direction of nitrogen nuclear spins. The research was published Dec. 12 in Intelligent Computing, a Science Partner Journal, in an open access article titled “Encoding Genetic and Structural Information in DNA Using Electric Field Gradients and Nuclear Spins.”

"Our research has unveiled the patterns of the principal axis directions of the electric field gradient at the nitrogen atom sites in DNA molecules, demonstrating that these directions are closely associated with the types of bases and the 3D structure of DNA," the authors said. These nitrogen nuclear spin patterns thus encode the three-dimensional structure information and base sequence information of DNA molecules. Therefore, by controlling the sequence of bases in DNA, they could be used as a storage mechanism in a DNA-based quantum computing device in the future. Such a device would also need a computation mechanism. Proton nuclear spins exhibit more complex and varied properties, allowing them to obtain information by interactions with nitrogen nuclear spins to achieve computational function. This mechanism enables the possibility of quantum computing using DNA.

Nitrogen atoms in DNA are bonded with either three or two atoms, resulting in different electric field gradient orientations. In the former case, the principal axis is always perpendicular to the base plane, while in the latter, the principal axis either aligns with the bisector of the bonds or is close to perpendicular to it, depending on the base and nitrogen type. These orientations vary across the four bases: adenine, guanine, cytosine and thymine. Furthermore, spin system simulations analyzed the electric field gradient data of adjacent bases, showing that for nitrogen atoms bonded with to atoms in adenine and guanine, deflection angles of nuclear spin orientations align consistently with the structural deflection angles of the bases. However, cytosine and thymine exhibit more variability, with no fixed rules for nitrogen orientations.

To investigate the electric field gradients in DNA, the authors used molecular dynamics simulations to model the atomic coordinates of the DNA molecule over time. They used a solvated DNA system with added ions to ensure neutrality, applying rigorous equilibration and simulation steps. Quantum chemical calculations were then conducted on selected nucleotide subsets, focusing on the nitrogen atom positions within the DNA bases. The electric field gradient components were analyzed to extract principal axis directions and eigenvalues. By comparing the deflection angles of the structures of the two adjacent homogeneous bases in DNA with the deflection angles of the electric field gradients of the nuclei, the authors investigated the dependence of deflection angles of nuclear spin orientations on DNA structure, as well as the influence of nitrogen nuclear spins on the spin directions of surrounding proton nuclei under the electric field gradient.

The study follows on the authors’ previous research, which focused on the potential of nuclear electric resonance to control the nuclear spins of sodium ions on phospholipid membranes using electric field gradients. This new study extended previous findings, uncovers the intricate relationships between electric field gradients, nitrogen atom orientations and DNA base structures, deepens the understanding of performing DNA computation through artificial intervention at the molecular level, and paves the way for innovative approaches to future quantum computer design and genetic information processing.

END