Understanding structural and molecular properties of complexes of nucleobases and Au13 golden nanocluster by DFT calculations and DFT-MD simulation

Aptamer functionalized gold nanoclusters as an emerging nanoprobe in biosensing, diagnostic, catalysis and bioimaging

DNA nanostructures, with their fascinating luminescent and detecting capabilities, provide a basis that can accommodate a wide range of applications. The unique electronic configurations, and physical and chemical properties of aptamer-assembled gold nanoclusters (apt-AuNCs) as a novel type of fluorophore have gradually piqued the interest of the scientific community. Bending DNA sequences and other templates/legends as a stabilizing agent with Au metal has produced an abundance of biosensors, along with catalytic and imaging properties. This review article summarizes the synthesis, conjugation tactics, advantages, and sensing mechanisms of AuNCs aptasensor after providing a brief introduction to the topic. Moreover, the application of DNA/aptamer functionalization has been briefly discussed in the fields of food safety and quality, catalysis, clinical diagnosis, cancer cell bioimaging, detection of cancer cell indicators, and therapy. We also concluded the current obstacles and made recommendations about the future prospects of AuNCs for fundamental research and applications in line with the developments in DNA/aptamer-AuNCs.

Understanding the Interaction Mechanism between the Epinephrine Neurotransmitter and Small Gold Nanoclusters (Aun; n = 6, 8, and 10): A Computational Insight

In this study, the interaction between the neurotransmitter epinephrine and small gold nanoclusters (AunNCs) with n = 6, 8, and 10 is described by density functional theory calculations. The interaction of Au6, Au8, and Au10 nanoclusters with epinephrine is governed by Au-X (X = N and O) anchoring bonding and Au···H-X conventional hydrogen bonding. The interaction mechanism of epinephrine with gold nanoclusters is investigated in terms of electronic energy and geometrical properties. The adsorption energy values for the most favorable configurations of Au6NC@epinephrine, Au8NC@epinephrine, and Au10NC@epinephrine were calculated to be -17.45, -17.86, and -16.07 kcal/mol, respectively, in the gas phase. The results indicate a significant interaction of epinephrine with AunNCs and point to the application of the biomolecular complex AunNC@epinephrine in the fields of biosensing, drug delivery, bioimaging, and other applications. In addition, some important electronic properties, namely, the energy gap between HOMO and LUMO, the Fermi level, and the work function, were computed. The effect of aqueous media on adsorption energy and electronic parameters for the most favorable configurations was also studied to explore the influence of physical biological conditions.

Predicting accurate ab initio DNA electron densities with equivariant neural networks

One of the fundamental limitations of accurately modeling biomolecules like DNA is the inability to perform quantum chemistry calculations on large molecular structures. We present a machine learning model based on an equivariant Euclidean neural network framework to obtain accurate ab initio electron densities for arbitrary DNA structures that are much too large for conventional quantum methods. The model is trained on representative B-DNA basepair steps that capture both base pairing and base stacking interactions. The model produces accurate electron densities for arbitrary B-DNA structures with typical errors of less than 1%. Crucially, the error does not increase with system size, which suggests that the model can extrapolate to large DNA structures with negligible loss of accuracy. The model also generalizes reasonably to other DNA structural motifs such as the A- and Z-DNA forms, despite being trained on only B-DNA configurations. The model is used to calculate electron densities of several large-scale DNA structures, and we show that the computational scaling for this model is essentially linear. We also show that this machine learning electron density model can be used to calculate accurate electrostatic potentials for DNA. These electrostatic potentials produce more accurate results compared with classical force fields and do not show the usual deficiencies at short range.

Importance of Sugar-Phosphate Backbone and Counterions to First-Principles Modeling of Nucleobases

DFT-based first-principles calculations were carried out to understand the electronic structure difference among a backbone-free nucleobase, a backbone-containing Na counterion nucleotide, and a backbone-containing H counterion nucleotide and their difference in the adsorption on graphene and on graphitic-carbon nitride. The study discovered that the inclusion of a sugar-phosphate backbone changes the electron affinity of most nucleobases from electron acceptors to electron donors. The methyl-terminated backbone-free model cannot replicate the steric effect induced by the sugar-phosphate backbone during the adsorption of nucleobases on 2D materials. Overall, we established that the sugar phosphate backbone should be included in the study of DNA nucleobase adsorption on 2D material. We also showed that when it comes to the adsorption on 2D materials, the backbone-containing H counterion model is superior to the Na counterion model because the Na counterion produces a LUMO near the Fermi energy, which may significantly affect the interaction with the 2D material.