Computational Insight into Graphene Functionalization for DNA Sequencing: A DFT Approach ^†

Most diseases, such as cancer, gene mutations, or infections among humans, are due to DNA nucleotide mis-sequence. Deoxyribonucleic acid (DNA) is vital in life science, and its sequence detection is imperative in the fields of disease diagnosis, forensic sciences, and genomics systems, making materials designed for DNA identification very crucial. Two-dimensional materials such as graphene doped with some heteroatoms have been explored for DNA nucleobase detection, but the role of functional groups remains unclear. This study investigates the influence of functional groups in the discrimination of the following DNA nucleotides: Adenine (A), Guanine (G), Thymine (T), and Cytosine (C). Herein, we studied how functional groups like carboxylate, nitrile, alcohol, and carboxylic acid improve the adsorption capacity of DNA nucleotides onto graphene sheets. Stable configurations of DNA bases adsorbed onto the graphene surface were investigated using Spartan software and density functional theory (DFT) for quantum chemical calculations. The adsorption energies and band gaps for interaction between the nucleobases and functionalized graphene sheets were determined. Our findings reveal that non-functionalized graphene is sensitive to G; alcohols and nitriles functionalized to A; and carboxylates functionalized to C. However, acetic (carboxylic) acid is significantly sensitive to all four nucleotides, making it suitable for DNA sequencing. The relative adsorption energy hierarchy of nucleotides was obtained and shown to be consistent with previous findings reported in the literature. Our findings confirm the potential of computational methods for predicting functionalized graphene’s selectivity in discriminating DNA nucleotides, offering a promising avenue for identifying mutations driving tumor growth, predicting prognosis, and guiding targeted therapies tailored to the unique genetic profile of each patient’s disease.