Potential Unwinding of Double-Stranded DNA upon Binding to a Carbon Nitride Polyaniline (C3N) Nanosheet

Precision Basecalling of Single DNA Nucleotide from Overlapped Transmission Readouts with Machine Learning Aided Solid-State Nanogap

DNA sequencing with the quantum tunneling technique heralds a paradigm shift in genetic analysis, promising rapid and accurate identification for diverging applications ranging from personalized medicine to security issues. However, the widespread distribution of molecular conductance, conduction orbital alignment for resonant transport, and decoding crisscrossing conductance signals of isomorphic nucleotides have been persistent experimental hurdles for swift and precise identification. Herein, we have reported a machine learning (ML)-driven quantum tunneling study with solid-state model nanogap to determine nucleotides at single-base resolution. The optimized ML basecaller has demonstrated a high predictive basecalling accuracy of all four nucleotides from seven distinct data pools, each containing complex transmission readouts of their different dynamic conformations. ML classification of quaternary, ternary, and binary nucleotide combinations is also performed with high precision, sensitivity, and F1 score. ML explainability unravels the evidence of how extracted normalized features within overlapped nucleotide signals contribute to classification improvement. Moreover, electronic fingerprints, conductance sensitivity, and current readout analysis of nucleotides have promised practical applicability with significant sensitivity and distinguishability. Through this ML approach, our study pushes the boundaries of quantum sequencing by highlighting the effectiveness of single nucleotide basecalling with promising implications for advancing genomics and molecular diagnostics.

Screening 2D Materials for Their Nanotoxicity toward Nucleic Acids and Proteins: An In Silico Outlook

Since the discovery of graphene, two-dimensional (2D) materials have been anticipated to demonstrate enormous potential in bionanomedicine. Unfortunately, the majority of 2D materials induce nanotoxicity via disruption of the structure of biomolecules. Consequently, there has been an urge to synthesize and identify biocompatible 2D materials. Before the cytotoxicity of 2D nanomaterials is experimentally tested, computational studies can rapidly screen them. Additionally, computational analyses can provide invaluable insights into molecular-level interactions. Recently, various "in silico" techniques have identified these interactions and helped to develop a comprehensive understanding of nanotoxicity of 2D materials. In this article, we discuss the key recent advances in the application of computational methods for the screening of 2D materials for their nanotoxicity toward two important categories of abundant biomolecules, namely, nucleic acids and proteins. We believe the present article would help to develop newer computational protocols for the identification of novel biocompatible materials, thereby paving the way for next-generation biomedical and therapeutic applications based on 2D materials.

Robust Mechanical Destruction to the Cell Membrane of Carbon Nitride Polyaniline (C3N): A Molecular Dynamics Simulation Study

The drug-resistant bacteria, particularly multidrug-resistant bacteria, has emerged as a major global public health concern posing serious threats to human life and survival. Nanomaterials, including graphene, have shown promise as effective antibacterial agents owing to their unique antibacterial mechanism compared with traditional drugs. Despite the structural similarity to graphene, the potential antibacterial activity of carbon nitride polyaniline (C₃N) remains unexplored. In this study, we employed molecular dynamics simulations to investigate the effects of the interaction between the C₃N nanomaterial and the bacterial membrane to evaluate the potential antibacterial activity of C₃N. Our results suggest that C₃N is capable of inserting deep into the bacterial membrane interior, regardless of the presence or absence of positional restraints in the C₃N. The insertion process also resulted in local lipid extraction by the C₃N sheet. Additional structural analyses revealed that C₃N induced significant changes in membrane parameters, including mean square displacement, deuterium order parameters, membrane thickness, and area per lipid. Docking simulations, where all the C₃N are restraint to a specific positions, confirmed that C₃N can extract lipids from the membrane, indicating the strong interaction between the C₃N material and the membrane. Free-energy calculations further revealed that the insertion of the C₃N sheet is energetically favorable and that C₃N exhibits membrane insertion capacity comparable to that observed for graphene, suggesting their potential for similar antibacterial activity. This study provides the first evidence of the potential antibacterial properties of C₃N nanomaterials via bacterial membrane damage and underscores the potential for its use as antibacterial agents in the future applications.