Recognition Tunneling of Canonical and Modified RNA Nucleotides for Their Identification with the Aid of Machine Learning

Effect of graphene electrode functionalization on machine learning-aided single nucleotide classification

Solid-state nanogap-based DNA sequencing with a quantum tunneling approach has emerged as a promising avenue due to its potential to deliver swift and precise sequencing outcomes. Nevertheless, despite significant progress, experimentally achieving single base resolution with a high signal-to-noise ratio remains a daunting challenge. In this work, we have utilized a machine learning (ML) framework coupled with the quantum transport method to assess and compare the nucleotide identification performance of graphene nanogaps functionalized with four different edge-saturating entities (C, H, N, and OH). The optimized ML model, especially the random forest classifier (RFC), demonstrates high accuracy (>90%) in classifying unlabeled nucleotides from their transmission readouts with the four functionalized graphene nanogaps. Additionally, the minor variance in the accuracy of nucleotide classification across the nanogaps highlights that RFC can capture the role of electrode-nucleotide coupling dynamics in their transmission function. Moreover, we have also conducted conductance sensitivity (%) and current-voltage (I-V) analyses of each functionalized nanogap. Among the edge-saturating entities, the nitrogen atom terminated graphene nanogap (NGN) is found to be the most sensitive for distinguishing DNA nucleotides. Our quantum transport combined ML study provides a useful perspective by conducting a comparative analysis of the role of edge-saturating entities in single-molecule DNA sequencing.

Single-Molecule Bioelectronic Sensors with AI-Aided Data Analysis: Convergence and Challenges

Single-molecule bioelectronic sensing, a groundbreaking domain in biological research, has revolutionized our understanding of molecules by revealing deep insights into fundamental biological processes. The advent of emergent technologies, such as nanogapped electrodes and nanopores, has greatly enhanced this field, providing exceptional sensitivity, resolution, and integration capabilities. However, challenges persist, such as complex data sets with high noise levels and stochastic molecular dynamics. Artificial intelligence (AI) has stepped in to address these issues with its powerful data processing capabilities. AI algorithms effectively extract meaningful features, detect subtle changes, improve signal-to-noise ratios, and uncover hidden patterns in massive data. This review explores the synergy between AI and single-molecule bioelectronic sensing, focusing on how AI enhances signal processing and data analysis to boost accuracy and reliability. We also discuss current limitations and future directions for integrating AI, highlighting its potential to advance biological research and technological innovation.

Insight of Employing Molecular Junctions for Sensor Applications

Molecular junctions (MJs) exhibit distinct charge transport properties and have the potential to become the next generation of electronic devices. Advancing molecular electronics for practical uses, such as sensors, is crucial to propel its progress to the next level. In this review, we discussed how MJs can serve as a sensor for detecting a wide range of analytes with exceptional sensitivity and specificity. The primary advances and potential of molecular junctions for the various kinds of sensors including photosensors, explosives (DNTs, TNTs), cancer biomarker detection (DNA, mRNA), COVID detection, biogases (CO, NO, NH), environmental pH, practical chemicals, and water pollutants are listed and examined here. The fundamental ideas of molecular junction formation as well as the sensing mechanism have been examined here. This review demonstrates that MJ-based sensors hold significant promise for real-time and on-site detection. It provides valuable insights into current research and outlines potential future directions for advancing molecular junction-based sensors for practical applications.

Integration of Artificial Intelligence and Quantum Transport toward Stereoselective Identification of Carbohydrate Isomers

Detection of stereoisomers of carbohydrates with molecular resolution, a challenging goal analysts desire to achieve, is key to the full development of glycosciences. Despite the promise that analytical techniques made, including widely used nuclear magnetic resonance and mass spectrometry, high throughput de novo carbohydrate sequencing remains an unsolved issue. Notably, while next-generation sequencing technologies are readily available for DNA and proteins, they are conspicuously absent for carbohydrates due to the immense stereochemical and structural complexity inherent in these molecules. In this work, we report a novel computational technique that employs quantum tunneling coupled with artificial intelligence to detect complex carbohydrate anomers and stereoisomers with excellent sensitivity. The quantum tunneling footprints of carbohydrate isomers show high distinguishability with an in-depth analysis of underlying chemistry. Our findings open up a new route for carbohydrate sensing, which can be seamlessly integrated with next-generation sequencing technology for real-time analysis.

Advancement of Next-Generation DNA Sequencing through Ionic Blockade and Transverse Tunneling Current Methods

DNA sequencing is transforming the field of medical diagnostics and personalized medicine development by providing a pool of genetic information. Recent advancements have propelled solid-state material-based sequencing into the forefront as a promising next-generation sequencing (NGS) technology, offering amplification-free, cost-effective, and high-throughput DNA analysis. Consequently, a comprehensive framework for diverse sequencing methodologies and a cross-sectional understanding with meticulous documentation of the latest advancements is of timely need. This review explores a broad spectrum of progress and accomplishments in the field of DNA sequencing, focusing mainly on electrical detection methods. The review delves deep into both the theoretical and experimental demonstrations of the ionic blockade and transverse tunneling current methods across a broad range of device architectures, nanopore, nanogap, nanochannel, and hybrid/heterostructures. Additionally, various aspects of each architecture are explored along with their strengths and weaknesses, scrutinizing their potential applications for ultrafast DNA sequencing. Finally, an overview of existing challenges and future directions is provided to expedite the emergence of high-precision and ultrafast DNA sequencing with ionic and transverse current approaches.

DNA-Based Conductors: From Materials Design to Ultra-Scaled Electronics

Photolithography has been the foundational fabrication paradigm in current high-performance electronics. However, due to the limitation in fabrication resolution, scaling beyond a 20-nm critical dimension for metal conductors presents a significant challenge for photolithography. Structural DNA nanotechnology emerges as a promising alternative to photolithography, allowing for the site-specific assembly of nano-materials at single-molecule resolution. Substantial progresses have been achieved in the ultra-scaled DNA-based conductors, exhibiting novel transport characteristics and small critical dimensions. This review highlights the structure-transport property relationship for various DNA-based conductors and their potential applications in quantum /semiconductor electronics, going beyond the conventional scope focusing mainly on the shape diversity of DNA-templated metals. Different material synthesis methods and their morphological impacts on the conductivities are discussed in detail, with particular emphasis on the conducting mechanisms, such as insulating, metallic conducting, quantum tunneling, and superconducting. Furthermore, the ionic gating effect of self-assembled DNA structures in electrolyte solutions is examined. This review also suggests potential solutions to address current challenges in DNA-based conductors, encouraging multi-disciplinary collaborations for the future development of this exciting area.

Machine Learning-Assisted Direct RNA Sequencing with Epigenetic RNA Modification Detection via Quantum Tunneling

RNA sequence information holds immense potential as a drug target for diagnosing various RNA-mediated diseases and viral/bacterial infections. Massively parallel complementary DNA (c-DNA) sequencing helps but results in a loss of valuable information about RNA modifications, which are often associated with cancer evolution. Herein, we report machine learning (ML)-assisted high throughput RNA sequencing with inherent RNA modification detection using a single-molecule, long-read, and label-free quantum tunneling approach. The ML tools achieve classification accuracy as high as 100% in decoding RNA and 98% for decoding both RNA and RNA modifications simultaneously. The relationships between input features and target values have been well examined through Shapley additive explanations. Furthermore, transmission and sensitivity readouts enable the recognition of RNA and its modifications with good selectivity and sensitivity. Our approach represents a starting point for ML-assisted direct RNA sequencing that can potentially decode RNA and its epigenetic modifications at the molecular level.

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.

Emerging sensing platforms based on Cucurbit[n]uril functionalized gold nanoparticles and electrodes

Cucurbit[n]urils (CB[n]s, n = 5-8, 10, and 14), synthetic macrocycles with unique host-guest properties, have triggered increasing research interest in recent years. Gold nanoparticles (Au NPs) and electrodes stand out as exceptional substrates for sensing due to their remarkable physicochemical characteristics. Coupling the CB[n]s with Au NPs and electrodes has enabled the development of emerging sensing platforms for various promising applications. However, monitoring the behavior of analytes at the single-molecule level is currently one of the most challenging topics in the field of CB[n]-based sensing. Constructing supramolecular junctions in a sensing platform provides an ideal structure for single-molecule analysis, which can provide insights for a fundamental understanding of supramolecular interactions and chemical reactions and guide the design of sensing applications. This feature article outlines the progress in the preparation of the CB[n] functionalized Au NPs and Au electrodes, as well as the construction and application of supramolecular junctions in sensing platforms, based on the methods of recognition tunneling (RT), surface-enhanced Raman spectroscopy (SERS), single-molecule force spectroscopy (SMFS), and electrochemical sensing (ECS). A brief perspective on the future development of and challenges in CB[n] mediated sensing platforms is also covered.

Molecular Electronics: Creating and Bridging Molecular Junctions and Promoting Its Commercialization

Molecular electronics is driven by the dream of expanding Moore's law to the molecular level for next-generation electronics through incorporating individual or ensemble molecules into electronic circuits. For nearly 50 years, numerous efforts have been made to explore the intrinsic properties of molecules and develop diverse fascinating molecular electronic devices with the desired functionalities. The flourishing of molecular electronics is inseparable from the development of various elegant methodologies for creating nanogap electrodes and bridging the nanogap with molecules. This review first focuses on the techniques for making lateral and vertical nanogap electrodes by breaking, narrowing, and fixed modes, and highlights their capabilities, applications, merits, and shortcomings. After summarizing the approaches of growing single molecules or molecular layers on the electrodes, the methods of constructing a complete molecular circuit are comprehensively grouped into three categories: 1) directly bridging one-molecule-electrode component with another electrode, 2) physically bridging two-molecule-electrode components, and 3) chemically bridging two-molecule-electrode components. Finally, the current state of molecular circuit integration and commercialization is discussed and perspectives are provided, hoping to encourage the community to accelerate the realization of fully scalable molecular electronics for a new era of integrated microsystems and applications.

Advances in single-molecule junctions as tools for chemical and biochemical analysis

The development of miniaturized electronics has led to the design and construction of powerful experimental platforms capable of measuring electronic properties to the level of single molecules, along with new theoretical concepts to aid in the interpretation of the data. A new area of activity is now emerging concerned with repurposing the tools of molecular electronics for applications in chemical and biological analysis. Single-molecule junction techniques, such as the scanning tunnelling microscope break junction and related single-molecule circuit approaches have a remarkable capacity to transduce chemical information from individual molecules, sampled in real time, to electrical signals. In this Review, we discuss single-molecule junction approaches as emerging analytical tools for the chemical and biological sciences. We demonstrate how these analytical techniques are being extended to systems capable of probing chemical reaction mechanisms. We also examine how molecular junctions enable the detection of RNA, DNA, and traces of proteins in solution with limits of detection at the zeptomole level.

Development of an Artificially Intelligent Nanopore for High-Throughput DNA Sequencing with a Machine-Learning-Aided Quantum-Tunneling Approach

Solid-state nanopore-based single-molecule DNA sequencing with quantum tunneling technology poses formidable challenges to achieve long-read sequencing and high-throughput analysis. Here, we propose a method for developing an artificially intelligent (AI) nanopore that does not require extraction of the signature transmission function for each nucleotide of the whole DNA strand by integrating supervised machine learning (ML) and transverse quantum transport technology with a graphene nanopore. The optimized ML model can predict the transmission function of all other nucleotides after training with data sets of all the orientations of any nucleotide inside the nanopore with a root-mean-square error (RMSE) of as low as 0.062. Further, up to 96.01% accuracy is achieved in classifying the unlabeled nucleotides with their transmission readouts. We envision that an AI nanopore can alleviate the experimental challenges of the quantum-tunneling method and pave the way for rapid and high-precision DNA sequencing by predicting their signature transmission functions.

Towards a graphene semi/hybrid-nanogap: a new architecture for ultrafast DNA sequencing

The tremendous upsurge in the research of next-generation sequencing (NGS) methods has broadly been driven by the rise of the wonder material graphene and continues to dominate the futuristic approaches for fast and accurate DNA sequencing. The success of graphene has also triggered the search for many new potential NGS methods capable of ultrafast, reliable, and controlled DNA sequencing. The present study delves into the potential of a new NGS architecture utilizing graphene, namely, a 'semi/hybrid-nanogap' for the identification of DNA nucleobases with single-base resolution. In the framework of first-principles density functional theory methods, we have calculated the transmission function and current-voltage (I-V) characteristics which are of particular significance for DNA sequencing applications. It is noted that the interaction energy values are significantly reduced compared to the previously reported graphene nanodevices, which can lead to a controlled translocation during experimental measurements. Based on the transmission function, each nucleobase can be identified with pertinent sensitivity. It is noticed that the use of highly conductive nucleobase analogs can facilitate improved single nucleobase sensing by increasing the transmission sensitivity. Therefore, we believe that the present study opens up promising frontiers for sequencing applications.

Functionalized electrodes embedded in nanopores: read-out enhancement?

In this review, functionalized nanogaps embedded in nanopores are discussed in view of their high biosensitivity in detecting biomolecules, their length, type, and sequence. Specific focus is given on nanoelectrodes functionalized with tiny nanometer-sized diamond-like particles offering vast functionalization possibilities for gold junction electrodes. This choice of the functionalization, in turn, offers nucleotide-specific binding possibilities improving the detection signals arising from such functionalized electrodes potentially embedded in a nanopore. The review sheds light onto the use and enhancement of the tunnelling recognition in functionalized nanogaps towards sensing DNA nucleotides and mutation detection, providing important input for a practical realization.

Machine Learning Aided Interpretable Approach for Single Nucleotide-Based DNA Sequencing using a Model Nanopore

Solid-state nanopore-based electrical detection of DNA nucleotides with the quantum tunneling technique has emerged as a powerful strategy to be the next-generation sequencing technology. However, experimental complexity has been a foremost obstacle in achieving a more accurate high-throughput analysis with industrial scalability. Here, with one of the nucleotide training data sets of a model monolayer gold nanopore, we have predicted the transmission function for all other nucleotides with root-mean-square error scores as low as 0.12 using the optimized eXtreme Gradient Boosting Regression (XGBR) model. Further, the SHapley Additive exPlanations (SHAP) analysis helped in exploring the interpretability of the XGBR model prediction and revealed the complex relationship between the molecular properties of nucleotides and their transmission functions by both global and local interpretable explanations. Hence, experimental integration of our proposed machine-learning-assisted transmission function prediction method can offer a new direction for the realization of cheap, accurate, and ultrafast DNA sequencing.

Machine Learning Prediction of the Transmission Function for Protein Sequencing with Graphene Nanoslit

Protein sequencing has rapidly changed the landscape of healthcare and life science by accelerating the growth of diagnostics and personalized medicines for a variety of fatal diseases. Next-generation nanopore/nanoslit sequencing is promising to achieve single-molecule resolution with chromosome-size-long readability. However, due to inherent complexity, high-throughput sequencing of all 20 amino acids demands different approaches. Aiming to accelerate the detection of amino acids, a general machine learning (ML) method has been developed for quick and accurate prediction of the transmission function for amino acid sequencing. Among the utilized ML models, the XGBoost regression model is found to be the most effective algorithm for fast prediction of the transmission function with a very low test root-mean-square error (RMSE ∼0.05). In addition, using the random forest ML classification technique, we are able to classify the neutral amino acids with a prediction accuracy of 100%. Therefore, our approach is an initiative for the prediction of the transmission function through ML and can provide a platform for the quick identification of amino acids with high accuracy.

Nanodevices for Biological and Medical Applications: Development of Single-Molecule Electrical Measurement Method

A comprehensive detection of a wide variety of diagnostic markers is required for the realization of personalized medicine. As a sensor to realize such personalized medicine, a single molecule electrical measurement method using nanodevices is currently attracting interest for its comprehensive simultaneous detection of various target markers for use in biological and medical application. Single-molecule electrical measurement using nanodevices, such as nanopore, nanogap, or nanopipette devices, has the following features:; high sensitivity, low-cost, high-throughput detection, easy-portability, low-cost availability by mass production technologies, and the possibility of integration of various functions and multiple sensors. In this review, I focus on the medical applications of single- molecule electrical measurement using nanodevices. This review provides information on the current status and future prospects of nanodevice-based single-molecule electrical measurement technology, which is making a full-scale contribution to realizing personalized medicine in the future. Future prospects include some discussion on of the current issues on the expansion of the application requirements for single-mole-cule measurement.

A Guide to Signal Processing Algorithms for Nanopore Sensors

Nanopore technology holds great promise for a wide range of applications such as biomedical sensing, chemical detection, desalination, and energy conversion. For sensing performed in electrolytes in particular, abundant information about the translocating analytes is hidden in the fluctuating monitoring ionic current contributed from interactions between the analytes and the nanopore. Such ionic currents are inevitably affected by noise; hence, signal processing is an inseparable component of sensing in order to identify the hidden features in the signals and to analyze them. This Guide starts from untangling the signal processing flow and categorizing the various algorithms developed to extracting the useful information. By sorting the algorithms under Machine Learning (ML)-based versus non-ML-based, their underlying architectures and properties are systematically evaluated. For each category, the development tactics and features of the algorithms with implementation examples are discussed by referring to their common signal processing flow graphically summarized in a chart and by highlighting their key issues tabulated for clear comparison. How to get started with building up an ML-based algorithm is subsequently presented. The specific properties of the ML-based algorithms are then discussed in terms of learning strategy, performance evaluation, experimental repeatability and reliability, data preparation, and data utilization strategy. This Guide is concluded by outlining strategies and considerations for prospect algorithms.

Key aurophilic motif for robust quantum-tunneling-based characterization of a nucleoside analogue marker

A quantum sequencer offers a scalable electrical platform for single-molecule analysis of genomic events. A thymidine (dT) analog exhibiting uniquely high single-molecule conductance is a key element in capturing DNA synthesis dynamics by serving as a decodable marker for enzymatic labeling of nascent strands. However, the current design strategies of dT analogs that focus on their molecular orbital energy levels require bulky chemical modifications to extend the π-conjugation, which hinders polymerase recognition. We report herein a polymerase-compatible dT analog that is highly identifiable in quantum sequencing. An ethynyl group is introduced as a small gold-binding motif to differentiate the nucleobase-gold electronic coupling, which has been an overlooked factor in modifying nucleobase conductance. The resulting C5-ethynyl-2'-deoxyuridine exhibits characteristic signal profiles that allowed its correct identification at a 93% rate while maintaining polymerase compatibility. This study would expand the applicability of quantum sequencing by demonstrating a robust nucleoside marker with high identifiability.

Combination of Single-Molecule Electrical Measurements and Machine Learning for the Identification of Single Biomolecules

The development of a next-generation DNA sequencer has provided a method for electrically measuring single molecules. Methods for electrically measuring one molecule are roughly divided into methods for measuring tunneling and ion currents. These methods enable identification of a single molecule of DNA, a RNA nucleotide, or a single protein based on current histograms. However, overlapping of current histograms of molecules with similar properties has been a major barrier to identifying single molecules with high accuracy. This barrier was broken by introducing machine learning. Combining single-molecule electrical measurement and machine learning enables high-precision identification of single molecules. Highly accurate discrimination has been demonstrated for DNA nucleotides, RNA nucleotides, amino acids, sugars, viruses, and bacteria. This combination enables quantitative evaluation of molecular recognition ability. Furthermore, a device structure suitable for high-precision identification has been designed. Combining single-molecule electrical measurement with machine learning enables digital analytical chemistry that can count certain types of molecules. Digital analytical chemistry enables comprehensive analysis of chemical reactions. This new analytical method will lead to the discovery of unknown or missed valuable molecules.

Modulating and probing the dynamic intermolecular interactions in plasmonic molecule-pair junctions

The intermolecular interactions, including hydrogen bonds, are electromechanically modulated and probed in metal–molecule pair–metal junctions.