Single-molecule spectroscopy of amino acids and peptides by recognition tunnelling

A single-molecule electrical approach for amino acid detection and chirality recognition

One of the ultimate goals of analytic chemistry is to efficiently discriminate between amino acids. Here we demonstrate this ability using a single-molecule electrical methodology based on molecular nanocircuits formed from stable graphene-molecule-graphene single-molecule junctions. These molecular junctions are fabricated by covalently bonding a molecular machine featuring a permethylated-β-cyclodextrin between a pair of graphene point contacts. Using pH to vary the type and charge of the amino acids, we find distinct multimodal current fluctuations originating from the different host-guest interactions, consistent with theoretical calculations. These conductance data produce characteristic dwell times and shuttling rates for each amino acid, and allow accurate, statistical real-time, in situ measurements. Testing four amino acids and their enantiomers shows the ability to distinguish between them within a few microseconds, thus paving a facile and precise way to amino acid identification and even single-molecule protein sequencing.

Single-aminoacid discrimination in proteins with homogeneous nanopore sensors and neural networks

A technology capable of sequencing individual protein molecules would revolutionize our understanding of biological processes. Nanopore technology can analyze single heteropolymer molecules such as DNA by measuring the ionic current flowing through a single nanometer hole made in an electrically insulating membrane. This current is sensitive to the monomer sequence. However, proteins are remarkably complex and identifying a single residue change in a protein remains a challenge. In this work, I show that simple neural networks can be trained to recognize protein mutants. Although these networks are quickly and efficiently trained, their ability to generalize in an independent experiment is poor. Using a thermal annealing protocol on the nanopore sample, and examining many mutants with the same nanopore sensor are measures aimed at reducing training data variability which produce an increase in the generalizability of the trained neural network. Using this approach, we obtain a 100% correct assignment among 9 mutants in >50% of the experiments. Interestingly, the neural network performance, compared to a random guess, improves as more mutants are included in the dataset for discrimination. Engineered nanopores prepared with high homogeneity coupled with state-of-the-art analysis of the ionic current signals may enable single-molecule protein sequencing.

Combined quantum tunnelling and dielectrophoretic trapping for molecular analysis at ultra-low analyte concentrations

Quantum tunnelling offers a unique opportunity to study nanoscale objects with atomic resolution using electrical readout. However, practical implementation is impeded by the lack of simple, stable probes, that are required for successful operation. Existing platforms offer low throughput and operate in a limited range of analyte concentrations, as there is no active control to transport molecules to the sensor. We report on a standalone tunnelling probe based on double-barrelled capillary nanoelectrodes that do not require a conductive substrate to operate unlike other techniques, such as scanning tunnelling microscopy. These probes can be used to efficiently operate in solution environments and detect single molecules, including mononucleotides, oligonucleotides, and proteins. The probes are simple to fabricate, exhibit remarkable stability, and can be combined with dielectrophoretic trapping, enabling active analyte transport to the tunnelling sensor. The latter allows for up to 5-orders of magnitude increase in event detection rates and sub-femtomolar sensitivity.

Probes that effectively utilize quantum tunneling are sought after for high-resolution study of nanoscale objects. Here the authors present an easily fabricated probe of two nanoelectrodes that enables highly sensitive quantum-tunneling-based sensing of single molecules.

Field Effect and Local Gating in Nitrogen-Terminated Nanopores (NtNP) and Nanogaps (NtNG) in Graphene

Functionalization of electrodes is a wide-used strategy in various applications ranging from single-molecule sensing and protein sequencing, to ion trapping, to desalination. We demonstrate, employing non-equilibrium Green's function formalism combined with density functional theory, that single-species (N, H, S, Cl, F) termination of graphene nanogap electrodes results in a strong in-gap electrostatic field, induced by species-dependent dipoles formed at the electrode ends. Consequently, the field increases or decreases electronic transport through a molecule (benzene) placed in the nanogap by shifting molecular levels by almost 2 eV in respect to the electrode Fermi level via a field effect akin to the one used for field-effect transistors. We also observed the local gating in graphene nanopores terminated with different single-species atoms. Nitrogen-terminated nanogaps (NtNGs) and nanopores (NtNPs) show the strongest effect. The in-gap potential can be transformed from a plateau-like to a saddle-like shape by tailoring NtNG and NtNP size and termination type. In particular, the saddle-like potential is applicable in single-ion trapping and desalination devices.

Advancing Biosensors with Machine Learning

Chemometrics play a critical role in biosensors-based detection, analysis, and diagnosis. Nowadays, as a branch of artificial intelligence (AI), machine learning (ML) have achieved impressive advances. However, novel advanced ML methods, especially deep learning, which is famous for image analysis, facial recognition, and speech recognition, has remained relatively elusive to the biosensor community. Herein, how ML can be beneficial to biosensors is systematically discussed. The advantages and drawbacks of most popular ML algorithms are summarized on the basis of sensing data analysis. Specially, deep learning methods such as convolutional neural network (CNN) and recurrent neural network (RNN) are emphasized. Diverse ML-assisted electrochemical biosensors, wearable electronics, SERS and other spectra-based biosensors, fluorescence biosensors and colorimetric biosensors are comprehensively discussed. Furthermore, biosensor networks and multibiosensor data fusion are introduced. This review will nicely bridge ML with biosensors, and greatly expand chemometrics for detection, analysis, and diagnosis.

Multifaceted aspects of charge transfer

Charge transfer and charge transport are by far among the most important processes for sustaining life on Earth and for making our modern ways of living possible. Involving multiple electron-transfer steps, photosynthesis and cellular respiration have been principally responsible for managing the energy flow in the biosphere of our planet since the Great Oxygen Event. It is impossible to imagine living organisms without charge transport mediated by ion channels, or electron and proton transfer mediated by redox enzymes. Concurrently, transfer and transport of electrons and holes drive the functionalities of electronic and photonic devices that are intricate for our lives. While fueling advances in engineering, charge-transfer science has established itself as an important independent field, originating from physical chemistry and chemical physics, focusing on paradigms from biology, and gaining momentum from solar-energy research. Here, we review the fundamental concepts of charge transfer, and outline its core role in a broad range of unrelated fields, such as medicine, environmental science, catalysis, electronics and photonics. The ubiquitous nature of dipoles, for example, sets demands on deepening the understanding of how localized electric fields affect charge transfer. Charge-transfer electrets, thus, prove important for advancing the field and for interfacing fundamental science with engineering. Synergy between the vastly different aspects of charge-transfer science sets the stage for the broad global impacts that the advances in this field have.

Molecular dynamics discrimination of the conformational states of calmodulin through solid-state nanopores

As a type of biological macromolecule, the conformation of proteins dynamically changes in a solution, which often results in a change in their function. However, traditional biological assays have significant drawbacks in detecting the conformation properties of proteins. Alternatively, nanopores have potential advantages in this area, which can detect protein in high throughput and without labelling. Herein, we investigated the translocation of calmodulins through silicon nitride nanopores using molecular dynamics (MD) simulation. Initially, the calmodulins were fixed in the nanopore. Distinguished blocked ionic currents were obtained between the two forms of calmodulin. Next, in the translocation simulations, a prominent difference in time resolution was easily found between the two states of calmodulin by using the appropriate voltage and comparable size of pore to protein, r_p/r_g→ 1, 4.5 nm (where r_p is the protein radius and r_g is the gyration radius). These simulations on the nanoscale are helpful for developing Ca²⁺-sensitive ion channels and nanodevices.

Critical Review: digital resolution biomolecular sensing for diagnostics and life science research

One of the frontiers in the field of biosensors is the ability to quantify specific target molecules with enough precision to count individual units in a test sample, and to observe the characteristics of individual biomolecular interactions. Technologies that enable observation of molecules with "digital precision" have applications for in vitro diagnostics with ultra-sensitive limits of detection, characterization of biomolecular binding kinetics with a greater degree of precision, and gaining deeper insights into biological processes through quantification of molecules in complex specimens that would otherwise be unobservable. In this review, we seek to capture the current state-of-the-art in the field of digital resolution biosensing. We describe the capabilities of commercially available technology platforms, as well as capabilities that have been described in published literature. We highlight approaches that utilize enzymatic amplification, nanoparticle tags, chemical tags, as well as label-free biosensing methods.

Enabling nanopore technology for sensing individual amino acids by a derivatization strategy

Nanopore technology holds remarkable promise for sequencing proteins and peptides. To achieve this, it is necessary to establish a characteristic profile for each individual amino acid through the statistical description of its translocation process. However, the subtle molecular differences among all twenty amino acids along with their unpredictable conformational changes at the nanopore sensing region result in very low distinguishability. Here we report the electrical sensing of individual amino acids using an α-hemolysin nanopore based on a derivatization strategy. Using derivatized amino acids as detection surrogates not only prolongs their interactions with the sensing region, but also improves their conformational variation. Furthermore, we show that distinct characteristics including current blockades and dwell times can be observed among all three classes of amino acids after 2,3-naphthalenedicarboxaldehyde (NDA)- and 2-naphthylisothiocyanate (NITC)-derivatization, respectively. These observable characteristics were applied towards the identification and differentiation of 9 of the 20 natural amino acids using their NITC derivatives. The method demonstrated herein will pave the way for the identification of all amino acids and further protein and peptide sequencing.

Planar graphene/h-BN/graphene heterostructures for protein stretching and confinement

Protein stretching and confinement in nanochannels is critical for advancing single-molecule detection techniques. For standard nanochannels integrated with nano-sensors, reducing their cross-section is beneficial for reading highly localized signals with minimal error, but results in increasing difficulty for the initial capture of any chain molecules due to the entropy barrier. Using molecular dynamics simulations, we show that spontaneous protein stretching can be realized by a two-dimensional (2D) heterostructure composed of a hexagonal boron nitride (h-BN) nanoribbon stitched with two graphene (GRA) sheets (i.e., a sandwiched GRA/BN/GRA structure). Due to fast protein diffusion on its flat surface and adsorption potential difference between two 2D materials, this planar nanochannel permits effective capture and elongation of three representative intrinsically disordered proteins including amyloid-β (1-42), polyglutamine (42) and α-synuclein (61-95). Moreover, we found that the extremely narrow h-BN stripe can provide stronger confinement for a longer polyglutamine chain after being stretched. Our approach has the potential to facilitate the bona fide readout of single-molecule protein sequencing techniques.

N-Terminal Derivatization-Assisted Identification of Individual Amino Acids Using a Biological Nanopore Sensor

Nanopore technology has been employed as a powerful tool for DNA sequencing and analysis. To extend this method to peptide sequencing, a necessary step is to profile individual amino acids (AAs) through their nanopore stochastic signals, which remains a great challenge because of the low signal-to-noise ratio and unpredictable conformational changes of AAs during their translocation through nanopores. We showed that the combination of an N-terminal derivatization strategy of AAs with nanopore technology could lead to effective in situ differentiation of AAs. Four different derivatization reactions have been tested with five selected AAs: Ala, Phe, Tyr, His, and Asp. Using an α-hemolysin nanopore, we demonstrated the feasibility of derivatization-assisted identification of AAs regardless of their charge composition and polarity. The method was further applied to discriminate each individual AA in testing data sets using their established nanopore profiles from training data sets. We envision that this proof-of-concept study will not only pave a way for identification of individual AAs but also lead to future applications in protein/peptide sequencing using the nanopore technology.

Application of Solid-State Nanopore in Protein Detection

A protein is a kind of major biomacromolecule of life. Its sequence, structure, and content in organisms contains quite important information for normal or pathological physiological process. However, research of proteomics is facing certain obstacles. Only a few technologies are available for protein analysis, and their application is limited by chemical modification or the need for a large amount of sample. Solid-state nanopore overcomes some shortcomings of the existing technology, and has the ability to detect proteins at a single-molecule level, with its high sensitivity and robustness of device. Many works on detection of protein molecules and discriminating structure have been carried out in recent years. Single-molecule protein sequencing techniques based on solid-state nanopore are also been proposed and developed. Here, we categorize and describe these efforts and progress, as well as discuss their advantages and drawbacks.

Gap Size Dependence of Atomistic-Resolved Peptide Bond Signals by Tunneling Current Across Nano-Gaps of Graphene Nano-Ribbons

According to the recent literature, it has been demonstrated that the atomistic scale recognition of amino acids and peptide-bonds in polypeptides and proteins is in principle possible by measuring the tunneling current flowing across a narrow nano-gap in graphene nano ribbons during the peptide translocation. In this paper, we concentrate on the tunneling current signal properties measured for nano-gaps of different sizes. Using the non equilibrium Green function method based on the density functional theory, we have studied the tunneling current for larger gap sizes that can be actually realized according to the present state of the art sub-nanometer nano-pore and nano-gap technology. Also in these cases the peptide bond can be still recognized, the obtained signal being well within the measurable range of the current. The signal shapes undergo a change from a double peak feature per peptide bond for narrow gaps to a structured single peak signal per peptide bond for wider gaps. The reason is related to the different orbital overlap range of the two contributions giving rise to the original double peak signal for narrow gaps.

Integrating Artificial Intelligence and Nanotechnology for Precision Cancer Medicine

Artificial intelligence (AI) and nanotechnology are two fields that are instrumental in realizing the goal of precision medicine-tailoring the best treatment for each cancer patient. Recent conversion between these two fields is enabling better patient data acquisition and improved design of nanomaterials for precision cancer medicine. Diagnostic nanomaterials are used to assemble a patient-specific disease profile, which is then leveraged, through a set of therapeutic nanotechnologies, to improve the treatment outcome. However, high intratumor and interpatient heterogeneities make the rational design of diagnostic and therapeutic platforms, and analysis of their output, extremely difficult. Integration of AI approaches can bridge this gap, using pattern analysis and classification algorithms for improved diagnostic and therapeutic accuracy. Nanomedicine design also benefits from the application of AI, by optimizing material properties according to predicted interactions with the target drug, biological fluids, immune system, vasculature, and cell membranes, all affecting therapeutic efficacy. Here, fundamental concepts in AI are described and the contributions and promise of nanotechnology coupled with AI to the future of precision cancer medicine are reviewed.

Coenzyme Coupling Boosts Charge Transport through Single Bioactive Enzyme Junctions

Oxidation of formate to CO₂ is catalyzed via the donation of electrons from formate dehydrogenase (FDH) to nicotinamide adenine dinucleotide (NAD⁺), and thus the charge transport characteristics of FDH become essential but remain unexplored. Here, we investigated the charge transport through single-enzyme junctions of FDH using the scanning tunneling microscope break junction technique (STM-BJ). We found that the coupling of NAD⁺ with FDH boosts the charge transport by ∼2,100%, and the single-enzyme conductance highly correlates with the enzyme activity. The combined flicker noise analysis demonstrated the switching of the coenzyme-mediated charge transport pathway and supported by the significantly reduced HOMO-LUMO gap from calculations. Site-specific mutagenesis analysis demonstrated that FDH-NAD⁺ stably combined own higher bioactivity and boosts charge transport, and the coupling has been optimized via the natural selection. Our work provides evidence of hydrogen bond coupling in bioactivity but also bridges the charge transport through single-enzyme junctions and enzyme activities.

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Binding of NAD⁺ with FDH boosts the charge transport by more than 2,100%

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Single-enzyme conductance highly correlates with the enzyme activity

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Hydrogen bond bridges the charge transport and enzyme activities

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Experiments combined with calculations probe switching of charge transport pathway

Soft Nanoarchitectonics for Enantioselective Biosensing

Chirality is a fundamental property of a molecule, and the significant progress in chirality detection and quantification of a molecule has inspired major advances in various fields ranging from chemistry, biology, to biotechnology and pharmacology. Chiral molecules have identical molecular formulas, atom-to-atom linkages, and bonding distances, and as such they are difficult to distinguish both sensitively and selectively. Today, most new drugs and those under development are chiral, which requires technological developments in the separation and detection of chiral molecules. Therefore, rapid and facile methods to detect and discriminate chiral compounds are necessary to accelerate advances in many research fields. The challenges in analysis stem from the obvious fact that chiral molecules have the same physical properties. Although significant progress on the detection of enantiomeric composition has been achieved in the past decade, in order to fully realize the capacity of chiral molecular interrogation, highly sensitive and selective, portable, and easy-to-use detection remains challenging because of the limitation of conventional techniques.Soft nanoarchitectonics is a new concept for the fabrication of functional soft material systems through harmonization of various actions including atomic/molecular-level manipulation, chemical reactions, self-assembly and self-organization, and their modulation by external fields/stimuli. Soft nanoarchitectonics has been widely used as a key enabling technology for integrating predefined molecular functionalities including electrochemical, optical, catalytic, or biological properties into biosensing devices, which provides exciting opportunities to design, assemble, and fabricate tailored nanosystems to enable new sensing strategies for chiral molecules.In this Account, we aim to concisely discuss how these molecule-inspired soft nanoarchitectonics work for enantioselective sensing. We will first outline the basic principle and mechanistic insights of the soft nanoarchitectonics approach for enantioselective sensing, and then we will describe the new breakthroughs and trends in the area that have been most recently reported by our groups and others. There will also be a discussion on the merits of soft nanoarchitectonics based sensing in comparison to conventional analytical methods. Finally, with this Account, we hope to spark new chiral molecule sensing strategies by fundamentally understanding chiral recognition and engineering soft nanoarchitectonics with programmable structures and predictable sensing properties.

First principle approach to elucidate transport properties through L-glutamic acid-based molecular devices using symmetrical electrodes

Protein-based electronics is one of the emerging technology in which inventive electronic devices are being adduced and developed based on the selective actions of specific proteins. The explicit actions can be predicted if the building blocks of proteins (i.e., amino acids) are studied decorously. We emphasize our work on electronic transport properties of L-glutamic acid (i.e., L-amino acid) stringed to gold, silver, and copper electrodes, respectively, to form three distinct devices. For our calculations, we employ NEGF-DFT approach using self-consistent function. Electronic coupling and tunneling barriers between the molecule and the electrodes have been emphasized with an inception of delocalization of molecular orbitals within the device. We observe strong correlation between tunneling barrier and Mulliken charge transfer between molecule and electrodes. The asymmetrical carbon chain (-CH₂) within the molecule exhibits negative differential resistance (NDR) and rectification ratio. The device using molecule with copper electrodes exhibits the highest peak to valley current ratio of 1.84. The rectification ratio of the device with gold, silver, and copper electrodes is 2.35, 2.25, and 15.62, respectively, at finite bias. These results yield fresh insight on the potential of L-glutamic acid like bio-molecule in the emerging field of proteotronics.

Electrical recognition of the twenty proteinogenic amino acids using an aerolysin nanopore

Efforts to sequence single protein molecules in nanopores1-5 have been hampered by the lack of techniques with sufficient sensitivity to discern the subtle molecular differences among all twenty amino acids. Here we report ionic current detection of all twenty proteinogenic amino acids in an aerolysin nanopore with the help of a short polycationic carrier. Application of molecular dynamics simulations revealed that the aerolysin nanopore has a built-in single-molecule trap that fully confines a polycationic carrier-bound amino acid inside the sensing region of the aerolysin. This structural feature means that each amino acid spends sufficient time in the pore for sensitive measurement of the excluded volume of the amino acid. We show that distinct current blockades in wild-type aerolysin can be used to identify 13 of the 20 natural amino acids. Furthermore, we show that chemical modifications, instrumentation advances and nanopore engineering offer a route toward identification of the remaining seven amino acids. These findings may pave the way to nanopore protein sequencing.

Single-Molecule Electrical Detection: A Promising Route toward the Fundamental Limits of Chemistry and Life Science

The ultimate limit of analytical chemistry is single-molecule detection, which allows one to visualize the dynamic processes of chemical/biological interactions with single-molecule or single-event sensitivity and hence enables the study of stochastic fluctuations under equilibrium conditions and the observation of time trajectories and reaction pathways of individual species in nonequilibrated systems. In addition, such studies may also allow the direct observation of novel microscopic quantum effects and fundamental discoveries of underlying molecular mechanisms in organic reactions and biological processes that are not accessible in ensemble experiments, thus providing unique opportunities to solve the key problems of physical, chemical, and life sciences. Consequently, the field of single-molecule detection has received considerable attention and has witnessed tremendous advances in different directions in combination with other disciplines. This Account describes our ongoing work on the development of groundbreaking methods (termed "single-molecule electrical approaches") of translating the detailed processes of chemical reactions or biological functions into detectable electrical signals at the single-event level on the platform of single-molecule electronic devices, with a particular focus on graphene-molecule-graphene single-molecule junctions (GMG-SMJs) and silicon-nanowire-based single-molecule electrical nanocircuits. These nanocircuit-based architectures are complementary to conventional optical or mechanical techniques but exhibit obvious advantages such as the absence of problems associated with bleaching and fluorescent labeling. Dash-line lithography (DLL) is an efficient lithographic method of cutting graphene and forming carboxylic-acid-functionalized nanogapped graphene point contact arrays developed to address the formidable challenges of molecular device fabrication difficulty and poor stability. Molecules of interest terminated by amines on both ends can be covalently sandwiched between graphene point contacts to create high-throughput robust GMG-SMJs containing only one molecule as the conductive element. In conjunction with the ease of device fabrication and device stability, this feature distinguishes GMG-SMJs as a new testbed platform for single-molecule analysis characterized by high temporal resolution and superior signal-to-noise ratios. By exploiting the DLL method, we have fabricated molecular devices that are sensitive to external stimuli and are capable of transducing chemical/biochemical events into electrical signals at the single-molecule level, with notable examples including host-guest interaction, hydrogen bond dynamics, DNA intercalation, photoinduced conformational transition, carbocation formation, nucleophilic addition, and stereoelectronic effect. In addition to GMG-SMJs and considering compatibility with the silicon-based industry, we have also developed a reliable method of point-functionalizing silicon-nanowire-based nanotransistors to afford single-molecule electrical nanocircuits. This approach proved to be a robust platform for single-molecule electrical analysis capable of probing fast dynamic processes such as single-protein detection, DNA hybridization/polymorphism, and motor rotation dynamics. The above systematic investigations emphasize the importance and unique advantages of universal single-molecule electrical approaches for realizing direct, label-free, real-time electrical measurements of reaction dynamics with single-event sensitivity. These approaches promise a fascinating mainstream platform to explore the dynamics of stochastic processes in chemical/biological systems as well as gain information in fields ranging from reaction chemistry for elucidating the intrinsic mechanisms to genomics or proteomics for accurate molecular and even point-of-care clinical diagnoses.

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.

Strategies for Development of a Next-Generation Protein Sequencing Platform

Proteomic analysis can be a critical bottleneck in cellular characterization. The current paradigm relies primarily on mass spectrometry of peptides and affinity reagents (i.e., antibodies), both of which require a priori knowledge of the sample. An unbiased protein sequencing method, with a dynamic range that covers the full range of protein concentrations in proteomes, would revolutionize the field of proteomics, allowing a more facile characterization of novel gene products and subcellular complexes. To this end, several new platforms based on single-molecule protein-sequencing approaches have been proposed. This review summarizes four of these approaches, highlighting advantages, limitations, and challenges for each method towards advancing as a core technology for next-generation protein sequencing.

Spontaneous ssDNA stretching on graphene and hexagonal boron nitride in plane heterostructures

Single-stranded DNA (ssDNA) molecules in solution typically form coiled structures, therefore stretching ssDNA is extremely crucial before applying any nanotechnology for ssDNA analysis. Recent advances in material fabrication enable the deployment of nanochannels to manipulate, stretch, sort and map double-stranded DNA (dsDNA) molecules, however nanochannels fail to stretch ssDNA molecules due to the ultra-short persistence length and the potential nonspecific-interaction-induced clogging. Given the significance of ssDNA stretching in genome analysis, here we report an ssDNA stretching platform: two dimensional in-plane heterostructure comprising graphene and hexagonal boron nitride (h-BN), and show that ssDNA can be stretched on a h-BN nanostripe sandwiched between two adjacent graphene domains (“nanochannel”). We further show that with a biasing voltage the stretched ssDNA can be electrophoretically transported along the “nanochannel”, allowing easy controls/manipulations. When being conveniently integrated with existing atomic resolution sensors, the heterostructure platform paves the way for sequencing DNA on a planar surface.

Single stranded DNA analysis is of interest for a range of applications; however, natural folding of DNA can cause problems with this. Here, the authors report on the in silico analysis of graphene and hexagonal-boron-nitride structures for the stretching and unfolding of DNA to allow for analysis.

Can One Define the Conductance of Amino Acids?

We studied the electron-transport properties of ten different amino acids and one dimer (di-methionine) using the mechanically controlled break-junction (MCBJ) technique. For methionine and cysteine, additional measurements were performed with the scanning tunneling microscope break-junction (STM-BJ) technique. By means of a statistical clustering technique, we identified several conductance groups for each of the molecules considered. Ab initio calculations revealed that the observed broad conductance distribution stems from the possibility of various binding geometries which can be formed during stretching combined with a multitude of possible conformational changes. The results suggest that it would be helpful to explore different experimental techniques such as recognition tunneling and conditions to help identify the nature of amino-acid-based junctions even further, for example, with the goal to establish a firm platform for their unambiguous recognition by tunneling break-junction experiments.

The influence of a solvent on the electronic transport across diamondoid-functionalized biosensing electrodes

Electrodes embedded in nanopores have the potential to detect the identity of biomolecules, such as DNA. This identification is typically being done through electronic current measurements across the electrodes in a solvent. In this work, using quantum-mechanical calculations, we qualitatively present the influence of this solvent on the current signals. For this, we model electrodes functionalized with a small diamond-like molecule known as diamondoid and place a DNA nucleotide within the electrode gap. The influence of an aqueous solvent is taken explicitly into account through Quantum-Mechanics/Molecular Mechanics (QM/MM) simulations. From these, we could clearly reveal that at the (111) surface of the Au electrode, water molecules form an adlayer-like structure through hydrogen bond networks. From the temporal evolution of the hydrogen bond between a nucleotide and the functionalizing diamondoid, we could extract information on the conductance across the device. In order to evaluate the influence of the solvent, we compare these results with ground-state electronic structure calculations in combination with the non-equilibrium Green's function (NEGF) approach. These allow access to the electronic transport across the electrodes and show a difference in the detection signals with and without the aqueous solution. We analyze the results with respect to the density of states in the device. In the end, we demonstrate that the presence of water does not hinder the detection of a mutation over a healthy DNA nucleotide. We discuss these results in view of sequencing DNA with nanopores.

Protein fingerprinting with digital sequences of linear protein subsequence volumes: a computational study

Proteins in a proteome can be identified from a sequence of K integers equal to the digitized volumes of subsequences with L residues from the primary sequence of a stretched protein. Exhaustive computations on the proteins of Helicobacter pylori (UniProt id UP000000210) with L and K in the range 4-8 show that approx. 90% of the proteins can be identified uniquely in this manner. This computational result can be translated into practice with a nanopore, an emerging technology that does not require analyte immobilization, proteolysis or labeling. Unlike other methods, most of which focus on a specific target protein, nanopore-based methods enable the identification of multiple proteins from a sample in a single run. Recent work by Kennedy, Kolmogorov and associates shows that the blockade current due to a protein molecule translocating through a nanopore is roughly proportional to one or more contiguous residues. The present study points to a modified version in which the volumes of subsequences (rather than of single residues) may be obtained by integrating the blockade current due to L contiguous residues. The advantages arising from this include lower detector bandwidth, elimination of the homopolymer problem and reduced noise. Because an identifier is based on near as well as distant (up to 2KL-L) residues, this approach uses more global information than an approach based on single residues and short-range correlations. The results of the study, which are available in a data supplement, are discussed in detail. Potential implementation issues are addressed.

Insights into protein sequencing with an α-Hemolysin nanopore by atomistic simulations

Single molecule protein sequencing would represent a disruptive burst in proteomic research with important biomedical impacts. Due to their success in DNA sequencing, nanopore based devices have been recently proposed as possible tools for the sequencing of peptide chains. One of the open questions in nanopore protein sequencing concerns the ability of such devices to provide different signals for all the 20 standard amino acids. Here, using equilibrium all-atom molecular dynamics simulations, we estimated the pore clogging in α-Hemolysin nanopore associated to 20 different homopeptides, one for each standard amino acid. Our results show that pore clogging is affected by amino acid volume, hydrophobicity and net charge. The equilibrium estimations are also supported by non-equilibrium runs for calculating the current blockades for selected homopeptides. Finally, we discuss the possibility to modify the α-Hemolysin nanopore, cutting a portion of the barrel region close to the trans side, to reduce spurious signals and, hence, to enhance the sensitivity of the nanopore.

Atomically defined angstrom-scale all-carbon junctions

Full-carbon electronics at the scale of several angstroms is an expeimental challenge, which could be overcome by exploiting the versatility of carbon allotropes. Here, we investigate charge transport through graphene/single-fullerene/graphene hybrid junctions using a single-molecule manipulation technique. Such sub-nanoscale electronic junctions can be tuned by band gap engineering as exemplified by various pristine fullerenes such as C₆₀, C₇₀, C₇₆ and C₉₀. In addition, we demonstrate further control of charge transport by breaking the conjugation of their π systems which lowers their conductance, and via heteroatom doping of fullerene, which introduces transport resonances and increase their conductance. Supported by our combined density functional theory (DFT) calculations, a promising future of tunable full-carbon electronics based on numerous sub-nanoscale fullerenes in the large family of carbon allotropes is anticipated.

All-carbon electronics holds promise beyond the conventional silicon-based electronics, but it remains challenging to manufacture them with well-defined structures thus tunability. Tan et al. control charge transport in single-molecule junctions using different fullerenes between graphene electrodes.

Discrimination of Protein Amino Acid or Its Protonated State at Single-Residue Resolution by Graphene Nanopores

The function of a protein is determined by the composition of amino acids and is essential to proteomics. However, protein sequencing remains challenging due to the protein's irregular charge state and its high-order structure. Here, a proof of principle study on the capability of protein sequencing by graphene nanopores integrated with atomic force microscopy is performed using molecular dynamics simulations. It is found that nanopores can discriminate a protein sequence and even its protonation state at single-residue resolution. Both the pulling forces and current blockades induced by the permeation of protein residues are found to be highly correlated with the type of amino acids, which makes the residues identifiable. It is also found that aside from the dimension, both the conformation and charge state of the residue can significantly influence the force and current signal during its permeation through the nanopore. In particular, due to the electro-osmotic flow effect, the blockade current for the double-protonated histidine is slightly smaller than that for single-protonated histidine, which makes it possible for discrimination of different protonation states of amino acids. The results reported here present a novel protein sequencing scheme using graphene nanopores combined with nanomanipulation technology.

Enhancing the optical detection of mutants from healthy DNA with diamondoids

A polarized laser pulse can distinguish between healthy and mutated DNA nucleotides hydrogen bonded to small diamond cages.

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.

Integrated Solid-State Nanopore Electrochemistry Array for Sensitive, Specific, and Label-Free Biodetection

Nanopore ionic current measurement is currently a prevailing readout and offers considerable opportunities for bioassays. Extending conventional electrochemistry to nanoscale space, albeit noteworthy, remains challenging. Here, we report a versatile electrochemistry array established on a nanofluidic platform by controllably depositing gold layers on the two outer sides of anodic aluminum oxide (AAO) nanopores, leading to form an electrochemical microdevice capable of performing amperometry in a label-free manner. Electroactive species ferricyanide ions passing through gold-decorated nanopores act as electrochemical indicator to generate electrolytic current signal. The electroactive species flux that dominates current signal response is closely related to the nanopore permeability. Such well-characteristic electrolytic current-species flux correlation lays a premise for quantitative electrochemical analysis. As a proof-of-concept demonstration, we preliminarily verify the analytical utility by detection of nucleic acid and protein at picomolar concentration levels. Universal surface modification and molecule assembly, specific target recognition and reliable signal output in nanopore enable direct electrochemical detection of biomolecules without the need of cumbersome probe labeling and signal amplification.

Highly parallel single-molecule identification of proteins in zeptomole-scale mixtures

The identification and quantification of proteins lags behind DNA-sequencing methods in scale, sensitivity, and dynamic range. Here, we show that sparse amino acid-sequence information can be obtained for individual protein molecules for thousands to millions of molecules in parallel. We demonstrate selective fluorescence labeling of cysteine and lysine residues in peptide samples, immobilization of labeled peptides on a glass surface, and imaging by total internal reflection microscopy to monitor decreases in each molecule's fluorescence after consecutive rounds of Edman degradation. The obtained sparse fluorescent sequence of each molecule was then assigned to its parent protein in a reference database. We tested the method on synthetic and naturally derived peptide molecules in zeptomole-scale quantities. We also fluorescently labeled phosphoserines and achieved single-molecule positional readout of the phosphorylated sites. We measured >93% efficiencies for dye labeling, survival, and cleavage; further improvements should enable studies of increasingly complex proteomic mixtures, with the high sensitivity and digital quantification offered by single-molecule sequencing.

Cucurbituril mediated single molecule detection and identification via recognition tunneling

Recognition tunneling (RT) is an emerging technique for investigating single molecules in a tunnel junction. We have previously demonstrated its capability of single molecule detection and identification, as well as probing the dynamics of intermolecular bonding at the single molecule level. Here by introducing cucurbituril as a new class of recognition molecule, we demonstrate a powerful platform for electronically investigating the host-guest chemistry at single molecule level. In this report, we first investigated the single molecule electrical properties of cucurbituril in a tunnel junction. Then we studied two model guest molecules, aminoferrocene and amantadine, which were encapsulated by cucurbituril. Small differences in conductance and lifetime can be recognized between the host-guest complexes with the inclusion of different guest molecules. By using a machine learning algorithm to classify the RT signals in a hyper dimensional space, the accuracy of guest molecule recognition can be significantly improved, suggesting the possibility of using cucurbituril molecule for single molecule identification. This work enables a new class of recognition molecule for RT technique and opens the door for detecting a vast variety of small molecules by electrical measurements.

Paving the way to single-molecule protein sequencing

Proteins are major building blocks of life. The protein content of a cell and an organism provides key information for the understanding of biological processes and disease. Despite the importance of protein analysis, only a handful of techniques are available to determine protein sequences, and these methods face limitations, for example, requiring a sizable amount of sample. Single-molecule techniques would revolutionize proteomics research, providing ultimate sensitivity for the detection of low-abundance proteins and the realization of single-cell proteomics. In recent years, novel single-molecule protein sequencing schemes that use fluorescence, tunnelling currents and nanopores have been proposed. Here, we present a review of these approaches, together with the first experimental efforts towards their realization. We discuss their advantages and drawbacks, and present our perspective on the development of single-molecule protein sequencing techniques.

Single-Molecule Dynamics and Discrimination between Hydrophilic and Hydrophobic Amino Acids in Peptides, through Controllable, Stepwise Translocation across Nanopores

In this work, we demonstrate the proof-of-concept of real-time discrimination between patches of hydrophilic and hydrophobic monomers in the primary structure of custom-engineered, macro-dipole-like peptides, at uni-molecular level. We employed single-molecule recordings to examine the ionic current through the α-hemolysin (α-HL) nanopore, when serine or isoleucine residues, flanked by segments of oppositely charged arginine and glutamic amino acids functioning as a voltage-dependent “molecular brake” on the peptide, were driven at controllable rates across the nanopore. The observed differences in the ionic currents blockades through the nanopore, visible at time resolutions corresponding to peptide threading through the α-HL’s constriction region, was explained by a simple model of the volumes of electrolyte excluded by either amino acid species, as groups of serine or isoleucine monomers transiently occupy the α-HL. To provide insights into the conditions ensuring optimal throughput of peptide readout through the nanopore, we probed the sidedness-dependence of peptide association to and dissociation from the electrically and geometrically asymmetric α-HL.

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

In the present study, we demonstrate a tunneling nanogap technique to identify individual RNA nucleotides, which can be used as a mechanism to read the nucleobases for direct sequencing of RNA in a solid-state nanopore. The tunneling nanogap is composed of two electrodes separated by a distance of <3 nm and functionalized with a recognition molecule. When a chemical entity is captured in the gap, it generates electron tunneling currents, a process we call recognition tunneling (RT). Using RT nanogaps created in a scanning tunneling microscope (STM), we acquired the electron tunneling signals for the canonical and two modified RNA nucleotides. To call the individual RNA nucleotides from the RT data, we adopted a machine learning algorithm, support vector machine (SVM), for the data analysis. Through the SVM, we were able to identify the individual RNA nucleotides and distinguish them from their DNA counterparts with reasonably high accuracy. Since each RNA nucleoside contains a hydroxyl group at the 2'-position of its sugar ring in an RNA strand, it allows for the formation of a tunneling junction at a larger nanogap compared to the DNA nucleoside in a DNA strand, which lacks the 2' hydroxyl group. It also proves advantageous for the manufacture of RT devices. This study is a proof-of-principle demonstration for the development of an RT nanopore device for directly sequencing single RNA molecules, including those bearing modifications.

Single-File Protein Translocations through Graphene-MoS2 Heterostructure Nanopores

Successfully threading unfolded protein molecules through nanopores whose sizes are comparable to that of an amino acid is a prerequisite for the nanopore-based protein sequencing method that promises to be high-throughput and low-cost. While the electric driving method can be effective for a homogeneously charged DNA molecule, it fails to drive an unfolded protein through a nanopore because the net charge of a protein fragment inside of the pore (where the electric field exists) can be positive, negative, or neutral. Here we propose and demonstrate by molecular dynamics simulations protein transport through a nanopore in a quasi-two-dimensional heterostructure stacked together by graphene and molybdenum disulfide (MoS₂) nanosheets. Thanks to different van der Waals interactions ( U) between a protein molecule and different 2D surfaces, it is energetically favorable for protein to progressively move from the MoS₂ surface to the graphene surface (more negative U) through a nanopore in the heterostructure.

Single-molecule peptide fingerprinting

Proteomic analyses provide essential information on molecular pathways of cellular systems and the state of a living organism. Mass spectrometry is currently the first choice for proteomic analysis. However, the requirement for a large amount of sample renders a small-scale proteomics study challenging. Here, we demonstrate a proof of concept of single-molecule FRET-based protein fingerprinting. We harnessed the AAA+ protease ClpXP to scan peptides. By using donor fluorophore-labeled ClpP, we sequentially read out FRET signals from acceptor-labeled amino acids of peptides. The repurposed ClpXP exhibits unidirectional processing with high processivity and has the potential to detect low-abundance proteins. Our technique is a promising approach for sequencing protein substrates using a small amount of sample.