Solid-state material-based protein sequencing techniques have emerged as a paradigm that is capable of decoding the sequence of amino acids in protein by electrical detection. We studied a graphene nanoslit device for ultrafast protein sequencing using electronic transport calculations. The first-principles consistent-exchange van der Waals density functional (vdW-DFcx) calculations have been employed to study the structural and electronic properties of the pristine graphene nanoslit and graphene nanoslit + amino acid systems. Ten amino acid molecules, namely, alanine (Ala), arginine (Arg), aspartic acid (Asp), glutamic acid (Glu), glycine (Gly), histidine (His), lysine (Lys), phenylalanine (Phe), proline (Pro), and tyrosine (Tyr), are considered. The electronic quantum transport properties of pristine graphene nanoslit and graphene nanoslit + amino acid systems are studied using the nonequilibrium Green's function (NEGF) combined with the density functional theory (DFT) approach. Significant changes in the electronic transmission conductance are observed in the graphene nanoslit device in the presence of certain amino acids. The computed conductance sensitivity and current− voltage (I−V) characteristics indicate that selective identification of amino acids is possible through the graphene nanoslit device. This study may be a practical guide toward the development of a graphene nanoslit-based device for ultrafast protein sequencing applications.

Recent advances in cost-effective, ultra-rapid, and efficient DNA sequencing are in the saddle of the advancement of the personalization of medicines for understanding and early-stage detection of several killer diseases. Paying attention to a timely need for the development of solid-state nanodevices for rapid and controlled identification of DNA nucleotides, in this report, we theoretically explored the potential of labeling techniques in the sequencing of DNA nucleotides through solid-state graphene nanogap electrodes using the quantum tunneling current approach. Our study boasts the idea that labeling of DNA nucleotides can solve major hurdles of DNA sequencing, such as improving the signal-to-noise ratio, slowing down translocation velocity, and controlling orientational variations. Employing the first-principle density functional theory study, we identify unique interaction energy values for each labeled nucleotide having remarkable differences in the range of 0.10–0.74 eV. The zero-bias transmission spectra of the proposed setup suggest that the detection of the nucleotides is possible by applying very low gate voltages. Moreover, the labeling of nucleotides amplifies the conductance sensitivity considerably. I–V characteristics suggest that electrical recognition of each labeled nucleotide can be carried out at both lower (0.3 V) and higher (0.8 V) bias voltages with single-molecule resolution, although the maximum current sensitivity is observed at a higher bias voltage. The proposed sequencing device possesses high sensitivity and selectivity characteristics that are crucial for experimental purposes. We find that our results are rich compared to unlabeled nucleotides-based graphene nanopore/nanogap devices. Hence, the study will certainly motivate the experimentalists toward the application of a labeled DNA nucleotide system for ultra-rapid DNA sequencing by using the tunneling current approach.

An important pursuit in medical research is to develop a fast and low-cost technique capable of sequencing the entire human genome (DNA) and epigenome (methylated DNA) de novo. Such a method would enable the advancement of personalized medicines and a universal cancer screening test. In this regard, we introduce a novel supervised machine learning (ML) approach for ultrarapid prediction of transmission function of DNA and methylated DNA nucleobases using a MXene-based nanochannel device. The proposed device can detect the targeted nucleobases with good transmission sensitivity. The random forest regression (RFR) model can predict the transmission function of each unknown nucleobase with root-mean-square error (RMSE) values as low as 0.16. Interestingly, if the machine is trained with the dataset of methylated DNA nucleobases, it can selectively identify all four DNA nucleobases with good accuracy. Therefore, our study demonstrates an effective approach for quick and accurate whole-genome and epigenome sequencing applications.

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.

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