Carbohydrates are one of the four main building blocks of life, and are categorized as monosaccharides (sugars), oligosaccharides and polysaccharides. Each sugar can exist in two alternative anomers (in which a hydroxy group at C-1 takes different orientations) and each pair of sugars can form different epimers (isomers around the stereocentres connecting the sugars). This leads to a vast combinatorial complexity, intractable to mass spectrometry and requiring large amounts of sample for NMR characterization. Combining measurements of collision cross section with mass spectrometry (IM–MS) helps, but many isomers are still difficult to separate. Here, we show that recognition tunnelling (RT) can classify many anomers and epimers via the current fluctuations they produce when captured in a tunnel junction functionalized with recognition molecules. Most importantly, RT is a nanoscale technique utilizing sub-picomole quantities of analyte. If integrated into a nanopore, RT would provide a unique approach to sequencing linear polysaccharides.

DNA, RNA and Protein are three pivotal biomolecules in human and other organisms, playing decisive roles in functionality, appearance, diseases development and other physiological phenomena. Hence, sequencing of these biomolecules acquires the prime interest in the scientific community. Single molecular identification of their building blocks can be done by a technique called Recognition Tunneling (RT) based on Scanning Tunneling Microscope (STM). A single layer of specially designed recognition molecule is attached to the STM electrodes, which trap the targeted molecules (DNA nucleoside monophosphates, RNA nucleoside monophosphates or amino acids) inside the STM nanogap. Depending on their different binding interactions with the recognition molecules, the analyte molecules generate stochastic signal trains accommodating their “electronic fingerprints”. Signal features are used to detect the molecules using a machine learning algorithm and different molecules can be identified with significantly high accuracy. This, in turn, paves the way for rapid, economical nanopore sequencing platform, overcoming the drawbacks of Next Generation Sequencing (NGS) techniques.

To read DNA nucleotides with high accuracy in an STM tunnel junction a series of nitrogen-based heterocycles were designed and examined to check their capabilities to interact with naturally occurring DNA nucleotides by hydrogen bonding in the tunnel junction. These recognition molecules are Benzimidazole, Imidazole, Triazole and Pyrrole. Benzimidazole proved to be best among them showing DNA nucleotide classification accuracy close to 99%. Also, Imidazole reader can read an abasic monophosphate (AP), a product from depurination or depyrimidination that occurs 10,000 times per human cell per day.

In another study, I have investigated a new universal reader, 1-(2-mercaptoethyl)pyrene (Pyrene reader) based on stacking interactions, which should be more specific to the canonical DNA nucleosides. In addition, Pyrene reader showed higher DNA base-calling accuracy compare to Imidazole reader, the workhorse in our previous projects. In my other projects, various amino acids and RNA nucleoside monophosphates were also classified with significantly high accuracy using RT. Twenty naturally occurring amino acids and various RNA nucleosides (four canonical and two modified) were successfully identified. Thus, we envision nanopore sequencing biomolecules using Recognition Tunneling (RT) that should provide comprehensive betterment over current technologies in terms of time, chemical and instrumental cost and capability of de novo sequencing.