Alditols, which have a sweet taste but produce much lower calories than natural sugars, are widely used as artificial sweeteners. Alditols are the reduced forms of monosaccharide aldoses, and different alditols are diastereomers or epimers of each other and direct and rapid identification by conventional methods is difficult. Nanopores, which are emerging single-molecule sensors with exceptional resolution when engineered appropriately, are useful for the recognition of diastereomers and epimers. In this work, direct distinguishing of alditols corresponding to all 15 monosaccharide aldoses was achieved by a boronic acid-appended hetero-octameric Mycobacterium smegmatis porin A (MspA) nanopore (MspA-PBA). Thirteen alditols including glycerol, erythritol, threitol, adonitol, arabitol, xylitol, mannitol, sorbitol, allitol, dulcitol, iditol, talitol, and gulitol (l-sorbitol) could be fully distinguished, and their sensing features constitute a complete nanopore alditol database. To automate event classification, a custom machine-learning algorithm was developed and delivered a 99.9% validation accuracy. This strategy was also used to identify alditol components in commercially available “zero-sugar” drinks and healthcare products, suggesting their use in rapid and sensitive quality control for the food and medical industry.

The nanopore is emerging as a means of singlemolecule protein sensing. However, proteins demonstrate different charge properties, which complicates the design of a sensor that can achieve simultaneous sensing of differently charged proteins. In this work, we introduce an asymmetric electrolyte buffer combined with the Mycobacterium smegmatis porin A (MspA) nanopore to form an electroosmotic flow (EOF) trap. Apo-and holo-myoglobin, which differ in only a single heme, can be fully distinguished by this method. Direct discrimination of lysozyme, apo/holo-myoglobin, and the ACTR/NCBD protein complex, which are basic, neutral, and acidic proteins, respectively, was simultaneously achieved by the MspA EOF trap. To automate event classification, multiple event features were extracted to build a machine learning model, with which a 99.9% accuracy is achieved. The demonstrated method was also applied to identify single molecules of α-lactalbumin and βlactoglobulin directly from whey protein powder. This protein-sensing strategy is useful in direct recognition of a protein from a mixture, suggesting its prospective use in rapid and sensitive detection of biomarkers or real-time protein structural analysis.

Ribonucleotides, which widely exist in all living organisms and are essential to both physiological and pathological processes, can naturally appear as ribonucleoside mono-, di-, and triphosphates. Natural ribonucleotides can also dynamically switch between different phosphorylated forms, posing a great challenge for sensing. A specially engineered nanopore sensor is promising for full discrimination of all canonical ribonucleoside mono-, di-, and triphosphates. However, such a demonstration has never been reported, due to the lack of a suitable nanopore sensor that has a sufficient resolution. In this work, we utilized a phenylboronic acid (PBA) modified Mycobacterium smegmatis porin A (MspA) hetero-octamer for ribonucleotide sensing. Twelve types of ribonucleotides, including mono-, di-, and triphosphates of cytidine (CMP, CDP, CTP), uridine (UMP, UDP, UTP), adenosine (AMP, ADP, ATP), and guanosine (GMP, GDP, GTP) were simultaneously discriminated. A machine-learning algorithm was also developed, which achieved a general accuracy of 99.9% for ribonucleotide sensing. This strategy was also further applied to identify ribonucleotide components in ATP tablets and injections. This sensing strategy provides a direct, accurate, easy, and rapid solution to characterize ribonucleotide components in different phosphorylated forms.

Enantiomers, chiral isomers with opposite chirality, typically demonstrate differences in their pharmacological activity, metabolism, and toxicity. However, direct discrimination between enantiomers is challenging due to their similar physiochemical properties. Following the strategy of programmable nanoreactors for stochastic sensing (PNRSS), introduction of phenylboronic acid (PBA) to a Mycobacterium smegmatis porin A (MspA) assists in the identification of the enantiomers of norepinephrine and epinephrine. Using a machine learning algorithm, identification of the enantiomers has been achieved with an accuracy of 98.2%. The enantiomeric excess (ee) of a mixture of enantiomeric catecholamines was measured to determine the enantiomeric purity. This sensing strategy is a faster method for the determination of ee values than liquid chromatography−mass spectrometry and is useful as a quality control in the industrial production of enantiomeric drugs.

Isomers of some chemical compounds may be dynamically interconvertible. Due to a lack of sensing methods with a sufficient resolution, however, direct monitoring of such processes can be difficult. Engineered Mycobacterium smegmatis porin A (MspA) nanopores can be applied as nanoreactors so that chemical reactions can be directly monitored. Here, an MspA modified with a phenylboronic acid (PBA) adapter was prepared and was used to observe dynamic interconversion between chiral configurations of boronate esters, which appears as telegraphic switching on top of nanopore events. The mechanism of this behavior was further confirmed by trials with different halogenated catechols, dopamine, adenosine, 1,2-propanediol, and (2R,3R)-2,3-butanediol, and its generality has been demonstrated. These results suggest that an engineered MspA possesses an exceptional resolution in its monitoring of chemical reaction processes and may inspire the future design of nanopore small-molecule sensors.