The device is built on MspA, a mushroom-shaped porin drawn from the bacterium Mycobacterium smegmatis. MspA has long been a favorite scaffold in nanopore research because of its conical geometry and its narrow, well-defined constriction, which sits at the narrowest point of the pore and acts as the readout zone where passing molecules modulate an ionic current. What the team led by Shuo Huang of Nanjing University did was to graft a bespoke chemical adaptor into that constriction. The adaptor, called maleimido-C2-formylphenylboronic acid, or maleimido-C2-FPBA, combines two reactive functions in one compact molecule: a maleimide group that covalently anchors the adaptor to a single cysteine residue engineered into the pore wall, and an ortho-formylphenylboronic acid group that juts into the lumen of the pore where it can interact with passing analytes.

The chemistry of that boronic acid head group is what gives the pore its versatility. Boronic acids are famous for forming reversible covalent bonds, known as boronate esters, with cis-diols — the paired hydroxyl groups found abundantly on sugars — which explains why FPBA-equipped pores excel at recognizing saccharides. But the formyl group adjacent to the boron adds a second recognition mode: it can condense with the N-terminal amino groups of amino acids and peptides to form iminoboronate adducts, a reversible linkage that has become a powerful tool in chemical biology. This dual chemistry means that amino acids, sugars and even nucleotides, which carry phosphate and hydroxyl groups capable of engaging the boron center, each generate their own characteristic electrochemical fingerprints as they dock at and escape from the constriction.

When a voltage is applied across a lipid membrane containing a single MspA-FPBA pore, ions stream through the opening and produce a steady baseline current. Each time an analyte molecule enters the pore and binds transiently to the FPBA adaptor, the ionic current drops in a brief blockade whose depth, duration, noise profile and shape encode information about the molecule’s identity. The researchers systematically characterized these events for each of their target molecules. Notably, the earlier versions of related sensors had struggled: an MspA pore carrying only a phenylboronic acid adaptor failed to detect the amino acid phenylalanine, and a nickel-nitrilotriacetic acid modified pore registered no events for fructose or guanosine monophosphate. Only the combined formyl-boronate chemistry of FPBA proved capable of capturing this chemically diverse set of analytes with strong, well-resolved signals.

The catalog of molecules the engineered pore can discriminate is remarkable. On the amino acid side, it resolves all twenty standard proteinogenic building blocks plus selenocysteine, the twenty-first genetically encoded amino acid, and it further distinguishes three post-translationally modified residues: N6-acetyllysine, O-phosphotyrosine and asymmetric dimethylarginine, representing acetylation, phosphorylation and methylation, respectively. On the nucleotide side, it identifies the four canonical nucleoside monophosphates — AMP, UMP, CMP and GMP — as well as three epigenetically relevant variants: pseudouridine monophosphate, N6-methyladenosine monophosphate and inosine monophosphate. Four monosaccharides, including iduronic acid, L-arabinose, D-fructose and N-acetyl-D-glucosamine, and five short peptides round out the panel. Some analytes produce more than one characteristic event type — cysteine, histidine and lysine each yield two event classes, and arabinose produces three — yet the machine learning layer absorbs this complexity without difficulty.

That machine learning layer is central to the system’s performance. The researchers extracted nine numerical features from each nanopore event, including the mean current blockade, its standard deviation, a local noise ratio, the minimum and maximum current values, the event range, the dwell time, and the skewness and kurtosis of the blockade shape, forming a nine-dimensional fingerprint for every single-molecule encounter. They then benchmarked several classifiers using ten-fold cross-validation and found that an ensemble of bagged decision trees — an approach rooted in Leo Breiman’s classic random forests methodology — performed best, reaching the headline accuracy of 98.7 percent across the full panel of 24 amino acids, 7 nucleotides, 4 sugars and 5 peptides. The resulting confusion matrix shows cleanly separated classes even for chemically subtle distinctions, such as proline versus methionine, which the model resolves with validation accuracies of 98.8 and 99.4 percent respectively.

Perhaps the most consequential demonstration is the sensor’s ability to generalize. Because each class of analyte — amino acids, nucleotides, saccharides, peptides — produces predictable families of event signatures, the trained model can recognize the category of a molecule it has never seen in its training data, a capability the team calls few-shot learning. When the researchers filtered a yeast cell extract into the pore chamber, the classifier correctly assigned events to their proper analyte classes and further identified individual amino acids and nucleotides within those classes, despite the mixture being far messier than any of the purified standards used in training. This out-of-database classification matters enormously for real-world applications, where an unknown sample cannot be presumed to contain only molecules with pre-archived reference signatures.

To showcase practical analytical chemistry, the team applied MspA-FPBA to the compositional analysis of glycopeptides — peptides decorated with sugar chains, which are central to protein glycosylation, one of the most important and clinically consequential post-translational modifications in biology. They digested a glycosylated heptapeptide with leucine aminopeptidase, an enzyme that clips amino acids from the N-terminus until it stalls at the bulky glycosylation site. As the enzyme released glycine, methionine, glutamine and arginine one by one, the nanopore and its machine learning classifier identified each in real time, while the residual glycosylated tripeptide fragment generated its own distinctive events. In this way, a single measurement revealed both the amino acid composition preceding the sugar attachment site and the presence of the glycosylated remnant — information that would ordinarily require multiple orthogonal techniques.

The researchers are candid that this is a waypoint rather than a destination. In their abstract they note that future integration of a hydrolase enzyme with MspA-FPBA could turn the system into a self-consuming reader that degrades larger biomolecules into their building blocks and identifies them sequentially — an architecture that would echo the motor-protein ratcheting used in commercial nanopore DNA sequencing and that has already been explored conceptually for exopeptidase-driven protein sequencing. Challenges remain, including throughput, the concentration ranges required for reliable detection, and extending the panel beyond the current analyte set. Still, the convergence of a single multifunctional chemical adaptor, a rugged bacterial pore and modern machine learning delivers something the field has been missing: one sensor that speaks the chemical languages of proteomics, glycomics and transcriptomics at the single-molecule level. If the promised hydrolase coupling matures, the humble mycobacterial porin may find itself at the heart of a universal molecular reader, one blockade at a time.

Subject of Research: An engineered MspA nanopore with a formylphenylboronic acid adaptor for simultaneous single-molecule identification of amino acids, nucleotides, saccharides and peptides

Article Title: An engineered nanopore identifies saccharides, amino acids, peptides and ribonucleotides

Article References: Yao, L., Wang, Z., Chen, J., Sun, W., Wang, K., Xiao, Y., Zhang, H., Li, W., Wang, Y., Zhao, L., Dai, X., Qian, L., Zhang, P., & Huang, S. (2026). An engineered nanopore identifies saccharides, amino acids, peptides and ribonucleotides. Nature Biotechnology. https://doi.org/10.1038/s41587-026-03308-9

Image Credits: AI Generated

DOI: 10.1038/s41587-026-03308-9

Keywords: nanopore sensing, MspA, formylphenylboronic acid, amino acid identification, nucleoside monophosphates, saccharide detection, peptide sensing, machine learning, single-molecule analysis, glycopeptide, post-translational modifications, epigenetic modifications