Seeing the Invisibles: Detection of Peptide Enantiomers, Diastereomers, and Isobaric Ring Formation in Lanthipeptides Using Nanopores

Identification and Detection of a Peptide Biomarker and Its Enantiomer by Nanopore

Until now, no fast, low-cost, and direct technique exists to identify and detect protein/peptide enantiomers, because their mass and charge are identical. They are essential since l- and d-protein enantiomers have different biological activities due to their unique conformations. Enantiomers have potential for diagnostic purposes for several diseases or normal bodily functions but have yet to be utilized. This work uses an aerolysin nanopore and electrical detection to identify vasopressin enantiomers, l-AVP and d-AVP, associated with different biological processes and pathologies. We show their identification according to their conformations, in either native or reducing conditions, using their specific electrical signature. To improve their identification, we used a principal component analysis approach to define the most relevant electrical parameters for their identification. Finally, we used the Monte Carlo prediction to assign each event type to a specific l- or d-AVP enantiomer.

Silica Nanowires-Filled Glass Microporous Sensor for the Ultrasensitive Detection of Deoxyribonucleic Acid

DNA carries genetic information and can serve as an important biomarker for the early diagnosis and assessment of the disease prognosis. Here, we propose a bottom-up assembly method for a silica nanowire-filled glass microporous (SiNWs@GMP) sensor and develop a universal sensing platform for the ultrasensitive and specific detection of DNA. The three-dimensional network structure formed by SiNWs provides them with highly abundant and accessible binding sites, allowing for the immobilization of a large amount of capture probe DNA, thereby enabling more target DNA to hybridize with the capture probe DNA to improve detection performance. Therefore, the SiNWs@GMP sensor achieves ultrasensitive detection of target DNA. In the detection range of 1 aM to 100 fM, there is a good linear relationship between the decrease rate of current signal and the concentration of target DNA, and the detection limit is as low as 1 aM. The developed SiNWs@GMP sensor can distinguish target DNA sequences that are 1-, 3-, and 5-mismatched, and specifically recognize target DNA from complex mixed solution. Furthermore, based on this excellent selectivity and specificity, we validate the universality of this sensing strategy by detecting DNA (H1N1 and H5N1) sequences associated with the avian influenza virus. By replacing the types of nucleic acid aptamers, it is expected to achieve a wide range and low detection limit sensitive detection of various biological molecules. The results indicate that the developed universal sensing platform has ultrahigh sensitivity, excellent selectivity, stability, and acceptable reproducibility, demonstrating its potential application in DNA bioanalysis.

Identification of Conformational Variants for Bradykinin Biomarker Peptides from a Biofluid Using a Nanopore and Machine Learning

There is a current need to develop methods for the sensitive detection of peptide biomarkers in complex mixtures of molecules, such as biofluids, to enable early disease detection. Moreover, to our knowledge, there is currently no detection method capable of identifying the different conformations of a peptide biomarker differing by a single amino acid. Single-molecule nanopore sensing promises to provide this level of resolution. In order to be able to identify these differences in a biofluid such as serum, it is necessary to carefully characterize electrical parameters to obtain specific signatures of each biomarker population observed. We are interested here in a family of peptide biomarkers, kinins such as bradykinin and des-Arg9 bradykinin, that are involved in many disabling pathologies (allergy, asthma, angioedema, sepsis, or cancer). We show the proof of concept for direct identification of these biomarkers in serum at the single-molecule level using a protein nanopore. Each peptide exhibits two unique electrical signatures attributed to specific conformations in bulk. The same signatures are found in serum, allowing their discrimination and identification in a complex mixture such as biofluid. To extend the utility of our experimental results, we developed a principal component analysis approach to define the most relevant electrical parameters for their identification. Finally, we used semisupervised classification to assign each event type to a specific biomarker at physiological serum concentration. In the future, single-molecule scale analysis of peptide biomarkers using a powerful nanopore coupled with machine learning will facilitate the identification and quantification of other clinically relevant biomarkers from biofluids.