Biology subjects

Ensslen, T.

Publications and source records attributed to Ensslen, T..

5 recordsLinked to original sources

bioRxiv2026

Sample preparation for direct nanopore based protein identification via tryptic peptide recognition

Rapid, cost-efficient and miniaturized protein analysis is essential for point-of-care clinical diagnostics. Nanopores offer a promising alternative to mass spectrometry, offering high-resolution, single-molecule sensing with a minimal footprint. However, peptide-level protein identification remains challenging due to difficulties in assigning nanopore signals to peptide sequences and the incompatibility of conventional proteolysis workflows with nanopore sensing. Here we integrate a single-pot, solid-phase-enhanced sample preparation (SP3) workflow with nanopore sensing. The SP3 protocol decouples proteolysis from interfering components via magnetic bead cleanup, yielding a digest compatible with nanopore sensing. As a proof of concept, tryptic digests of myelin basic protein measured with an engineered aerolysin variant (AeL-R220S) generated a reproducible five-peak peptide fingerprint. Cross-validation against a synthetic peptide library and LC-MS/MS enabled reference-based assignment of blockade signatures to four MBP-derived peptides. This approach establishes SP3 as a sample-preparation workflow for nanopore sensing, paving the way for label-free identification and characterization of protein targets.

biochemistry↗

bioRxiv2026

Nanopore event detection in a simple and adaptive way

Nanopore read-out, that is the current signals measured across nanometer-sized openings in dielectric membranes or through natural protein channels, enables the detection, identification and sequencing of individual molecules. The detection can take place by analyzing the events of single biomolecules interacting with the pore. The accuracy in the detection of these single events is key for identification of physicochemical properties of analyte molecules. To this end, we further develop a very simple, fast, almost parameter-free, and adaptable cluster-based event detection (CBED) algorithm that clusters the nanopore signals prior to detecting nanopore events. The algorithm is validated against two other event detection schemes with respect to simplicity and efficiency. For this, nanopore data from four different experiments stemming from different laboratories that vary in the nanopore type, size, and analyte are considered. The comparison is made on the basis of the number of events detected, their quality, and the most important features extracted from nanopore events. Our results underline the higher efficiency and less noise of the CBED detected events for biological nanopore data and the need for an on-the-fly adaptivity of the baseline current for a class of solid-state nanopore data.

bioinformatics↗

bioRxiv2026

Graph-based learning and read-out of nanopore translocation event signals

Nanopores enable single-molecule analysis by measuring current signals through nanoscale pores in either biological or solid-state membranes. Accurate detection of analyte fingerprints within the pore environment is essential for reading-out the analyte type. We develop a framework for robust and label-free detection of the molecular nanopore events using a graph representation of the measured signals. To this end, we build a graph-based two-stage workflow based on a convolutional and graph neural networks that first perform a fast screening of the nanopore events, followed by a deep validation of these. The learned model can thus efficiently and in an unsupervised manner select possible molecular signatures (the current blockades) in the full signal, denoise, validate, reconstruct these, and predict the morphology of unseen molecular events. We could show that the learned model can efficiently predict the correct event morphology for the same analyte within a 2.4-fold range of transmembrane voltage values not included in the training. The developed graph-based workflow is modular, generalizable, and provided that it is trained on a huge amount of different nanopore experiments has the potential to become a blueprint model for nanopore read-out. Such a read-out model would be able to identify subtle differences in molecules like proteins, as well as their conformational or folding states. The proposed framework is developed using experimental signals from DNA translocation through an aerolysin pore and demonstrates a unified approach linking unsupervised feature learning to raw-signal inference for single-molecule sensing.

bioengineering↗

bioRxiv2022

A chip-based array for high-resolution fluorescence characterization of free-standing horizontal lipid membranes under voltage clamp

Optical techniques, such as fluorescence microscopy, are of great value in characterizing the structural dynamics of membranes and membrane proteins. A particular challenge is to combine high-resolution optical measurements with high-resolution voltage clamp electrical recordings providing direct information on e.g. single ion channel gating and/or membrane capacitance. Here, we report on a novel chip-based array device which facilitates optical access with water or oil-immersion objectives of high numerical aperture to horizontal free-standing lipid membranes while conrolling membrane voltage and recroding currents using micropatterned Ag/AgCl-electrodes. We demonstrate both wide-field and confocal imaging, as well as time-resolved single photon counting on free-standing membranes spanning sub-picoliter cavities are demonstrated while electrical signals, including single channel activity, are simultaneously acquired. This optically addressable microelectrode cavity array will allow combined electrical-optical studies of membranes and membrane proteins to be performed as a routine experiment.

bioengineering↗

bioRxiv2021

Resolving isomeric posttranslational modifications using a nanopore

Posttranslational modifications (PTMs) of proteins are crucial for cellular function but pose analytical problems, especially in distinguishing chemically identical PTMs at different nearby locations within the same protein. Current methods, such as liquid chromatography-tandem mass spectrometry, are technically tantamount to de novo protein sequencing1. Nanopore techniques may provide a more efficient solution, but applying the concepts of nanopore DNA strand sequencing to proteins still faces fundamental problems2-4. Here, we demonstrate the use of an engineered biological nanopore to differentiate positional isomers resulting from acetylation or methylation of histone protein H4, an important PTM target5,6. In contrast to strand sequencing, we differentiate positional isomers by recording ionic current modulations resulting from the stochastic entrapment of entire peptides in the pores sensing zone, with all residues simultaneously contributing to the electrical signal. Molecular dynamics simulations show that, in this whole-molecule sensing mode, the non-uniform distribution of the electric potential within the nanopore makes the added resistance contributed by a PTM dependent on its precise location on the peptide. Optimization of the pores sensitivity in combination with parallel recording and automated and standardized protein fragmentation may thus provide a simple, label-free, high-throughput analytical platform for identification and quantification of PTMs.

biophysics↗