Researchers at the University of Geneva have engineered a novel nanopore-based platform capable of identifying proteins at the single-molecule level, a breakthrough published in the Journal of the American Chemical Society in 2026. The method, spearheaded by Assistant Professor Chan Cao and first author Verena Rukes, addresses longstanding challenges in protein analysis by merging advanced biophysics with artificial intelligence to enable rapid, label-free detection. Traditional protein analysis remains computationally and experimentally complex, despite its critical role in disease research, drug development, and biomarker discovery. The Geneva team overcame these hurdles by utilizing a nanometer-scale aperture embedded in a synthetic membrane. As individual molecules traverse the pore, they momentarily alter the transmembrane electrical current, producing distinct signal patterns that function as molecular fingerprints. A primary technical obstacle was driving proteins through the aperture reliably, given their inconsistent surface charges which render standard electrophoretic methods ineffective. The researchers resolved this by harnessing electro-osmotic flow, a controlled liquid movement within the pore that transports proteins regardless of their electrical properties. To decode the resulting high-noise electrical signals, the team integrated machine learning algorithms. The system extracts multiple temporal and amplitude features from each current disruption and trains on known reference samples to map specific signal signatures to distinct protein structures. This hybrid approach significantly enhances discrimination capabilities, allowing the platform to differentiate between highly homologous proteins that previously produced indistinguishable signals. The technology positions itself as a versatile tool across multiple high-impact sectors. In clinical diagnostics, its capacity to detect analytes at minute concentrations could accelerate disease screening and enable precision medicine protocols. Beyond healthcare, the underlying principle demonstrates utility in molecular data storage, where digital information encoded into synthetic polypeptides can be read sequentially through the nanopore. Looking ahead, the research group is focused on establishing a direct quantitative correlation between measured current fluctuations and primary protein sequences. This advancement would transition the platform from comparative recognition to de novo structural analysis, permitting the characterization of previously unmeasured proteins. By eliminating the need for fluorescent or chemical labeling and automating signal interpretation, the UNIGE methodology marks a substantial progression in single-molecule biophysics, promising to streamline proteomic workflows and expand the boundaries of molecular detection.