Understanding molecular diversity is fundamental to biomedical research and diagnostics, but existing analytical tools struggle to distinguish subtle variations in the structure or composition among biomolecules, such as proteins. Researchers at the University of Tokyo have developed a new analytical approach, which helps overcome this problem. The new method, called voltage-matrix nanopore profiling, combines multivoltage solid-state nanopore recordings with machine learning for accurate classification of proteins in complex mixtures, based on the proteins’ intrinsic electrical signatures.
The study, published in Chemical Science, demonstrates how this new framework can identify and classify “molecular individuality” without the need for labels or modifications. The research holds promise of providing a foundation that could lead to more advanced and wider applications of molecular analysis in various areas, including disease diagnosis.