Abstract
AbstractProteoforms are proteins derived from highly related genes or post translational modifications (PTMs) of the same protein. They share extremely similar primary structures but have varying functions. Unfortunately, protein de novo sequencing including specific PTM/mutation detection is still challenging. Herein, a nanopore‐based technique is reported to resolve the amino acid order of amyloid‐β (Aβ1‐42) with site specificity. Subnanopores are sputtered in 5 nm‐thick inorganic membranes with a sensing depth of 0.66 nm inferred by finite element analysis. Denatured molecules at 0.45 ng mL−1 translocate through subnanopores while the current traces are sampled at 500 kHz with rms noise <15 pA. Hundreds of blockades are clustered using machine learning, and multiple blockades are averaged to establish current consensus. Consensus traces strongly correlate with a linear model of amino acid volume of Aβ1‐42 at single residue resolution, with Pearson Correlation Coefficients (PCCs) of 0.81 ± 0.03 and 0.92 ± 0.03 before and after dynamic time warping (DTW). A scrambled version of Aβ1‐42 is tested for validation purposes. Deep learning classification reveals that different polypeptides generate distinct translocation fluctuating patterns, but variations become imperceptible for the same species measured across nanopores (Area Under the Curve, AUC 0.93 ± 0.05 vs 0.64 ± 0.12). Lastly, important PTMs and mutations are site‐specifically located along the primary structure, implying new potential clinical applications.
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