Dynamic Features Driven by Stochastic Collisions in a Nanopore for Precise Single-Molecule Identification.

Nanopore technology holds great potential for single-molecule identification. However, extracting meaningful features from ionic current signals and understanding the molecular mechanisms underlying the specific features remain unresolved. In this study, we uncovered a distinctive ionic current pattern in a K238Q aerolysin nanopore, characterized by transient spikes superimposed on two stable transition states. By employing a neural network model, we demonstrated that these previously overlooked dynamic spike features exhibit superior discriminative power, improving the accuracy from 44% to 93%. We identified that the stable transition states result from simultaneous interactions of ssDNA with the two sensitive sites of the nanopore. The proposed stochastic collision model offers a mechanistic framework for interpreting the generation of the dynamic spike features. This model indicates that the continuous transitions facilitate iterative, comprehensive snapshots of molecular interactions by nanopores. Our findings introduce a new approach for optimizing nanopore technology to capture complex dynamic features and substantially improve the accuracy of single-molecule identification.