Simulation of single-protein nanopore sensing shows feasibility for whole-proteome identification
Fig 6
CNN-based classification results of: a) whole proteome, b) plasma proteome, and c) a cytokine panel. The fractions of the correctly-identified translocation events from whole-proteome classifications repeated five times are shown in a) and b) left panels. Each classification consisted of five separate training-and-testing of a CNN using 100 translocation events per protein (a total of ~107 events), whose resulting correct identifications were averaged. These experiments and analyses were performed under four different spatial resolutions (20, 30, 50 and 100 nm) and labelling efficiencies (60, 70, 80 and 90%). Right-hand panels show the fraction of the proteome correctly identified with probability p when considering a spatial resolution of 30 nm for different labeling efficiencies. The bin size was set to 1%. The insets display the degree of randomness in misclassification. The bin height is given by the fraction of mis-identified proteins R (i.e. proteins that had at least 10% of their events misclassified) at different ri (fraction of identical mismatch) intervals: ri = maxjnij/Ni for each protein i, where nij is the number of translocation events misidentified to protein j and Ni the total number of misclassified translocation events. The bin width–ri interval size–was set to 10%. Other experimental conditions are provided in supporting information file. c) Cytokines panel identification using the same proteins as in the ELISA set “CytokineMAP A”. The heat-map represents the correct ID of each cytokine under the specified labelling efficiency and resolution. The average correct ID is provided in the right-hand column.