Abstract

Single-molecule protein sequencing would provide unprecedented insights into cellular processes and the diseases that arise from protein misfunction. Recently, nanopores have emerged as a platform for fast label-free analyte detection guided by the ionic current blockades generated from single molecules translocating under an applied electric field. The major bottleneck to nanopore protein sequencing is the need to distinguish twenty proteinogenic amino acids accurately at every single residue. Here, we demonstrate a proof-of-principle proteome characterization system that relies on ionic current signatures produced by complete translocation of natively folded proteins through an array of nanopores. Using the steric exclusion model, we computed the ionic current spectra for all structurally known proteins, initially excluding all membrane proteins, proteins containing partially-disordered domains, and multimeric protein assemblies - about 4000 proteins in total. For each protein, we computationally simulated a fluid driven tumbling through a nanopore by calculating current for 5000 randomly chosen orientations that changes the effective cross-section and thus modulates current. Ionic current signatures of proteins with varying molecular weight clearly differed by the magnitude of blockades confirming the steric exclusion origin of the current. Interestingly, proteins with similar molecular weight also showed a varied range of mean blockade currents due to changing current signatures originating from the protein shape. Using a support vector machine learning algorithm trained on our data set, we were able to theoretically distinguish the weight and shape of the 4000 chosen proteins to near perfect accuracy, in the absence of instrumentation noise. We then theoretically introduced a thermal noise source into our simulated ionic current recordings to demonstrate the minimum requirements of an experimental system that would permit accurate protein identification from a single molecule translocation.

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