  Proteins are arguably the most important class of biomarkers for health
diagnostic purposes. Label-free solid-state nanopore sensing is a versatile
technique for sensing and analysing biomolecules such as proteins at
single-molecule level. While molecular-level information on size, shape, and
charge of proteins can be assessed by nanopores, the identification of proteins
with comparable sizes remains a challenge. Here, we present methods that
combine solid-state nanopore sensing with machine learning to address this
challenge. We assess the translocations of four similarly sized proteins using
amplifiers with bandwidths (BWs) of 100 kHz (sampling rate=200 ksps) and 10 MHz
(sampling rate=40 Msps), the highest bandwidth reported for protein sensing,
using nanopores fabricated in <10 nm thick silicon nitride membranes. F-values
of up to 65.9% and 83.2% (without clustering of the protein signals) were
achieved with 100 kHz and 10 MHz BW instruments, respectively, for
identification of the four proteins. The accuracy of protein identification was
significantly improved by grouping the signals into several clusters depending
on the event features, resulting in F-value and specificity reaching as high as
88.7% and 96.4%, respectively, for combinations of four proteins. The combined
improvement in sensor signals through the use of high bandwidth instruments,
advanced clustering, machine learning, and other advanced data analysis methods
allows identification of proteins with high accuracy.
