Identification of amino acids with sensitive nanoporous MoS2: towards machine learning-based prediction

Abstract Protein detection plays a key role in determining the single point mutations which can cause a variety of diseases. Nanopore sequencing provides a label-free, single base, fast and long reading platform, which makes it amenable for personalized medicine. A challenge facing nanopore technology is the noise in ionic current. Here, we show that a nanoporous single-layer molybdenum disulfide (MoS₂) can detect individual amino acids in a polypeptide chain (16 units) with a high accuracy and distinguishability. Using extensive molecular dynamics simulations (with a total aggregate simulation time of 66 µs) and machine learning techniques, we featurize and cluster the ionic current and residence time of the 20 amino acids and identify the fingerprints of the signals. Using logistic regression, nearest neighbor, and random forest classifiers, the sensor reading is predicted with an accuracy of 72.45, 94.55, and 99.6%, respectively. In addition, using advanced ML classification techniques, we are able to theoretically predict over 2.8 million hypothetical sensor readings’ amino acid types.