期刊
NPJ 2D MATERIALS AND APPLICATIONS
卷 2, 期 -, 页码 1-9出版社
NATURE PORTFOLIO
DOI: 10.1038/s41699-018-0060-8
关键词
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资金
- NSF [1420882, 1506619, 1708852, 1720701, 1720633, 1545907]
- Direct For Mathematical & Physical Scien
- Division Of Materials Research [1708852] Funding Source: National Science Foundation
- Directorate For Engineering
- Div Of Electrical, Commun & Cyber Sys [1506619] Funding Source: National Science Foundation
- Div Of Engineering Education and Centers
- Directorate For Engineering [1720701] Funding Source: National Science Foundation
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 mu s) 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.
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