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Solid-state nanopores have emerged as one of the most versatile tools for single-biomolecule detection and characterization. Nanopore sensing is based on the measurement of variations in ionic current as charged biomolecules immersed in an electrolyte translocate through nanometer-sized channels, in response to an external voltage applied across the membrane. The passage of a biomolecule through a pore yields information about its structure and chemical properties, as demonstrated experimentally with sub-microsecond temporal resolution. However, extracting the sequence of a biomolecule without the information about its position remains challenging due to the fact there is a large variability of sensing events recorded. In this paper, we performed microsecond time scale all-atom non-equilibrium Molecular Dynamics (MD) simulations of peptide translocation (motifs of alpha-synuclein, associated with Parkinson's disease) through single-layer MoS2 nanopores. First, we present an analysis based on the current threshold to extract and characterize meaningful sensing events from ionic current time series computed from MD. Second, a mechanism of translocation is established, for which side chains of each amino acid are oriented parallel to the electric field when they are translocating through the pore and perpendicular otherwise. Third, a new procedure based on the permutation entropy (PE) algorithm is detailed to identify protein sequence motifs related to ionic current drop speed. PE is a technique used to quantify the complexity of a given time series and it allows the detection of regular patterns. Here, PE patterns were associated with protein sequence motifs composed of 1, 2 or 3 amino acids. Finally, we demonstrate that this very promising procedure allows the detection of biological mutations and could be tested experimentally, despite the fact that reconstructing the sequence information remains unachievable at this time.


Adrien Nicolaï, Aniket Rath, Patrice Delarue, Patrick Senet. Nanopore sensing of single-biomolecules: a new procedure to identify protein sequence motifs from molecular dynamics. Nanoscale. 2020 Nov 19;12(44):22743-22753

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PMID: 33174564

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