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Molecular Dynamics of Peptide Sequencing through MoS2 Solid-State Nanopores for Binary Encoding Applications.

Andreina Urquiola Hernández1, Christophe Guyeux2, Adrien Nicolaï1

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Biological peptides show promise for data storage. Molecular dynamics simulations and machine learning identified sequence patterns in peptides passing through nanopores, enabling efficient binary information encoding for future storage solutions.

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Area of Science:

  • Biotechnology
  • Nanotechnology
  • Data Storage

Background:

  • Biological peptides offer programmable and versatile platforms for data storage.
  • Advances in peptide synthesis and sequencing enable enhanced capacity, durability, and speed in peptide-based data storage.

Purpose of the Study:

  • To evaluate the efficiency of peptide sequences for encoding binary information using ionic current traces.
  • To analyze sequence factors influencing data encoding through solid-state nanopores.

Main Methods:

  • Coarse-grained peptide sequencing of 12 distinct sequences using molecular dynamics (MD) through single-layer MoS2 solid-state nanopores (SSNs).
  • Classification using LightGBM to analyze sequence factors like amino acid position and spacing between charged amino acids.
  • Monitoring ionic current traces during peptide passage through SSNs.

Main Results:

  • Identified two distinct sequence groups based on the relative positions of charged amino acids.
  • Observed a strong correlation between discrimination accuracy and the separation of charged amino acids.
  • Established a nonlinear relationship between amino acid positions (sequence motifs) and ionic current fluctuations.

Conclusions:

  • Peptide sequence design is crucial for efficient molecular data storage.
  • The proposed approach using MD and machine learning can help develop scalable and reliable molecular data storage solutions.
  • Future research should focus on encoding longer binary chains to enhance storage capacity.