Computational modeling of DNA and protein sensing via nanopore systems

Liu, Jingqian

Permalink

https://hdl.handle.net/2142/130189 Copy

Description

Title

Computational modeling of DNA and protein sensing via nanopore systems

Author(s)

Liu, Jingqian

Issue Date

2025-07-15

Director of Research (if dissertation) or Advisor (if thesis)

Aksimentiev, Aleksei

Doctoral Committee Chair(s)

Aksimentiev, Aleksei

Committee Member(s)

• Pogorelov, Taras
• Shukla, Diwakar
• Tajkhorshid, Emad

Department of Study

School of Molecular & Cell Bio

Discipline

Biophysics & Quant Biology

Degree Granting Institution

University of Illinois Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

Nanopore Sensing, Molecular Dynamics, Deep Learning

Abstract

Nanopores have emerged as powerful tools for biomolecular detection, enabling diverse applications—from sensing large-scale protein conformational changes to high-throughput DNA and protein sequencing. The core detection principle relies on measuring ionic current through a nanopore under an applied voltage; as analytes translocate through the pore, they modulate the open-pore current, producing characteristic signatures that encode structural or sequence information. In this thesis, I leverage molecular dynamics (MD) simulations and machine learning (ML) methods to explore a range of nanopore applications, advancing the fundamental understanding of nanopore sensing. Chapter 2 investigates the feasibility of protein sequencing using nanopores by simulating peptide analytes with specific nanopores, including sequences with single-residue substitutions and post-translationally modified peptides. These simulations reveal key determinants of nanopore signal generation, such as steric exclusion, pore–analyte interactions, analyte stretching, and local ion accumulation. Chapter 3 elucidates the mechanisms by which engineered MspA nanopore mutants—designed by our collaborators—enhance DNA sensing. To improve the predictive power of MD simulations, I optimized protocols for simulating open-pore mutants and evaluated three widely used force fields (CHARMM36 and two AMBER variants). My findings indicate that AMBER force fields tend to overestimate base stacking interactions, leading to inaccurate current predictions, whereas CHARMM36 shows better agreement with experiments. Further refinements to protein–DNA interaction parameters within CHARMM were implemented and validated through targeted experimental studies. Chapter 4 introduces two convolutional neural network-based models—NanoID-Net and NanoIC-Net—as efficient, data-driven alternatives to traditional numerical solvers for predicting ion distributions and steady- state ionic currents in nanopore systems. These models achieve one to two orders of magnitude speedup while maintaining high accuracy, particularly in peptide detection applications. Chapter 5 explores an emerging application of nanopores in DNA data storage, where synthetic DNA encodes digital information and nanopores serve as high-throughput readers. Through MD simulations, I examine chemically modified DNA bases designed to expand the nucleotide alphabet, thereby enhancing storage density and readout speed. My results confirm that these modified bases form stable hydrogen bonds with natural bases, supporting their viability for long-term data integrity and random-access retrieval. In summary, this thesis integrates physics-based MD simulations and data-driven ML models to advance nanopore-based DNA and protein sensing. The synergy between these approaches offers a toolkit for interpreting nanopore signals and paves the way for improved biomolecular detection technologies.

Graduation Semester

2025-08

Type of Resource

Thesis

Handle URL

https://hdl.handle.net/2142/130189

Copyright and License Information

Copyright 2025 Jingqian Liu

Owning Collections