Cluster Modified Nanopore for Protein Post-Translational Modification Detection

Abstract

The precise and sensitive detection of protein post-translational modifications (PTMs), particularly phosphorylation, is critical for advancing our understanding of cellular signaling and disease pathology. In this thesis, we present a nanopore-based biosensing platform enhanced by cluster modifications, offering novel capabilities for the single-molecule analysis of phosphorylated peptides. The introductory chapter outlines the principles of nanopore sensing and its relevance as a next generation biosensing technology. The second chapter explores the use of nanoparticle-assisted nanopores for detecting ovarian cancer peptide biomarkers, demonstrating the method’s capability to discriminate between cysteine-containing peptide variants from clinically important proteins such as LRG-1. Building on this, the third chapter presents a detailed study on the discrimination of isomeric phosphorylated peptides derived from the human insulin receptor. A cluster-modified nanopore platform enabled accurate identification of phosphorylation states at the single-molecule level. To enhance the classification of nanopore signals, a Gaussian Mixture Model (GMM)-based machine learning pipeline was developed and optimized specifically for the complex signal profiles produced by the cluster-modified nanopore. The fourth chapter is dedicated to the design and optimization of the GMM algorithm, tailored to capture the multi-modal characteristics of the nanopore signal distributions. The final chapter examines the interaction of titanium dioxide (TiO₂) nanoparticles with phosphonate ligands in the nanopore environment, offering insight into the chemical challenges and opportunities in designing phosphonate-specific sensing platforms. Altogether, this work establishes an integrated strategy for high precision phosphoproteomic sensing using modified nanopores and machine learning, demonstrating the potential of this technology for both research and clinical diagnostics