Mass-spectrometry Based Structural Proteomics in Characterizing Protein Higher Order Structure-Development and Applications in Neurodegeneration, Cancer, and Infectious Disease

Abstract

Proteins carry out most biological functions, and their activities are governed by their higher-order structure (HOS), which arises from folding, assembly, and dynamic conformational rearrangements. These structures are not static but exist as ensembles of interconverting states that respond to environmental changes and binding partners. Characterizing protein HOS and dynamics is therefore essential for understanding protein function but remains challenging due to structural heterogeneity and transient interactions. Mass spectrometry (MS)-based structural proteomics has emerged as a powerful approach to address these challenges by enabling solution-phase, high spatial resolution, and integrative characterization of protein structure, dynamics, and interactions. Chapter 1 introduces the principles and instrumentation of MS-based structural proteomics and highlights how complementary MS techniques-including hydrogen-deuterium exchange mass spectrometry (HDX-MS), covalent labeling footprinting, chemical crosslinking mass spectrometry (XL-MS), native mass spectrometry (nMS), and ion mobility mass spectrometry (IM-MS) can be integrated to probe protein HOS. These methods provide insights into solvent accessibility, side chain interactions, conformational dynamics, and oligomeric states, offering a framework for studying complex and heterogeneous protein systems. The second part of this dissertation focuses on the structural characterization of transient protein–protein interactions using integrative MS-based approaches. Chapter 2 investigates the interaction between glutathione S-transferase pi (GSTP1) and peroxiredoxin 6 (PRDX6), a system involved in redox regulation. Footprinting and XL-MS are combined with molecular docking to map the interaction interface and define solution-phase structural models. A reporter peptide-based normalization strategy is introduced to correct for differences in labeling efficiency and reactive site abundance, enabling reliable comparison of solvent accessibility between protein states. Chapters 3 and 4 examine the interaction between the host E3 ubiquitin ligase MIB2 and Ebola virus protein VP35 (eVP35). In Chapter 3, HDX-MS and XL-MS are used to identify regions of flexibility and stability in both proteins and to characterize binding interfaces and conformational changes upon complex formation. Chapter 4 builds on these findings by introducing a 14N/15N isotopologue mixing crosslinking strategy (MIX-XL) to distinguish intramolecular from intermolecular crosslinks in systems with mixed oligomeric states. Integration of MIX-XL restraints with molecular docking yields experimentally derived models that define MIB2 domain organization and its rearrangement upon eVP35 binding. The third part of this dissertation addresses protein aggregation, focusing on amyloid-β 1–42 (Aβ42), a key protein in Alzheimer’s disease. Chapter 5 reviews recent advances in structural characterization of Aβ aggregation, emphasizing the complementary roles of high-resolution structural methods and MS-based approaches. Chapter 6 applies MS-based glycine ethyl ester (GEE) footprinting combined with kinetic modeling to monitor peptide-level and residue-level conformational changes during Aβ42 aggregation. This approach identifies key residues and regions involved in aggregation and reveals multiple coexisting polymeric populations, providing insight into the dynamic and heterogeneous aggregation pathway. The final part of this dissertation focuses on improving the quantitative reliability of MS-based footprinting experiments. Chapter 7 develops a reporter peptide-based dosimetry strategy to address variability in reagent efficiency caused by sample composition and scavenging effects. By systematically evaluating candidate reporter peptides across different labeling chemistries, this work establishes practical criteria for reporter selection based on reactivity and dynamic range. Application of this strategy, combined with orthogonal distance regression (ODR)-based analysis, enables more accurate quantification of residue-specific labeling differences and improves comparison across protein states. Together, these seven chapters demonstrate the versatility and power of MS-based structural proteomics for studying protein HOS. By integrating multiple MS-based techniques and developing new quantitative strategies, this work advances the ability to characterize complex and dynamic protein systems, providing broadly applicable tools for structural biology and proteomics