Thesis Format
Integrated Article
Degree
Doctor of Philosophy
Program
Chemistry
Supervisor
Konermann, Lars
Abstract
Proteins are biological macromolecules that are essential for countless physiological processes. Many health conditions are rooted in protein misfunction, resulting in the need to develop analytical techniques that can thoroughly study protein structures and functions. Liquid chromatography (LC)-mass spectrometry (MS) plays a key role in this context. By leveraging the separation power of LC, techniques such as hydrogen-deuterium exchange (HDX) MS can be used to decipher protein dynamics. However, the interpretation of experimental data obtained from these techniques is often hindered by an incomplete molecular understanding of the underlying physical processes. This dissertation explores how in silico tools, specifically molecular dynamics (MD) simulations, can provide novel insights into how these analytical techniques inform on the behavior of proteins and peptides.
The work in Chapter 2 examines HDX patterns of the model protein cytochrome c in its two canonical heme oxidation states, Fe(II) and Fe(III). Previous HDX work already pointed to increased stability in the Fe(II) state, however, the molecular foundation for this stability enhancement remained elusive. We performed HDX experiments in conjunction with MD simulations of cytochrome c with different heme coordination environments, and we elucidated how these changes account for the observed HDX patterns. Additionally, our MD work uncovered large-scale protein motions that are not detectable by HDX, thereby providing evidence for the existence of “HDX silent” protein motions.
Chapter 3 showcases the first high-fidelity MD modeling of a reversed-phase LC (RPLC) stationary phase and mobile phase for studying peptide interactions that are responsible for RPLC retention. Two tryptic peptides with different physicochemical properties were modeled in water and in a water/acetonitrile (ACN) mixture. It was found that peptide/stationary phase interactions primarily involved side chains with strongly hydrophobic character. While the data were insufficient for making retention predictions, the MD setup developed in this Chapter established the foundation for conducting detailed computational analyses of peptide interactions with nonpolar stationary phases under various solvent conditions and in the presence of formate as ion pairing agent.
Chapter 4 expands on the foundation established in Chapter 3 by using additional MD tools for making peptide RPLC retention predictions. In the past, peptide retention predictions relied on algorithms that involved empirical rules or large sets of RPLC training data; however, such traditional approaches are unable to uncover the physicochemical principles that drive retention. Using four tryptic peptides and five water/ACN solvent conditions, umbrella sampling MD was used to generate retention predictions from first principles, by determining the free energy of peptide/stationary phase binding equilibria under various mobile phase conditions. This study marks the first time that MD simulations alone have been effective at predicting peptide retention, highlighting how this technique can be used as a standalone tool or to enhance existing prediction algorithms. Overall, this dissertation highlights that MD can complement experimental techniques by providing a molecular level understanding of the underlying physicochemical principles.
Summary for Lay Audience
Proteins are large, chain-like molecules consisting of thousands of atoms. After folding into specific structures, these macromolecules perform countless essential tasks in the body, for example in the form of enzymes that catalyze biochemical reactions. Various forms of protein misfunction are responsible for a range of diseases. Additionally, recent years have witnessed an increased interest in the use of custom-designed proteins as therapeutics, complementing traditional “small molecule” drugs. As such, studying proteins is of great importance. Mass spectrometry (MS) is one of the most widely used tools in this context, usually in conjunction with techniques like reversed-phase liquid chromatography (RPLC) and hydrogen-deuterium exchange (HDX). RPLC is a tool used to separate proteins and peptides based on their hydrophobic character, allowing for the analysis of individual components in complex mixtures. HDX reports on protein motions by probing the exchange of hydrogen with deuterium upon placing proteins in a D2O-based solvent. Because D atoms are heavier than H atoms, these exchange events can be detected by RPLC/MS, following digestion of the labeled proteins into peptides. Simply speaking, protein segments that exhibit more rapid exchange are more dynamic. Both RPLC and HDX are widely used, however, the proper interpretation of data obtained from these techniques remains challenging because their molecular basis is not completely understood. Molecular dynamics (MD) simulations have seen wide adoption as a computational tool for atomistic modeling of physical, chemical, and biochemical systems. MD simulations can track the positions of thousands of atoms over time to uncover how chemical species interact with one another. This dissertation explores how MD simulations can be used to elucidate the complex molecular interplay that underpins analytical techniques.
In Chapter 2, HDX-MS experiments of the model protein cytochrome c are complemented by MD simulations. We uncover the existence of a rare protein state responsible for the observed deuteration patterns. MD simulations also revealed the occurrence of large-scale protein motions that are undetectable in HDX experiments, serving as a precautionary illustration of “HDX-silent” motions, and implying that HDX-MS may provide an incomplete view of protein behavior.
Chapter 3 uses MD simulations to obtain the first-ever atomistic view of peptides under RPLC conditions. We characterize binding interactions between two different peptides and a C18 stationary phase, and we uncover how mobile phase and ion pairing agents (formate ions) modulate the peptide behavior. Building on the foundation provided in Chapter 3, Chapter 4 explores the behavior of a wide range of peptides and mobile phase compositions to mimic the conditions of typical gradient elution RPLC experiments. An MD technique known as umbrella sampling is used to obtain thermodynamic data associated with peptide/C18 binding, and it is used to show that these MD results correctly predict the order in which the peptides emerge from the RPLC column. Our work marks the first application of umbrella sampling in this context.
Each chapter in this dissertation applies unique MD approaches to elucidate complex molecular interactions that provide the foundation of commonly used analytical techniques, replacing traditional and vague “black box” concepts with atomistic insights.
Recommended Citation
Scrosati, Pablo M., "Molecular Dynamics Simulations as a Tool for Probing Molecular Interactions in Hydrogen-Deuterium Exchange and Liquid Chromatography Workflows" (2025). Electronic Thesis and Dissertation Repository. 10690.
https://ir.lib.uwo.ca/etd/10690
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