Author ORCID Identifier
Semester
Spring
Date of Graduation
2025
Document Type
Dissertation (Campus Access)
Degree Type
PhD
College
Eberly College of Arts and Sciences
Department
Chemistry
Committee Chair
Stephen J. Valentine
Committee Co-Chair
Blake Mertz
Committee Member
Peng Li
Committee Member
Terry Gullion
Committee Member
Bradley Webb
Abstract
Biopolymers are versatile molecules that perform myriad cellular functions such as gene regulation and high-energy molecule synthesis. Alterations in biomolecular structure such as those arising from mutations can disrupt important cellular processes and lead to disease. Therefore, accurate biopolymer structure characterization plays an outsized role in improving human health.
X-ray crystallography, cryogenic electron microscopy (Cryo-EM), and nuclear magnetic resonance (NMR) spectroscopy are the most powerful techniques for obtaining high-resolution, 3D structures.Valuable structural insight is also obtained using optical rotation, small angle X-ray scattering (SAXS), circular dichroism (CD) spectroscopy, and Forster resonance energy transfer (FRET) spectroscopy. Over the past few years, remarkable advances in computational power and algorithm development have revolutionized the field of biopolymer structure prediction. For example, leveraging machine learning (ML) and artificial intelligence (AI), AlphaFold and Open fold have demonstrated a remarkable ability to predict biopolymer structure from sequence. Although powerful, these techniques are limited in their ability to provide information about challenging systems where the structure is either unknown or highly flexible under physiological conditions. In this work, studies are presented in which structural information is gleaned from challenging biopolymer systems using novel computational and experimental methods.
In Chapter 2, a new structure characterization methodology based on hydrogen-deuterium exchange-mass spectrometry (HDX-MS) in combination with molecular dynamics (MD) simulations is introduced. The approach is used to describe the structural motifs of the unstructured peptide, Nt17, a conserved sequence in Huntingtin protein and associated with Huntington’s Disease progression. The model is used to show that, although highly flexible, Nt17 is intrinsically primed for facile conversion to α-helical conformation upon binding with molecular partners.
Having shown that HDX reactivity is predictive of the degree of structural flexibility and bias (propensity to form 2° structural elements such as α-helix, β-sheet, and β-turn) for intrinsically disordered regions (IDRs), the work in Chapter 3 explores an extension of structure characterization to protein-ligand systems. In this study human FK506 binding protein (FKBP12) structure is examined in the absence and presence of two, well-recognized ligand molecules (i.e., FK506 and rapamycin). The work provides a better understanding of binding/unbinding events, competitive binding, and folded/unfolded conformer heterogeneity. Native MS and HDX-MS were utilized in conjunction with MD simulations to understand this select intrinsically disordered region (IDR) and IDR-drug conformational landscape.
Chapter 4 explores the functional property prediction of Proteorhodopsin, by integrating experimental studies with MD simulations validation. Here, a novel linear regression model accurately predicts the spectral tuning of UV-visible data of the PR mutant. Chapter 5 examines the effects of lipid and membrane environments on folded states of PR which have been validated in the literature based on biophysical analyses. The stability of the PR pentamer and hexamer in detergent micelles and the lipid bilayer is investigated. The potential for PR to serve as a standard model system for future studies, is also investigated.
Chapter 6 describes an extension of the novel experimental and modeling approaches for the characterization of membrane activated peptides (MAPs). Experiencing structural transitions through change in solution conditions (e.g., pH), these molecules present one of the most challenging systems to characterize. Preliminary studies suggest that the advanced experimental and computational techniques described in Chapter 2 to Chapter 5 may offer the best opportunity to detail such structural transitions.
Recommended Citation
Rahman, Mohammad Azizur, "Native mass spectrometry techniques and molecular dynamics simulations for characterizing the conformational landscape of peptides, protein-ligand complexes, and integral membrane proteins" (2025). Graduate Theses, Dissertations, and Problem Reports. 12723.
https://researchrepository.wvu.edu/etd/12723