Graduation Semester and Year
Summer 2026
Language
English
Document Type
Dissertation
Degree Name
Doctor of Philosophy in Chemistry
Department
Chemistry and Biochemistry
First Advisor
Dr. Saiful M. Chowdhury
Abstract
This thesis combines analytical chemistry, mass spectrometry, and computational methods to study three molecular systems: the sweet-tasting protein brazzein, arginine reactivity in macrophages, and atropine enantiomers in food and protein-binding experiments. Although the projects address different questions, each examines how molecular structure influences chemical reactivity, recognition, or biological interaction.
Chapter 2 focuses on brazzein. Mass spectrometry was used to characterize the protein and evaluate its molecular form and structural integrity. Computational modeling and HADDOCK docking were then applied to examine possible interactions between brazzein and the human T1R2/T1R3 sweet taste receptor. The study considers how differences in brazzein structure and surface properties may influence receptor recognition and binding orientation.
Chapter 3 investigates arginine reactivity in RAW 264.7 macrophages under untreated, phenylglyoxal-treated, lipopolysaccharide-treated, and combined LPS plus phenylglyoxal conditions. LC-MS/MS proteomics was used to identify proteins and phenylglyoxal-modified arginine-containing peptides. ProtBERT-derived sequence representations and machine-learning models were used to examine sequence patterns associated with experimentally observed arginine reactivity. Selected sites were also mapped onto protein structures to provide functional context.
Chapter 4 presents a chiral LC-MS/MS method for separating and quantifying atropine enantiomers. The method was applied to food matrices, including baby snacks, wheat, and sorghum powder, to evaluate atropine contamination. The separated enantiomers were also studied for stereoselective binding to α1-acid glycoprotein and human serum albumin.
Chapter 5 summarizes the main findings, limitations, and future directions. Across the three projects, mass spectrometry provides experimental information on molecular identity, modification, abundance, and separation. Computational and structural approaches support the interpretation of these measurements and help relate molecular structure to interaction and function.
Keywords
Mass Spectrometry, Biomolecular Analysis, Molecular Docking, Proteomic, Analytical Chemistry, Bioinformatics
Disciplines
Biochemistry | Bioinformatics
License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Recommended Citation
Khaki Firooz, Sepideh, "Integration of Mass Spectrometry and Machine Learning for Investigating Protein Reactivity and Structural Interactions" (2026). Chemistry & Biochemistry Dissertations. 16.
https://mavmatrix.uta.edu/chemistry_dissertations2/16