Author ORCID Identifier
Semester
Spring
Date of Graduation
2025
Document Type
Dissertation
Degree Type
PhD
College
Eberly College of Arts and Sciences
Department
Chemistry
Committee Chair
Mark Tinsley
Committee Co-Chair
Blake Mertz
Committee Member
Stephen Valentine
Committee Member
Hacer Karatas Bristow
Committee Member
Srinjoy Das
Abstract
Studies in protein ligand binding are a significant focus in the field of medicinal chemistry
and biochemistry, facilitating a greater understanding of the structure-function relationship
of the protein and controlling function via drug development. In this research, we identify
new binding sites for lipids on the microbial membrane protein, proteorhodopsin, and apply
machine learning approaches to accelerate the generation of binding affinity-like data for the
use in the identification of potential small molecule drugs. The platelet activating factor
receptor. Cardiolipin, a lipid in membranes, and proteorhodopsin, a microbial proton pump,
are both commonly found in microbial membranes, but little is known about their putative
interactions. Using the Martini force field with coarse grain, molecular dynamics (MD) simulations
were carried out on μs time scales to model the lateral interactions of cardiolipin and
proteorhodopsin in a bilayer environment. We managed to identify two potential cardiolipin
binding sites with long-lived residence times. Both of these binding sites are located near regions
critical to proteorhodopsin function, suggesting that cardiolipin may play a role in the
ability for proteorhodopsin to pump protons across the outer membrane. The second area of
research was the application of machine learning approaches as a substitute for the screening
of small-molecule hits on the platelet activating factor receptor. Graph neural networks
with attentive mechanisms were trained in a 50,000-ligand library using structural features
and docking scores as input. Several ML techniques were applied to the generation of the
GNN models, identifying the combination of features that maximized accurate prediction
docking scores while also prioritizing efficiency. Although there were issues with overfitting
using a relatively small dataset, this GNN model in combination with active learning has the
potential to accelerate screening of ligand libraries. Further developments of this approach
are likely to improve the accuracy of screening, as well.
Recommended Citation
Wroe, Alexander Benjamin, "Investigation of Protein-Ligand binding interactions through the computational methods of Molecular Dynamics and Graph Neural Networks" (2025). Graduate Theses, Dissertations, and Problem Reports. 12796.
https://researchrepository.wvu.edu/etd/12796