Armin Ahmadi

Date of Award


Document Type


Degree Name

Doctor of Philosophy (PhD)


Mechanical Engineering

Committee Chair

Babak Shotorban

Committee Member

Vineetha Menon

Committee Member

Joseph D. Ng


Biomedical engineering, Drug development, T cells


In pursuit of an interdisciplinary exploration of molecular interactions from an engineering perspective with a focus on protein-protein and protein-small molecule interactions, multiple research projects were undertaken, leveraging both experimental and computational approaches. The focus of the wet-lab experimental project was on the identification and evaluation of a novel peptide ligand that binds specifically to the human CD3ε and study how this interaction influences T-Cell activation, proliferation, and calcium signaling. A novel peptide ligand, WSLGYTG, was discovered through phage display peptide technology. Peptide interaction to the CD3ε receptor was simulated using molecular docking calculations. This peptide ligand was synthesized and coated onto microbeads and was then exposed to Jurkat T-Cell line, which showed a weak but specific association. The efficacy of the peptide ligand in inducing T-Cell activation was evaluated by monitoring cell proliferation, calcium signaling, and other activation markers and it demonstrated a capacity to induce T-Cell activation. These findings show a huge potential in T-Cell immunotherapy. The dry-lab computational projects describe the design and development of a novel technology pipeline that aims at significantly enhances the efficiency of computational drug discovery, a traditionally inefficient and resource/time intensive process. The primary objective was to implement workflows that incorporates protein dynamics into computational drug discovery by leveraging the ensemble pharmacophore models to accelerate the virtual screening process. The ensemble pharmacophore models were generated based on the availability of protein crystal structures co-crystalized with a ligand in the binding site and using an ensemble of protein structures or molecular dynamics trajectories conformation. Applying this technology pipeline on multiple target protein, we demonstrated that our approach is significantly less computationally demanding than traditional docking methods. These models demonstrated the capability of distinguishing between agonist and antagonist compounds when specifically trained to do so. The workflow was then applied to a panel of target proteins suggested for early assessment of potential drug-related risks due to off-target binding by major pharmaceutical companies. This alignment with the strategic framework proposed by leading pharmaceutical companies ensures that our workflow is not only efficient but also is relevant and applicable to real-world drug discovery scenarios.

Available for download on Sunday, August 17, 2025