Date of Award
2026-05-01
Degree Name
Master of Science
Department
Physics
Advisor(s)
Lin Li
Abstract
Kinesins are ATP-driven molecular motors essential for intracellular transport and mitotic spindle assembly, and their mechanochemical behavior has been extensively characterized through computational tools. The first project reviews decades of advances in molecular dynamic simulations, including all-atom and coarse-grained molecular dynamics, Markov state models, Brownian dynamics, and free-energy approaches, which have collectively deepened our understanding of conformational transitions, microtubule-binding interfaces, and ligand interactions, especially for the mitotic motor Eg5. The second review examines the rapid emergence of quantum computing as a transformative technology for drug discovery, highlighting key historical milestones, recent progress in hybrid quantum-classical algorithms, quantum-enhanced docking, and quantum machine learning for molecular modeling. Building on insights from these two reviews, the original research project presented in this thesis focuses on the rational design of peptide inhibitors targeting kinesin-5 (Eg5) and kinesin-12 (KIF15), two central regulators of spindle bipolarity and promising anticancer targets. Using structure-based virtual screening and molecular docking, druggable sites on motor domains and protein-protein interfaces were identified, leading to promising peptide scaffolds with improved selectivity and favorable drug-like properties compared to existing small-molecule inhibitors. The full molecular dynamics simulations, free-energy analyses, and the integration of quantum-computing-based optimization strategies constitute an important part of the future work and will be conducted to refine peptide-kinesin interactions and strengthen the predictive power of the proposed design pipeline. This combined perspective highlights both the therapeutic potential of peptide-mediated kinesin inhibition and the broader contribution of emerging computational and quantum technologies to next-generation drug discovery.
Language
en
Provenance
Received from ProQuest
Copyright Date
2026-05
File Size
86 p.
File Format
application/pdf
Rights Holder
Maraf Mbah Bake
Recommended Citation
Mbah Bake, Maraf, "Design And Optimization Of Peptide Inhibitors Targeting Kinesin: Molecular Modeling Studies And Virtual Screening For Therapeutic Candidate Development" (2026). Open Access Theses & Dissertations. 4728.
https://scholarworks.utep.edu/open_etd/4728