Thomas Long’s Thesis Defense
October 31st, 2014
1:30 PM
MEC 201
Advisor: Dr. Timothy Andersen
Committee: Dr. Owen McDougal and Dr. Amit Jain
ABSTRACT:
Since the discovery of the molecular basis of disease, numerous studies have reported a correlation between the activity of specific protein receptors and the development/progression of certain diseases. As a result, the study of protein receptor interactions has become an intricate part of drug development. The relative inexpensiveness of computing hardware has made computational methods an important supplementary tool for receptor modeling, although software tools do not exist that are capable of efficiently screening large peptide mutant libraries without adding significantly to the researchers’ workload.
A Computational Approach to Efficient Peptide Influenced Drug Repurposing (CAEPIDR) has been developed to explore the conformational ligand binding space of the alpha3beta2 nicotinic acetylcholine receptor (nAChR) isoform and use the results to identify small molecule drugs that target the receptor. The nAChR’s conformational ligand binding space was heuristically explored using a genetic algorithm, which managed a structure-based virtual screen of a 640,000 alpha-conotoxin (a-CTx) MII mutant library. A utility was also created to search the PubChem Compound database for small molecule drugs with a 3-D shape similar to the highest affinity peptides from the virtual screen.
CAEPIDR’s genetic algorithm-based procedure was able to find high-affinity peptides while performing docking calculations for only 9344 of the 640,000 a-CTx MII mutants. The PubChem Compound search yielded a small molecule drug with an estimated binding affinity which was nearly double a-CTx MII’s. CAEPIDR has been integrated with the DockoMatic suite to create DockoMatic 2.1, which can be used to create virtual peptide analog libraries, virtually dock ligands to macromolecular receptors, and identify small molecule drugs for disease treatment.