Publication Date

April 2016


Erika Taylor




English (United States)


With the constantly improving processing power of computers and advancements in molecular modeling software, it is increasingly feasible to understand ligand-protein interactions through computer simulations, with considerable advantages in terms of cost, time, labor, and resources used. Molecular docking is one such computational method which simulates the position, conformation, and affinity of a small molecule ligand binding to a target protein. In the experiments herein described, molecular docking was used to study the structure-activity relationships of inhibitors of two enzymes, Heptosyltransferase I (HepI) from Escherichia coli and E1A binding protein p300 from Homo sapiens, toward the development of new potent and selective inhibitors for use in further research and as possible leads in the development of new therapeutics. HepI catalyzes the attachment of a key sugar in the biosynthesis of lipopolysaccharide (LPS), a large lipid-bound carbohydrate found in the outer membrane of Gram-negative bacteria. LPS allows these bacteria to form biofilms, which contributes to virulence by shielding them from antibiotics and the immune system. An inhibitor of HepI (and thus of LPS synthesis) therefore has the potential to be a useful new tool to fight infection and better understand this pathway. p300 is a large multi-domain DNA regulatory protein including a histone acetyltransferase (HAT) domain that catalyzes the acetylation of lysine residues on the N-terminal domains of histones. This acetylation neutralizes their positive charge, causing negatively charged DNA to associate less tightly with the histone and therefore be more accessible to polymerases and other DNA-associated proteins. p300 is thus a mediator of epigenetic changes to DNA. Mutations in p300 are associated with cancer, and thus the development of a p300 HAT inhibitor represents a promising target in cancer therapy. AutoDock Vina was used to perform molecular docking experiments, and a package of scripts was developed to automate the processes of input file preparation, data extraction from results, and post-docking analysis. This package was used to dock and analyze substrates and known inhibitors of both proteins, design chemical variants of higher affinity, and perform high-throughput screning of chemical libraries to identify new structural leads. These experiments compliment parallel in vitro inhibitor assays and provide insight into the binding mechanisms of known inhibitors. In addition, putative allosteric sites were identified on both p300 and HepI. Future studies will pursue the leads raised by these studies and further refine the scripts package to enable it to provide automated docking and data extraction and analysis of any protein with any set of ligands.

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