Year: 2015
Type: dissertation
Abstract: Computational modelling of proteins and their interactions with small molecule ligands is a growing field of research. Such studies provide an understanding of how protein structure relates to mechanism and function as well as informing drug discovery and design. \nThis thesis had two main aspects: computational modelling of ciprofloxacin derivatives binding to DNA gyrase and homology modelling of the protein IcaB based on sequence alignment with a related protein, PgaB. \nThe inhibitory activity of synthetic ciprofloxacin derivatives (with various linkage to citrate groups) was experimentally assessed by gel electrophoresis to examine the effect on DNA gyrase binding to a target DNA strand. Overall, the derivative which possessed the greatest inhibition compared to the unmodified ciprofloxacin was the c-gly-ciprofloxacin derivative, which had a 2 atom linker between the ciprofloxacin and citrate groups. This correlated with the change in interactions seen between ciprofloxacin derivatives as computationally modelled by molecular mechanics methods. \nThe second aspect of the thesis was to generate a model of the protein IcaB to test the hypothesis that it is a deactylase of poly-N-acetyl-glucosamine (PNAG) during maturation of the poly-glycan in the extracellular matrix responsible for biofilm generation for bacteria. An initial review of deacetylase enzyme structures identified the conserved features required for activity. A homologous protein, Pga,B was then used as a template to generate a homology model of IcaB. The model maintained the orientation and positioning of the metal-binding and catalytic residues critical for proper deacetylase function. However, the PNAG binding groove, believed to be involved in the transport of the PNAG to the active site of PgaB, was not properly replicated in the IcaB model. Further modelling would require improved characterization of the binding groove of IcaB.
Author: Ahmad Abd‐El‐Aziz
Language: English
Subfield: Drug Discovery
Domain: Life Sciences
Open Access status: green
Funder: Wellcome Trust
OpenAlex ID: W2234680049