Advances in phage display technology revolutionised our understanding of molecular interactions. The aim of using phage display is to screen a peptide library for the identification of rare ligand-bound variants with enhanced specific interaction. One of the most common challenges in dealing with an exhaustive peptide library is the selection of target-unrelated peptides. Here we improved methodical approaches that reduce phage binding to the substrate surrounding the target protein. With this we were able to improve the quality of peptide interactions selected from phage libraries. The main purpose of this study is investigate the range of applications of phage-displayed peptide libraries in the context of identifying peptides that interact with virus proteins and antibodies. Using our refined methods, we were able to select inhibitory peptides to Hepatitis C Virus (HCV) that blockade its entry into the host, along with mapping antigenic determinants of several monoclonal antibodies such as anti non-primate hepacivirus (NPHV) antibodies and human HC33.4 anti-HCV antibody. Epitope mapping proved easier than discovering potent viral therapeutics. However, lead sequences were identified that neutralise HCV entry in in vitro models. The presence of thousands of HCV quasispecies leads to another difficulty of discovering pangenotypic inhibitors. Robust alignment and significant binding were demonstrated with peptides selected with both NPHV and HC33.4 human antibodies, while the selection failed with other target proteins. There might be no hot spot residues presented in the libraries used against these proteins. Future investigations should focus on developing the suggested HCV-selected peptides to enhance their affinity. All in all, phage display technology has been successfully developed to improve the performance of peptide libraries in a way that can avoid undesirable sequences during library sorting and enhancing the chance to find favourable ligands.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:706409 |
| Date | January 2017 |
| Creators | Hakami, Abdulrahim R. |
| Publisher | University of Nottingham |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://eprints.nottingham.ac.uk/40700/ |
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