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Title: Peptides in dynamic combinatorial chemistry : from reinforced molecular recognition to mechanosensitive self-replication
Author: Carnall, J. M. A.
Awarding Body: University of Cambridge
Current Institution: University of Cambridge
Date of Award: 2009
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Biological receptors such as antibodies and enzymes can bind their guest molecules with association constants that are, on average, several orders of magnitude greater than those observed in synthetic, water-based host-guest systems. This is believed to be made possible by secondary interactions within natural receptors, which reinforce recognition. This thesis initially aims to address this gap in binding efficiencies by incorporating intra-receptor interactions into synthetic hosts. To achieve this, a strategy utilising dynamic combinatorial chemistry (DCC) was followed, employing peptide chains to provide the intra-receptor interactions. While only limited progress towards the original goal was made, several macrocycles with unexpected and exciting properties were discovered. The investigation of the latter constitutes the bulk of the work undertaken. Chapter 1 provides a brief review of relevant literature, covering topics including molecular recognition and DCC. An introduction to the project, outlining the design of a system that may benefit from intra-receptor interactions, then follows in chapter 2. Chapter 3 details the progress made towards achieving the original goal of reinforced molecular recognition, from which several lessons regarding building block design are distilled. Thereafter, the focus of the thesis shifts away from the initial aim, and chapter 4 introduces some further concepts which underpin the remaining chapters, including self-replication, supramolecular polymers and amyloid. Chapters 5-7 present an investigation of several structurally-related peptides within a dynamic combinatorial setting. Some of the macrocycles formed as a result are shown to display intriguing properties such as mechanosensitivity, self-replication and self-organisation into extended aggregates. Explanations for this behaviour and models for the systems are proposed.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available