As understanding of the ways in which the complex cellular environment affects the in vivo folding of proteins improves, improved methods for their study are required. It is possible to produce limited quantities of ribosome-nascent chain complexes (RNCs) and techniques for gathering data about them are improving, but no single technique provides all the information required to understand folding of nascent proteins on the ribosome and there are still significant data that cannot be obtained experimentally. In particular, while NMR chemical shift and residual dipolar couplings may be recorded, the samples are of too low concentration and stability to conduct the most informative NOESY experiments that are traditionally used for revealing atomic-resolution structure. Recently, the ability to use chemical shifts to reveal structural details and dynamic properties of small proteins has been developed. By simulating multiple molecules and predicting the average chemical shift of the ensemble, the simulation may be restrained to conform to the experimentally measured data, making testable predictions about the atomic-resolution dynamic properties of the molecule. By adapting these methods to the macromolecular RNC structures it is theorized that the limited chemical shift data available may be used to provide structural details of the protein as it emerges from a ribosome. This, however, is faced by many challenges, including the ability to simulate such large number of atoms in a suitable timescale and applying the restraints to the nascent chain alone. The thesis presented describes the development of computational techniques to characterize RNCs, including the concepts and challenges faced, the chemical-shift restrained simulation of nascent chains alone, the development of techniques to perform chemical-shift restrained molecular dynamics simulations of the RNCs and the application of these techniques to a model system.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:674669 |
| Date | January 2015 |
| Creators | Goodsell, L. S. |
| Publisher | University College London (University of London) |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://discovery.ucl.ac.uk/1470654/ |
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