| Summary: | Understanding how and why a protein folds to a well defined structure would be a great scientific advance. The work in this thesis aims to sample relevant pathways through configuration space to understand how a polypeptide chain can order itself in a short time. The work presented characterises a molecule using its potential energy surface. We construct databases of local minima and the transition states that connect them, and show that these methods are useful in the investigation of larger and more varied peptides than previously studied. New methods for coarse-graining the databases and for plotting free energy disconnectivity graphs are presented. The recently developed discrete path sampling method is applied to biomolecular systems for the first time, and used to iteratively refine stationary point databases by linking groups of minima. The harmonic superposition approximation is used to calculate equilibrium properties, and master equation and kinetic Monte Carlo techniques are employed to calculate kinetic properties. We also implement an internal coordinate optimisation algorithm, and demonstrate it to be more efficient than using Cartesian coordinates by a factor that increases with system size. We apply a selection of these techniques to a range of systems of increasing size. We study the isomerisations of a small dipeptide NATMA with two different force fields, to model experimental ‘quantum yields’ after IR excitation in a jet expansion. We investigate the effects of varying the cooling rate and the excitation energy. Work on the alanine tetra-peptide examines the overall energy landscape and the relative population of a-helical and <i>b </i>structures. The <i>BLN </i>model protein is shown to have numerous low free energy funnels, and the folding transition into the lowest energy state is characterised. The free energy landscape of met-enkephalin is also characterised, and the folding behaviour is examined.
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