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Title: Protein structure prediction and refinement
Author: Offman, Marc Nathan
ISNI:       0000 0004 2675 8653
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2008
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Over the last few years it has been shown that protein modelling techniques, especially template based modelling, are now accurate enough for qualitative analysis and decision-making in support of a wide range of experimental work. Automatic protein modelling pipelines are becoming ever more accurate; however, this has come hand in hand with an increasingly complicated interplay between all components involved. Despite all progress, still important problems remain and so far computational methods cannot routinely meet the accuracy of experimentally determined protein structures. In protein modelling pipelines, several important steps dictate a model's quality. Selecting a good template and aligning the query sequence correctly, backbone completion, model refinement and final model selection are considered the main steps. As a first step to approach protein refinement, a genetic algorithm (GA) for protein model recombination and optimization is presented in this work. This algorithm has the potential, to drive models away from the template towards the native structure. Furthermore, a complete and novel modelling pipeline, incorporating this GA is presented. In this context, a new scoring scheme, backbone repair algorithm and several other findings are reported and presented: We introduce the novel concept of Alternating Evolutionary Pressure, i.e. intermediate rounds within the GA simulation, where unrestrained linear growth of the model population is allowed. This approach improves the structural sampling and thereby facilitates energy-based model selection. Finally, the GA in combination with molecular dynamics simulations is used in the context of protein engineering. Several mutants were identified to stabilise and increase the activity of the cancer drug L-Asparaginase, a complex enzyme. The successful prediction of these mutations stresses the importance of protein molecular modelling for cell biology and in a clinical context.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available