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Title: Algorithms for protein comparative modelling and some evolutionary implications
Author: Contreras-Moreira, Bruno
ISNI:       0000 0001 3561 5273
Awarding Body: University of London
Current Institution: University College London (University of London)
Date of Award: 2004
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Protein comparative modelling (CM) is a predictive technique to build an atomic model for a polypeptide chain, based on the experimentally determined structures of related proteins (templates). It is widely used in Structural Biology, with applications ranging from mutation analysis, protein and drug design to function prediction and analysis, particularly when there are no experimental structures of the protein of interest. Therefore, CM is an important tool to process the amount of data generated by genomic projects. Several problems affect the performance of CM and therefore solutions for them are needed to increase its applicability. In this work different algorithms and approaches were tested with this aim, particularly to help in template selection and alignment, and some useful insights were obtained. First, this work describes the development of DomainFishing, a tool to split protein sequences into functionally and structurally defined domains and to align each of them to the available templates. The performance of our approach is benchmarked and some problems and possible developments are identified. When comparing different alignment procedures none of them is found to be consistently superior, suggesting that a combination of several could be an advantage. Driven by these ideas and the fact that selecting templates can be a difficult problem, a new modelling approach is designed and tested. This algorithm uses crossover, mutation and selection within populations of protein models generated from different templates and alignments to obtain recombinant structures optimised in terms of fitness. Despite our simple definition of fitness, the procedure is shown to be robust to some alignment errors while simplifying the task of selecting templates, making it a good candidate for automatic building of reliable protein models. In-house benchmarks of the method show its strengths and limitations. The method was also benchmarked during the fifth Critical Assessment of techniques for protein Structure Prediction (CASP5), in which its perfomance was encouraging both for comparative modelling and fold recognition targets, among the top 20 predictors. Finally, we present some data to support a possible evolutionary feedback mechanism between protein structure and gene structure, using human and murine genomic data, structural data from the Protein Data Bank and the protein recombination methodology. This may have some implications for understanding protein evolution and protein design, which are discussed.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available