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Title: The characterisation and prediction of protein-protein interfaces
Author: Kabir, T.
Awarding Body: University of London
Current Institution: University College London (University of London)
Date of Award: 2004
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Understanding how proteins interact with each other is of fundamental importance and is one of the most important goals of molecular biology. In order to study the characteristics of protein-protein interaction sites datasets of non-homologous protein-complexes have been compiled. These datasets include 142 obligate homocomplexes, 20 obligate hetero-complexes, 20 enzyme-inhibitor complexes, 15 antibody-antigen complexes, and 10 signaling complexes. Overall, the protein-protein interfaces of obligate complexes were found to be closely packed, relatively hydrophobic when compared to the entire protein exterior, planar, and enriched in residues such as tyrosine, phenylalanine, and isoleucine. In comparison to the protein-protein interfaces found within obligate protein-complexes the protein-protein interfaces of non-obligate protein-complexes were found to be on average much smaller in size and contain larger numbers of polar and charged residues. The bulk properties of the obligate and non-obligate protein-complexes are also discussed. A neural network was used together with the Patch Analysis method of Jones and Thornton (1997) to predict the location of the protein-protein interfaces in selected datasets of obligate homo and hetero-complexes. The Patch Analysis method is based upon defining a series of contiguous patches over the surface of a protein. The physical and chemical characteristics of each patch are encoded in the form of six parameters. One of these parameters is hydrophobicity. Another parameter that is used is accessible surface area (ASA). By comparing average values of these six parameters for the residues in a given surface patch with those covering known protein-protein interfaces the likelihood of a patch corresponding to a protein-protein interface can be assessed. Based upon the results for a dataset of 76 homo-dimers the use of a neural network enhances the accuracy of the original Patch Analysis method by some thirteen percent.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available