Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.320275
Title: A constraint based assignment system for protein 2D nuclear magnetic resonance
Author: Leishman, Scott
ISNI:       0000 0001 3607 9476
Awarding Body: University of Aberdeen
Current Institution: University of Aberdeen
Date of Award: 1995
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Abstract:
The interpretation of Nuclear Magnetic Resonance (NMR) spectra to produce a 3D protein structure is a difficult and time consuming task. The 3D structure is important because it largely determines the properties of the protein. Therefore, knowledge of the 3D structure can aid in the understanding of its biological function and perhaps lead to modifications which have an enhanced therapeutic activity. An NMR experiment produces a large 2D data spectrum. The important part of the spectrum consists of thousands of small cross peaks and the interpretation task is to associate a pair of hydrogen nuclei with each peak. Manual interpretation takes many months and there is considerable interest in providing (semi-) automatic tools to speed up this process. The interpretation is difficult because the number of combinations can quickly swamp the human mind and the spectrum suffers from peaks overlapping and random noise effects. ASSASSIN (A Semi-automatic Assignment System Specialising In Nmr) is a distributed problem solving system that has been implemented in the identification of peaks associated with the hydrogen nuclei at the end of long side chains. These results are then passed onto the structural assignment stage. The structural assignment stage is a feedback loop which involves the interpretation of a spectrum and the generation of preliminary structural models. These models can then be used to simplify further analysis of the spectrum. ASSASSIN uses a constraint manager implemented in CHIP to analyse this data more quickly and thoroughly than a human. The results of this work reveal that a constraint based approach is well suited to the NMR domain where the problems can be easily represented and solved efficiently.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.320275  DOI: Not available
Keywords: ASSASSIN; Distributed problem solving Biochemistry Chemistry, Organic Computer software
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