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Title: Sequence and structural templates for protein motifs
Author: Lancaster, Owen
Awarding Body: University of Manchester
Current Institution: University of Manchester
Date of Award: 2006
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Current methodologies for recognizing similar protein motifs are predominantly based upon establishing a homology relationship between the sequences. These methods are widely exploited to annotate new genomes and assign putative functions to new genes. However they are usually based on sequence data alone. More recent approaches have incorporated structural data into methods to improve the predictions compared to just sequence based methods alone. So far these approaches have not been widely exploited in bioinformatics for identifying common, small motifs. A test system was examined containing such a degenerate but short, repeating motif, the tetratricopeptide repeat (TPR). Sequence analysis was done to assess the effectiveness of common search tools for finding TPR motifs. These methods included BLAST, PSI-BLAST and Hidden Markov Models and found the latter to be easily the most effective search strategy. Further sequence analysis of the TPR motif was carried out to demonstrate the extent to which TPRs with similar sequences are related in functional terms. In addition a full structural analysis was also performed. The results of the sequence and structural analysis of the TPR allowed structural information to be obtained and structurally conserved features in TPRs comprising conserved interacting residues pair positions were revealed. Comparative models were built and evaluated for all annotated TPR sequences with unknown structures to assess their compatibility with the TPR motif structure. From these and other models the interaction energy of structurally adjacent residues pairs has been calculated. These models were generated by mutating residues in key conserved positions to all possible amino acid combinations. The energy is then evaluated for each of these 20x20 pair combinations. This energy is then integrated into sequence based methods such as Hidden Markov Models with the aim of improving TPR prediction. An improvement in search sensitivity and specificity is demonstrated which should allow improved identification and annotation of this motif in sequence databases.
Supervisor: Hubbard, Simon; Avis, Johanna Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available