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      <td align="right" valign="top" width="20%">&nbsp; </td>
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<span lang="en-gb">Improving Sequence Alignment for Protein Modelling</span></font></b></td>
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<p ALIGN="center"><b><span lang="en-gb"><font size="4">Danielle Talbot</font></span></b></p>
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Supervisor:</b> <span lang="en-gb">Dr Andrew C R Martin</span></p>
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<p>School:</b> School of Animal and Microbial Sciences<span lang="en-gb">,
</span>University of <span lang="en-gb">R</span>eading</p>
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<p ALIGN="LEFT"><span lang="en-gb">Generating the correct alignment between template and target is the most 
important stage of comparative modelling. When the alignment is incorrect it 
will introduce major errors into the final modelled structure.<br>
<br>
Simple sequence alignments do not always give the correct structural aligment, 
especially when the sequence identity between the template and the target is 
low. For example, indels within regions of secondary structure are relatively 
rare - a pure sequence alignment will not reflect this and may place an indel 
within a section of secondary structure rather than shifting it to an adjacent 
loop.<br>
<br>
One way to try to improve comparative modelling is to create many models each 
with a different alignment. The resulting models are then analysed and scored to 
select the model likely to be most accurate. We thus need to address two 
problems: (1) how to generate different possible alignments (2) how to score 
those alignments.<br>
<br>
To address the first problem we have started by examining in detail the extreme 
case of alignment error known as MLSAs (Misleading Local Sequence Alignments). 
In these relatively rare cases, the target and template sequence have an 
apparently obvious local alignment with high sequence identity, but the 
structural alignment between the two proves very different. We have identified a 
number of factors which appear to contribute to these mis-alignments.<br>
<br>
For the second problem, we have investigated the application of empirical 
potentials to distinguish between models created from different alignments. The 
RAM potential set was used to attempt to distinguish between models created from 
a sequence alignment and the (correct) structural alignment using approximately 
50,000 template/target pairs. In the majority of cases the model derived from 
the structural alignment scored better than that derived from the sequence 
alignment. However, when applied to distinguishing between models with more subtle variations in alignment, or simply different loop 
conformations, the RAM potential did not perform very well.</span></p>
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