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Hi Patrick<br>
<br>
Take a look at the Binet-Cauchy kernel, e.g. here:
<a class="moz-txt-link-freetext" href="http://bioinformatics.oxfordjournals.org/content/30/6/784.long">http://bioinformatics.oxfordjournals.org/content/30/6/784.long</a><br>
<br>
It is a shape-correlation metric that is rather insensitive to
global shape changes, but sensitive to local ones. Maybe that's what
you need. It's also much faster to compute than RMSD.<br>
<br>
I have attached a python code snippet that computes a BC distance
matrix for a set of proteins defined by CA coordinates.<br>
<br>
Kind regards,<br>
Dmytro.<br>
<br>
<br>
<div class="moz-cite-prefix">On 02/07/14 17:15, Patrick. C wrote:<br>
</div>
<blockquote cite="mid:3DC97382C23.000010FBpatrick.cossins@inbox.com"
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Hi Phenix users,
<div><br>
</div>
<div>I am not a crystallographer but I though you guys might be a
good place to ask this question.</div>
<div><br>
</div>
<div>I have 2 super secondary structures, A and B and they consist
of Helix-turn-Strand</div>
<div><br>
</div>
<div>Due to the turn the two structures have a poor RMSD because
the two flanking fragments of Helix and Strand are far from each
other but when I superimpose the two fragments
individually(helixA with helix B and standA with strandB in
Pymol they align very well).</div>
<div><br>
</div>
<div>Now, is there a way to express this instead of using the
RMSD?</div>
<div>When the two structures align well the RMSD is very good but
a slight movement and the RMSD is awful.</div>
<div>But looking at the two structures I can see they follow the
same path through space.</div>
<div><br>
</div>
<div>Thank you,</div>
<div>Patrick</div>
<div><br>
</div>
<div><br>
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