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Hello again -<br>
<br>
<div align="justify">IMHO the best approach for any simulation study is
to plan how the simulation will be used to solve a specific scientific
problem before running the simulations, rather then the other way
around. To clarify what I wrote below, there's no algorithm I'm aware
of where the input is an MD simulation of a certain protein and the
output is an aggregation propensity of certain residues.<br>
<br>
Having said that, simulations can be a useful tool to study amyloid
aggregation and even tendency of specific residues to aggregate first.
A useful approach was developed in the group where I work now, based on
the aggregation propensity of peptide sequences decomposed from the
whole protein:<br>
<a class="moz-txt-link-freetext" href="http://dx.doi.org/10.1016/j.jmb.2006.01.009">http://dx.doi.org/10.1016/j.jmb.2006.01.009</a><br>
<br>
Using your simulations, you can check if some residues are more prone
to lose their secondary structure as the temperature increases, which
suggests that they are more likely to unfold. Do bear in mind though
that aggregation is a complex process which involves multimers. Unless
you show some correlation with experimental findings it will be
difficult to defend your conclusions.<br>
<br>
</div>
Ran<br>
<br>
shahid nayeem wrote:
<blockquote
cite="midAANLkTinRB2qKUAY_lberHEi88cR-IGmqDimGyq2B3BvU@mail.gmail.com"
type="cite">
<div>Hi</div>
<div> I have used TANGO Aggrescan, Zyggregator and other online tools
but I am unable to find and pinpoint residue responsible for
aggregation. Then I did MD simulation of the proteins with gromacs at
different temperature. Now in this background I need suggestion to
analyse my MD trajectory.</div>
<div>shahid Nayeem<br>
<br>
</div>
<div><span class="gmail_quote">On 5/12/10, <b
class="gmail_sendername">Ran Friedman</b> <<a
href="mailto:r.friedman@bioc.uzh.ch">r.friedman@bioc.uzh.ch</a>>
wrote:</span>
<blockquote class="gmail_quote"
style="border-left: 1px solid rgb(204, 204, 204); margin: 0px 0px 0px 0.8ex; padding-left: 1ex;">Hi,<br>
<br>
There's no recipie to locate aggregation hot spots based on MD<br>
simulations. There are many papers on simulations of protein and peptide<br>
aggregation from which you can draw some ideas, but bear in mind that<br>
aggregation of more than very few and very small peptides is typically<br>
much slower than what one can simulate using atomistic MD.<br>
<br>
For a quick approach you can use sequence analysis tools, e.g., TANGO<br>
<a href="http://tango.crg.es/">http://tango.crg.es/</a><br>
<br>
Good luck,<br>
Ran<br>
<br>
--<br>
------------------------------------------------------<br>
Ran Friedman<br>
Postdoctoral Fellow<br>
Computational Structural Biology Group (A. Caflisch)<br>
Department of Biochemistry<br>
University of Zurich<br>
Winterthurerstrasse 190<br>
CH-8057 Zurich, Switzerland<br>
Tel. +41-44-6355559<br>
Email: <a href="mailto:r.friedman@bioc.uzh.ch">r.friedman@bioc.uzh.ch</a><br>
Skype: ran.friedman<br>
------------------------------------------------------<br>
<br>
shahid nayeem wrote:<br>
> Dear all<br>
> What are the analysis tools which should be used on MD trajectory
file<br>
> in order to find potential aggregation sites of a protein. Anyone
can<br>
> tell me about specific resource material on use of Gromacs to
predict<br>
> protein aggregation hot spots from MD trajectory anlysis.<br>
> Shahid Nayeem<br>
</blockquote>
</div>
</blockquote>
<br>
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