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Hi John,<br><br>
Thank you for your comments. <br><br>
I think it is OK to say that the data is absorbance and that it looks
like a picture from a Beckman training manual. I was a little
surprised they were interested enough in the truth to let me describe the
problem I was having on RASMB in this general way to get community input,
but I don't want to push things by divulging any details: I
actually do not know myself what the buffer is. I mention
non-ideality because the darn scans just don't fit as a whole but each
one looks fine by itself. <br>
I might be more interested in getting a good fit than the people who own
the data: but it really bugs me that something that looks so simple
doesn't fit the models I tried in Sedfit and Sedanal.<br><br>
Thanks<br><br>
David Hayes<br><br>
<br><br>
<blockquote type=cite class=cite cite="">
<font face="arial" size=2 color="#0000FF">David,<br>
</font> <br>
<font face="arial" size=2 color="#0000FF">It is hard to comment without
knowing more about the experiment or seeing the data. Can you at least
tell us whether you are talking about absorbance or interference data?
<br>
</font> <br>
<font face="arial" size=2 color="#0000FF">You mention that you have
considered non-ideality, which suggests that this could be an experiment
at high protein concentration. Is that correct? If so, then part of the
problem might be optical distortions due to refraction by the
boundary---these are always worst early in the run, and go away as
diffusion makes the gradient less steep. That problem can be
significantly reduced by using 3 mm centerpieces.<br>
</font> <br>
<font face="arial" size=2 color="#0000FF">John<br>
</font>
<dl>
<dd><font face="tahoma" size=2>-----Original Message-----<br>
<dd>From:</b> rasmb-bounces@rasmb.bbri.org
[<a href="mailto:rasmb-bounces@rasmb.bbri.org" eudora="autourl">
mailto:rasmb-bounces@rasmb.bbri.org</a>] On Behalf Of </b>David
Hayes<br>
<dd>Sent:</b> Friday, March 24, 2006 12:41 PM<br>
<dd>To:</b> RASMB<br>
<dd>Subject:</b> [RASMB] Sedimentation Velocity Experiments that are hard
to fit<br><br>
</font>
<dd>Hi to all experienced Ultracentrifuge Scientists,<br><br>
<dd>I have some data (repeated experiments) from sample A buffer 1 (which
may or may not have been produced on the planet earth -- that is all I
can say about it)<br><br>
<dd>There are two puzzling aspects to this data.<br>
<dd>First, I am not getting good repeatability in the amount of
aggregate.<br>
<dd>Second, by eye, the raw data looks like a single species (which it
should be) with a small amount of higher weight aggregate (not
surprising), but the entire data set never fits well no matter what I
try.<br>
<dd>Thinking that the repeatability problem might be an artifact of my
inexperience using Sedfit I tried many different strategies:
including floating or not floating just about every parameter and also
using the nifty "experimental initial distribution" option of
Sedfit.<br>
<dd> Fitting to the whole data set, I got a fit with a single large
peak and two very small aggregate bumps which all together integrate to
about 4% of the total. The rmsd was a bit large at 0.01 and the
residuals were systematic. Early scans were fit very poorly and
later scans fit somewhat poorly with scans in the middle fit pretty
well. Later using Sedfit with the single species model and only the
later half of the scans and floating a lot of things and turning off the
hat function in Sedfit brought me down to a comparable fit with a rmsd of
.006, but the residuals were still systematic and the molecular weight
was off more than what I expected for having 4% aggregate.<br>
<dd>Then I did something that Peter Shuck thinks is a bit strange, but I
think it helped me understand the data a bit more. I fit pairs of
scans with c(s) taking scans at different times paired with the last
scan. Essentially then, I was fitting each scan individually:
using the last scan in ever pair was needed to help Sedfit know the
baseline. Peter reminded me that by fitting each scan, I lose the
ability to distinguish heterogeneity from diffusion. But in this
case, all the evidence points to a single species, so I did not think
heterogeneity (beyond the measured 4% aggregation) was significant.
The peak S value stays constant all the way down the cell. Each
individual scan fit this way fit very well without systematic residuals
and a rmsd of about .005. What changed was that early scans fit to
an f/f0 of 1.06 while by the later scans f/f0 fit to 1.5. This
means that the early scans are broader than they should be for a
reasonable f/f0 and that the later scans seem to be narrower and
diffusing less. <br>
<dd>I repeated this with Sedanal dcdt based curve fitting (here I took
scans by twos going down the cell and fit to a single species ignoring
the 4% aggregate) and found a similar trend: the fitted molecular
weight went from 95,000 to 225,000 and then settled down to about
160,000. If there were heterogeneity, the scans would fit the
opposite way to a one species model, the f/f0 would go down, or the
weight would go up later in the run as the species sedimented apart.<br>
<br>
<dd>I had some interference data of Tropomyosin that I subjected to a
similar type of analysis with Sedanal, fitting the whole data set and
then fitting scans by sets of 10 for the different times in the
experiment (because of TI, RI noise fitting, it is not possible to fit
small sets of scans with Sedfit using interference data; however, a
Sedfit fit to the whole Tm data set showed no systematic variation of the
residuals dependent on scan number). There was no scan number
dependent trend in molecular weight with this molecule, though the
variability in fitted molecular weight was surprising (weights from
different sets of 10 varied from 60,000 to 80,000 randomly).<br><br>
<dd>Seeing this I tried a Sedfit single species and Sedanal single
species fits with non-ideality parameters, but floating these parameters
made no substantial difference.<br><br>
<dd>I don't know if anyone has run into this type of problem, but I am
out of ideas what to try next. I am being a bit stubborn wanting a
good fit to the data: the S value of the main peak is reproducible
and the data contains quite a bit of information, but I can't figure out
exactly what is going on: <br><br>
<dd>Is this just non-ideality that I am not fitting properly or maybe
that is not modeled well?<br>
<dd>Is the culprit convection? Thoughts on convection in the next
message.<br><br>
<dd>Cheers<br><br>
<br>
<dd>Dr. David B Hayes<br>
<dd>Boston Biomedical Research Institute<br>
<dd>64 Grove St.<br>
<dd>Watertown, MA 02472<br>
<dd>617-658-7738<br><br>
<div align="center">
<dd>Boston Biomedical Research Institute... Today's Research for
Tomorrow's Health.<x-tab> </x-tab><br>
<dd>Please visit us at
<a href="http://www.bbri.org/" eudora="autourl">www.bbri.org</a></i></b>
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