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Hi to all experienced Ultracentrifuge Scientists,<br><br>
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>
There are two puzzling aspects to this data.<br>
First, I am not getting good repeatability in the amount of
aggregate.<br>
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>
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>
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>
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>
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>
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>
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>
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>
Is this just non-ideality that I am not fitting properly or maybe that is
not modeled well?<br>
Is the culprit convection? Thoughts on convection in the next
message.<br><br>
Cheers<br><br>
<br>
<x-sigsep><p></x-sigsep>
Dr. David B Hayes<br>
Boston Biomedical Research Institute<br>
64 Grove St.<br>
Watertown, MA 02472<br>
617-658-7738<br><br>
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