[RASMB] Sedimentation Velocity Experiments that are hard to fit

John Philo jphilo at mailway.com
Fri Mar 24 13:32:24 PST 2006


David,
 
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? 
 
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.
 
John

-----Original Message-----
From: rasmb-bounces at rasmb.bbri.org [mailto:rasmb-bounces at rasmb.bbri.org] On
Behalf Of David Hayes
Sent: Friday, March 24, 2006 12:41 PM
To: RASMB
Subject: [RASMB] Sedimentation Velocity Experiments that are hard to fit


Hi to all experienced Ultracentrifuge Scientists,

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)

There are two puzzling aspects to this data.
First, I am not getting good repeatability in the amount of aggregate.
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.
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.
  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.
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.  
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.

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).

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.

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:


Is this just non-ideality that I am not fitting properly or maybe that is
not modeled well?
Is the culprit convection?  Thoughts on convection in the next message.

Cheers




Dr. David B Hayes
Boston Biomedical Research Institute
64 Grove St.
Watertown, MA  02472
617-658-7738



Boston Biomedical Research Institute... Today's Research for Tomorrow's
Health. 
Please visit us at www.bbri.org <http://www.bbri.org/>  

-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://list.rasmb.org/pipermail/rasmb-rasmb.org/attachments/20060324/784c3677/attachment.htm>


More information about the RASMB mailing list