Fwd: RE: [RASMB] Sedimentation Velocity Experiments that are hard to fit

Fri Mar 24 14:15:52 PST 2006

David,
can you say at least as much about the buffer to exclude co-sedimenting 
solvent making dynamic density gradients?  Dependent on the nature and 
amount of the co-solute, that can change the profiles significantly enough 
to pick up with boundary modeling.
Peter

At 05:12 PM 3/24/2006, David Hayes wrote:
>Hi John,
>
>Thank you for your comments.
>
>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.
>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.
>
>Thanks
>
>David Hayes
>
>
>
>>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
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