[RASMB] Re: RASMB digest, Vol 1 #319 - 5 msgs

[John Correia]

Isolating the main peak, reconcentrating the sample, and generating the
same distribution suggests reversibility, the classic test, although its
not clear what a 10 peak pattern means?  Discrete peaks suggest
irreversible aggregates.  Is there reducing agent in the buffer?  Cys in
the protein?  

What optical system and how good are the fits, rms values?   c(s) can
get more peaky with very low noise levels & apparently low p values - I
personally only do .95.  I always check c(s) distributions with g(s),
although for a broad distribution g(s) may be hard to apply. 
Alternatively what do the Ls-g(s) distributions look like?  Ultimately I
plot c(s) and g(s) as a function of concentration to develop hypotheses
about the data.  The shape of the conc dependence of the boundaries is
often informative, although I would never fit a c(s) or Ls-g(s)
distribution shape to extract molecular information.  Maybe a g(s) since
if properly done its the derivative of the boundary.  

Then I go to direct boundary fitting, individually & globally - in my
case Sedanal but sedfit/sedphat can work depending upon the model. 
Years ago (the 70's) there was a lively, but non email (didn't exist),
discussion about fitting raw data vs smoothed data or extracted moments
- many methods give similar answers, but if possible always fit the raw
data directly.

So if your system is a broad but reversible distribution what are the
options?  Indefinite?  You are describing big shifts?  You need to
establish endpoints, the s value of the monomer and the endpoint of the
association, if there is one?  Integrate the entire distribution and
plot weight average S vs concentration.  Remember the behavior of a
simple titration experiment, you need at least two orders of magnitude
to go from 10% to 90% saturation.  Plot the data vs log conc.  Does it
look like a binding curve and does it saturate?  To go higher in c try
the 0.3 mm path centerpieces, you can gain a factor of 5 in
concentration.  Try 230 nm or interference to go lower, or can you
estiamte s1 from the other isoform's data?

Without seeing the data or the actual distributions it is hard to know!



-------------------------------------------------------------------
 Dr. John J. "Jack" Correia
 Department of Biochemistry
 University of Mississippi Medical Center
 2500 North State Street
 Jackson, MS  39216
 (601) 984-1522                                 
 fax (601) 984-1501                             
 email address: jcorreia at biochem.umsmed.edu     
 homepage location: http://biochemistry.umc.edu/correia.html
 dept homepage location:    http://biochemistry.umc.edu/
-------------------------------------------------------------------
 
 

>>> "medakachou" <ls890067 at ndmctsgh.edu.tw> 07/13/04 11:38 PM >>>
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> Date: Tue, 13 Jul 2004 10:59:00 -0500
> From: "John Correia" <jcorreia at biochem.umsmed.edu><~!B*+R^&>> To:
<jphilo at mailway.com>, <r6243023 at ms48.hinet.net>,<~!B*+R^&>>   
<arthur.rowe at nottingham.ac.uk>, <rasmb at server1.bbri.org><~!B*+R^&>>
Subject: Re: [RASMB] difference of p = 0.95, 0.68 and 0.55, the
> confidencelevel in the sedfit c(s) distributi
>
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>
> One problem I have with these discussions is they are method of
analysis
> focused and until Arthur's illustion to "know your system" not focused
> on the molecules, the mechanism, the interactions.  Were we just
talking
> about one run and one concentration or a series of conectrations?  Why
> do you want to integrate?  Are the "peak" positions constant or do
they
> change with concentration?  Is this in fact an impure system of
> nointeracting species or aggregates, or are these aggregates of a
single
> component?  Is there any reversible interaction going on?
>
> The goal is to describe your system in molecular and mechanistic terms
> and then fit the data individually & globally to that model to prove
the
> hypothesized mechanism.  Statistics, assumptions, simulations are all
> important.  Now what is going on in your system?

Dear John,

    Yes, I should give more information about my case. I expressed and
purified a 34 kDa protein and it's N-terminal or C-terminal truncated
fragments by E. coli expression system. The protein purity is > 99% by
SDS-PAGE.  This protein has two isoforms. I study them in three
different
concentration: 0.15, 0.50 and 1.00 mg/ml in PBS (pH7.3). By using
sedimentation velocity and c(s) distribution analysis, I found one
isoform's
N-terminal truncated fragments showed a 10 "peaks" pattern whose s is
from 3
to 23 at 1.00 mg/ml. While at 0.15 mg/ml, only 5 peaks were found at s =
3
to 12. It means it should be a single component aggregation and is
concentration-dependent. The other isoform didnot show this
characteristics.
I want to give my paper some quantitative data about this difference, so
I
chose "Origin peak fitting module" and analyzed the pattern of gaussian
peaks. The reviewer thought it is overinterpretation (about the
fused-"peaks") and suggested me lowering the cinfidence level to p =
0.7.
I've tried and found the resolution is better (every "peaks" is still
existed). These two days I've tried Jack Lebowitz's comment and gained
some
quantitative data. I'm going to compare them and hope it can make my
paper
more quantitative sound.
    By the way, while I isolated the major "peak" species by using
gel-filtration chromatography (S-300 column) and concentrate them (I
need
higher concentration), It just change back to the same "multi-peaks"
situation (by sedimentation velocity). I think it's not a
non-interacting
but a associating system, right? Thanks your help.

Chi-Yuan Chou
PhD student, the Institutes of Life sciences, National Defense Medical
Center, Taipei, Taiwan
e-mail: r6243023 at yahoo.com.tw



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