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<DIV><FONT face=Arial color=#0000ff size=2><SPAN class=531133117-12072004>Sorry
Arthur but I have to strongly disagree with your statement that the peaks from
SEDFIT's least-squares g(s) method are Gaussians to a good approximation. You
are making the assumption that this approach is really equivalent to Walter's
original method, but it is not. Peter has never published any theory
showing this Gaussian assumption is true. Fundamentally there is no
reason I can see to believe that a least-squares fitter using a model that
assumes no diffusion should always produce a Gaussian peak when applied to data
broadened by diffusion.</SPAN></FONT></DIV>
<DIV><FONT face=Arial color=#0000ff size=2><SPAN
class=531133117-12072004></SPAN></FONT> </DIV>
<DIV><FONT face=Arial color=#0000ff size=2><SPAN class=531133117-12072004>While
your statement is probably true in certain cases (particularly for
high mass species and when you use a high degree of smoothing), it is quite
easy to show it is NOT true in general, as I have done, by simulating a
single species of say 20 kDa and then calculating the ls-g(s) distribution.
The result will be distinctly non-Gaussian. </SPAN></FONT></DIV>
<DIV><FONT face=Arial color=#0000ff size=2><SPAN
class=531133117-12072004></SPAN></FONT> </DIV>
<DIV><FONT face=Arial color=#0000ff size=2><SPAN class=531133117-12072004>John
Philo</SPAN></FONT></DIV>
<DIV><FONT face=Arial color=#0000ff size=2><SPAN
class=531133117-12072004>Alliance Protein Laboratories</SPAN></FONT></DIV>
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<DIV></DIV>
<DIV class=OutlookMessageHeader lang=en-us dir=ltr align=left><FONT
face=Tahoma size=2>-----Original Message-----<BR><B>From:</B>
rasmb-admin@server1.bbri.org [mailto:rasmb-admin@server1.bbri.org] <B>On
Behalf Of </B>Arthur Rowe<BR><B>Sent:</B> Monday, July 12, 2004 9:15
AM<BR><B>To:</B> Jacob Lebowitz; medakachou;
rasmb@server1.bbri.org<BR><B>Subject:</B> Re: [RASMB] difference of p = 0.95,
0.68 and 0.55, the confidencelevel in the sedfit c(s)
distribution<BR><BR></FONT></DIV>
<BLOCKQUOTE><FONT color=#800000>Hi all<BR><BR>In Peter's absence let me make
one point which he (very correctly) makes concerning estimation of s values
and relative concentrations. Which is that the c(s) distribution for each
individual species in a mixture is <U>not</U> a gaussian distribution. Hence
one should not do 'peak fitting' by the usual algorithms, in ORIGIN or
anything else. As Jack very correctly says, it is numerical
integration over appropriate ranges which you need.<BR><BR>What <U>is</U>
valid to a very good approximation is to fit multiple gaussians to the
(least squares in SEDFIT) g(s) profile. Basically as per Walter Stafford's
original approach. In our own experience we find this gives a more
objective description of such systems, albeit - since it is better at
resolving closely related species including dimers of lower M (i.e. more
rapidly diffusing) monomers - the outcome can be rather less flattering to
the perceived 'quality' of one's precious preparation!<BR><BR>All best
wishes to everyone<BR><BR>Arthur<BR></FONT><BR></BLOCKQUOTE>--
<BR>*************************<BR>Arthur Rowe<BR>Lab at Sutton
Bonington<BR>tel: +44 115 951 6156<BR>fax: +44 115 951
6157<BR>*************************<BR><BR>
<BLOCKQUOTE><BR><BR></BLOCKQUOTE><BR>
<BLOCKQUOTE>Since Peter is on a long vacation, I will attempt to answer your
question. You can integrate the peaks by pressing the ctrl and I keys
simultaneously which will give you a dialog box that states that you should
hold the right mouse button down and draw a rectangle to cover the s range
to be integrated. You will see once you do the integration you will obtain
both the % of the loading signal in the integration range and the weight
average s value. Also the results box states that this integration it is
best done without regularization, confidence level of zero. Regularization
gives the most parsimonious distribution for the confidence level that
you set. Total removal of regularization may give you too many peaks that
will merge at higher confidence levels. You can still integrate over
multiple peaks in the s range you have selected and compare the result with
integration of the distribution you obtain at higher confidence levels. In
my experience the integration results over the same s range are comparable
from no regularization to using settings of <FONT size=2>p = 0.68 to 0.95.
At the latter p selections you have the more realistic description of
the sedimenting species. Hope that the above is clear.<BR><BR></FONT>Jack
Lebowitz<BR><BR><BR>At 10:29 PM 7/12/2004 +0800, medakachou wrote:<BR>
<BLOCKQUOTE><FONT size=2>Dear all,<BR><BR>Recently, I'm analyzing the
sedimentation velocity spectra by continuous c(s) distribution (SEDFIT).
I've analyzed my data in three kind of confidence level: p = 0.95, 0.68
and 0.55 and the regularization method is maximum entropy. The s limit is
0.1 to 25S. I found every species is not well seperated (they just fuse
together) in p = 0.95. In p = 0.68, the situation is better and the peaks
are more significant. p= 0.55 can give me the highest resolution and every
peak is very clear cut. Now the question is: if I want to calculate the
area of peaks by Origin peak fitting module, which results should I use?
I've check Schuck's paper and he suggests using p = 0.68 to 0.95 is
enough. How about 0.55? I appreciate your response and
suggestion.<BR><BR>Sincerely,<BR><BR><BR>Chi-Yuan Chou<BR>PhD student, the
Institutes of Life sciences, National Defense Medical Center, Taipei,
Taiwan<BR></FONT></BLOCKQUOTE><FONT size=2>e-mail:
r6243023@yahoo.com.tw<BR></FONT><BR></BLOCKQUOTE><BR><BR>
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