[RASMB] Re: RASMB digest, Vol 1 #319 - 5 msgs
medakachou
ls890067 at ndmctsgh.edu.tw
Fri Jul 16 22:08:01 PDT 2004
Dear Prof. Philo and Prof. Correia,
After calculating the sw by the second moment integration (by SEDFIT), I found sw is 6.4 at 0.15 mg/ml, 6.9 at 0.50 mg/ml and 7.2 at 1.00 mg/ml. Another isoform showed s = 5.5-5.8 at the same concentration range. If I set s = 10.0 as the middle line, the c(s) curve area larger than s = 10.0 is 9% at 0.15 mg/ml and that is 20% at 1.00 mg/ml. Differently, another isoform didn't have this feature (14% at 0.15 mg/ml and 11% at 1.00 mg/ml). I'm trying to use ultrascan II for the g(s) model analysis ( although my free-trial has been out of date...). I attached a pdf file including the results of ls-g*(s) model. Seems like no obvious difference at the two concentration. However, the area larger than s = 10.0 is also higher (22%) at 1.00 mg/ml, compared with 14% at 0.15 mg/ml.
I think it is truly a aggregating system. The aggregating isoform didn't have any cysteine and the buffer didn't contain any reducing agent like DTT or Beta mercaptoethanol. My buffer is PBS containing 150 mM NaCl, 20 mM phosphate and pH is 7.3.
I've already give up trying to calculate the individual "peaks". The difference of sw and the percentage of "pretty large" aggregating species (s > 10.0) may be enough to make a conclusion about the tendency. This structural feature can partial explain why one isoform is pathgenic and the other is normal isoform.
Recently, I've learned a lot from RASMB and think this club is full of treasure ^^. Once again, thanks, everyone.
Chi-Yuan Chou
PhD student, the Institutes of Life sciences, National Defense Medical
Center, Taipei, Taiwan
e-mail: r6243023 at yahoo.com.tw
Lab homepage: http://www.enzkin.org/
----- Original Message -----
From: John Correia
To: ls890067 at ndmctsgh.edu.tw ; rasmb at rasmb-email.bbri.org
Sent: Thursday, July 15, 2004 11:11 PM
Subject: Re: [RASMB] Re: RASMB digest, Vol 1 #319 - 5 msgs
Chi-Yuan
It is always helpful to make composite plots of the c(s) or g(s) distributions so you can directly see the presence or I think in this case the absence of concentration dependence. Some people like to normalize the plots by dividing by the area under the total curve. Make both those composite plots and look at the data and tell us again what you see! The extra stuff at larger s is most likely just a sensitivity issue, you see it because you raised the concentration of all aggregates. This does display the amazing ability of AUC to fractionate broad distributions.
I ask again - reducing agent? cys in the sequence?
If this had really been a reversibly interacting system, why would you expect to see peaks? c(s) has a tendency to make peaks, even for reacting boundaries. It is the tendency to over interpret those peaks that one must guard against. Others have pointed out the amazing ability of c(s) to see small amounts of aggregates, something of special interest in the biotech area. In this case the peaks in fact suggest even the major species are aggregates.
After all the discussion about how to use sedfit and analysis of distributions, what did you expect to learn from integrating these peaks?
>>> "medakachou" <ls890067 at ndmctsgh.edu.tw> 07/14/04 10:16PM >>>
Dear RASMB,
I attached a pdf file (distribution.pdf) containing c(s) distribution plot
at high and low protein conecentration. The parameters are included, also
the rmsd, f/f0, residual bitmap. p = 0.68 and the resolution is 250 (smin to
smax is 0.1 to 25). Thanks everyone's comments and suggestion.
Chi-Yuan Chou
PhD student, the Institutes of Life sciences, National Defense Medical
Center, Taipei, Taiwan
e-mail: r6243023 at yahoo.com.tw
Lab homepage: http://www.enzkin.org/
----- Original Message -----
From: "John Correia" <jcorreia at biochem.umsmed.edu>
To: <ls890067 at ndmctsgh.edu.tw>; <rasmb at server1.bbri.org>
Sent: Thursday, July 15, 2004 6:45 AM
Subject: Re: [RASMB] Re: RASMB digest, Vol 1 #319 - 5 msgs
> 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
> >
> > This is a MIME message. If you are reading this text, you may want to
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> >
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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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