Hi again
Thanks for your answer !
I think that, for me, the CSA value would have a significant impact on
my analysis since my protein has a tumbling time of about 13 ns and I
have data from 500, 600 and 800 MHz...
I don't know if this is relevant, but I performed simple tests with the
test data and sample scripts provided with relax (path :
'relax/test_suite/data/model_free/S2_0.970_te_2048_Rex_0.149' in
version 1.2.7 and the sample script 'mf_multimodel.py')...
TEST 1
=====
r = 1.02
CSA = -160 ppm
m4
S2 = 0.97
te = 2048
Rex = 0.149
X2 = 7.3e-28
TEST 2
=====
r = 1.02
CSA = -172 ppm
m4
S2 = 0.97
te = 82
Rex = 4.34
X2 = 2.27
As you can see, for this single residue (with data at 500 and 600 MHz),
there is no effect for the value of S2, but the effect is important for
te and Rex... And still, the best model (the lower X2) is m4 for both
situations...
I think that this ambiguity in the value for CSA leads to important
variations in the interpretation of relaxation data.
Thanks for getting me to understand more this topic and also choose the
best value to use...
Séb
Alexandar Hansen wrote:
Hi Sebastien,
I'm quite new to relax as well, but I can give you at least a some
answer to the questions you pose.
In general, the CSA mechanism is a little underappreciated. At low
enough field strengths for 15N relaxation (400-500MHz), the 15N CSA
accounts for somewhere between 10-20% of your R1 and R2 rates. Varying
the CSA magnitude between 160 and 172 only changes this by 2-3%. So,
if relaxation rates are measured with, let's say, 5% error, there's no
statistical reason to vary the CSA. As we go to higher fields
(800MHz), the CSA can account for 50-60% of the R1 and R2 rates and
varying the CSA between 160 and 172 can affect those rates by up to
10%. So, now people are finding that this thing called CSA is
relatively improtant and should be better understood.
In many analysis techniques, such as relax, you have the option of
letting the CSA vary. For relax, I believe that's models m10-m19 and
tm10-tm19. One word of warning though, I wouldn't encourage fitting
the CSA unless you have data at multiple field strengths as you're
adding another variable to the analysis, so the standard 3 measurements
at a single field strength are likely not enough to do this. You also
run the risk of overinterpretting your data because, in my opinion,
varying the CSA freely in relaxation analysis is not unlike simply
throwing in a fudge factor. :-)
As for what is the best value to use, I can't really help you there.
We'll have to wait for some of the protein people to respond (I know
RNA better ;-) ). But if you're at low enough fields or tiny proteins
(<2-3 ns tau( m )) it shouldn't really matter what you use.
I hope all of this makes sense and I haven't said anything blatantly
incorrect. If I have, hopefully someone will follow up on both of our
posts. Thanks, and good luck!
Alex Hansen
Hi
I am new to relax and have a quite general question about the value used
for the CSA while studying proteins' 15N-1H vectors with model-free
approach.
In the litterature, we mainly find two values for the CSA (-160 and -172
ppm).
There is, if I understand well, a link between the bond length and the
CSA, but everyone seems to agree about using the same value of 1.02 A
which should give rise to a mean S2 of 0.85 for secondary structure when
combined to a CSA of -172 ppm.
In the relax sample scripts (as well as in the Model-free manual), a
value of -160 ppm is used for CSA.
What is the best value to use and, most importantly, why ?
Also, what about the CSA variability from one vector to another (JACS,
128 (24), 7855 -7870, 2006) ?
Thanks !
Sébastien
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______________________________________
_______________________________________________
| |
|| Sebastien Morin ||
||| Etudiant au doctorat en biochimie |||
|||| Laboratoire de resonance magnetique nucleaire ||||
||||| Dr Stephane Gagne |||||
|||| CREFSIP (Universite Laval) ||||
||| 1-418-656-2131 poste 4530 |||
|| sebastien.morin.1@xxxxxxxxx ||
|_______________________________________________|
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Re: CSA & bond length