In most of the base nuclei in RNA/DNA, 13C and 15N, the CSA tensor is almost fully anisotropic (eta >0.9), so dzz ~= dyy and dxx ~= 0. Of course, this is the traceless anisotropic part (let's pray there's no anti-symmetric part!!! :) ) and |dzz| >= |dyy| >= |dxx|. I prefer the d11 >= d22 >= d33 version where you always know that d33 is the most shielded and d11 has the largest CS, but that's just me. dzz is more often than not the direction perpindicular to the base plane, although it varies with your choice of CSA tensor source (DFT, SS NMR, Solution NMR, etc.).
Alex
On 10/5/06, Edward d'Auvergne <edward.dauvergne@xxxxxxxxx> wrote:
That's a good idea, the eigenvector of the CSA tensor perpendicular to
the base rings and parallel to the long axis of the diffusion tensor
should contain a bit of that missing information about Da. Is the
amplitude of the perpendicular CSA component (say sigma_z)
significantly different from the other two eigenvalues (sigma_x and
sigma_y)? relax is currently incapable of using that information
though. Maybe that will soon change ;)
Edward
On 10/6/06, Alexandar Hansen <
viochemist@xxxxxxxxx> wrote:
> I had meant to say more on this but had to run to a meeting.
>
> In addition to ribose residues being helpful, the 13C CSA tensors of the
> base are highly asymetric and anisotropic. One of the components of the CSA
> tensor is perpindicular to the plane of the base (I think, perhaps, 13CO has
> a similar situation?) so that the CSA part of the relaxation will be
> sensitive to both orientations and should help to adequately span the 3D
> environment of anisotropic diffusion tensors. We have shown this to be true
> when measuring residual CSAs (RCSAs) as complementary to RDCs (Me, JMR
> (2006) 179, p323)
>
> Alright, off to another meeting!
>
>
> Alex
>
>
>
> On 10/4/06, Alexandar Hansen <viochemist@xxxxxxxxx
> wrote:
> > You have it right. Measuring ribose, or simply anything that's not also
> perpindicular to the base, should adequately sample more of the 3D space.
> We find this to be the case frequently when analyzing RDCs measured in RNA.
> Of particular interest would be the C1'-H1's. Having just a handful of
> those would like be highly beneficial.
> >
> > Alex
> >
> >
> >
> >
> > On 10/4/06, Edward d'Auvergne <
edward.dauvergne@xxxxxxxxx> wrote:
> > > Hi,
> > >
> > > In relaxation data analysis, you can only view the components of the
> > > Brownian rotational diffusion tensor that the XH bond vectors sample.
> > > So if your macromolecule diffuses as a prolate spheroid but the XH
> > > bond vectors are close to perpendicular to the unique axis of the
> > > tensor, the only component of the diffusion tensor that the relaxation
> > > data contains information about is the eigenvalue Dper (the
> > > perpendicular component of the tensor). The result is that the
> > > diffusion will appear to be spherical where Diso has the value of
> > > Dper! In relax the parameters tm (which is essentially Diso) and Da
> > > are optimised. For this case, Da (and hence Dratio) would be
> > > undefined - it can have any geometrically possible value while having
> > > zero effect on the results.
> > >
> > > Have you tried starting with the calculated Da value (or Dratio if you
> > > wish)? This is not possible using the 'full_analysis.py' script, but
> > > the other sample scripts can be modified to do this. As these
> > > parameters will be statistically undefined, the final optimised values
> > > should be pretty close to the input values. This assumes tm (or Diso)
> > > is set to be close to the Dper value as the curvature of the space may
> > > cause optimisation to shift Da. The parameter Dr would also be
> > > undefined and this would fully explain the Dr value of 1 reported in
> > > bug #7297 ( https://gna.org/bugs/?7297 ).
> > >
> > > The problem of the undefined Da and Dr, and hence the molecule
> > > appearing to diffuse as a sphere, could be resolved by having a few
> > > vectors which deviate from the perpendicular. However this is only
> > > important if you are actually interested in characterising the
> > > Brownian rotational diffusion. In any case, attempting to optimise
> > > these values using relaxation data of perpendicular XH's will only
> > > result in statistically insignificant values - it's not statistically
> > > possible to pull out these parameters. It is almost guaranteed that
> > > AIC model selection will select spherical diffusion. Would the ribose
> > > CH's together with the base XH's adequately sample three-dimensional
> > > space?
> > >
> > > I hope this info helps,
> > >
> > > Edward
> > >
> > >
> > >
> > > On 10/5/06, Alexandar Hansen <
viochemist@xxxxxxxxx> wrote:
> > > > Hello all,
> > > >
> > > > In studying RNA you run into a number of limiting factors of your data
> set.
> > > > a) NH data is available only on half of the residues (G's and U's), b)
> these
> > > > G's and U's must be in a helix, or the NH becomes exchanged with
> solvent,
> > > > and c) the NH vectors on the bases in a helix don't sample space
> randomly
> > > > and are oriented ~perpindicular to the diffusion axis (RNA is almost
> always
> > > > prolate shaped). This last scenario, for you protein folks, would be
> > > > similar to the situation where you had a single alpha helix and only
> NH
> > > > data, ie. sample only directions paralell to the helix axis.
> > > >
> > > > With this in mind, one can easily imagine that any relaxation analysis
> would
> > > > be happy to fit them to a lower diffusion model, such as spherical,
> than
> > > > what is in reality highly anisotropic. What I'd like to know how to
> do is
> > > > impose additional limits on the minimization step such that, for
> instance,
> > > > the Dratio could be fixed between some values. With the data I've
> been
> > > > analyzing, relax happily fits my NH data to the spherical case and,
> for the
> > > > prolate model, fits the Dratio to 1 -> 1.1. From hydrodynamic
> simulation,
> > > > we know, however, that the Dratio should be between 4-5. Are there
> any
> > > > thoughts on how to do this? On one level, it appears to be forcing
> the data
> > > > into a particular model. But if you can know something about the
> diffusion
> > > > parameters or anything else a priori from a different source than NMR,
> > > > shouldn't that be allowed to factor into the analysis?
> > > >
> > > > Thanks,
> > > > Alex Hansen
> > > >
> > > >
> > > > _______________________________________________
> > > > relax ( http://nmr-relax.com)
> > > >
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> > >
> >
> >
> >
>
>
Re: analysis of limited data sets