Matplotlib DPL94 R1rho R2eff

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About

The production to these figures relates to the Suppport Request:
sr #3124: Grace graphs production for R1rho analysis with R2_eff as function of Omega_eff

References

Refer to the manual for parameter explanation

  1. Evenäs, J., Malmendal, A. & Akke, M. (2001). Dynamics of the transition between open and closed conformations in a calmodulin C-terminal domain mutant. Structure 9, 185–195 DOI
  2. Kempf, J.G. & Loria, J.P. (2004). Measurement of intermediate exchange phenomena. Methods Mol. Biol. 278, 185–231 DOI
  3. Palmer, A.G. & Massi, F. (2006). Characterization of the dynamics of biomacromolecules using rotating-frame spin relaxation NMR spectroscopy. Chem. Rev. 106, 1700–1719 DOI
  4. Palmer, A.G., Kroenke, C.D. & Loria, J.P. (2001). Nuclear magnetic resonance methods for quantifying microsecond-to-millisecond motions in biological macromolecules. Meth. Enzymol. 339 DOI
  5. Francesca Massi, Michael J. Grey, Arthur G. Palmer III* (2005) Microsecond timescale backbone conformational dynamics in ubiquitin studied with NMR R1ρ relaxation experiments, Protein science DOI

Figures

Ref [1], Figure 1.b. The bell-curves As function of angle calculation.

Ref [1], Figure 1.c. The wanted graph. No clear "name" for the calculated parameter.

Ref [2], Equation 27. Here the calculated value is noted as: $R_{eff}$. : Equation 27: $ R_{eff} = R_{1\rho} / sin^2(\theta) - R_1 / tan^2(\theta) = R^{0}_2 + R_{ex} $.
Where $R^{0}_2$ refers to $R_{1\rho '}$ as seen at DPL94

Ref [3], Equation 20. Here the calculated value is noted as: $R_2$: $R_2 = R_{1\rho} / sin^2(\theta) - R_1 / tan^2(\theta)$
Figure 11+16, would be the reference.

Ref [4], Equation 43. $R_{eff} = R_{1\rho} / sin^2(\theta) - R_1 / tan^2(\theta)$

Ref [5], Material and Methods, page 740. Here the calculated value is noted as: $R_2: R_2 = R^{0}_2 + R_{ex}$.
Figure 4 would be the wished graphs.


A little table of conversion then gives 

Relax equation    |   Relax store    | Articles
---------------------------------------------------------------
R1rho'                spin.r2             R^{0}_2 or Bar{R}_2
Fitted pars           Not stored          R_ex
R1rho                 spin.r2eff          R1rho
R_1                   spin.ri_data['R1']  R_1 or Bar{R}_1

The parameter is called R_2 or R_eff in the articles. Since reff is not used in relax, this could be used?

A description could be:

  • The effective rate
  • The effective transverse relaxation rate constant
  • The effective relaxation rate constant.

Make graphs

The outcome

Figure 1
Figure 2
Figure 3

To run

relax -p r1rhor2eff.py

Code

Code: r1rhor2eff.py Python script

Bugs ?

Do you get an error with matplotlib about dateutil? Then see Matplotlib_dateutil_bug

See also

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