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Added all of my references.
* a) Find an initial diffusion tensor estimate (you can do this in relax by only using model m0). This requires all non-mobile residues and side chain spins to be excluded, and this can be problematic. See the [d'Auvergne and Gooley, 2008b] paper for an example of the catastrophic failure that this initial estimate can result in. Or the bacteriorhodopsin fragment of [Orekhov et al., 1999] where this complete failure was earlier demonstrated.
* b) Optimise all of the model-free models from m0 to m9. This requires high precision optimisation, for a comparison of all the softwares see the [d'Auvergne and Gooley, 2008a ] model-free optimisation paper at http://dx.doi.org/10.1007/s10858-007-9214-2. Only relax and Dasha implement the full range of model-free models, though the models m6, m7, and m8 cannot be used if only single field strength data is used (m6 is the original 2-time scale motion model of Clore et al., 1990).
* c) Eliminate failed models (this is only available in relax). See the [d'Auvergne and Gooley, 2006 ] model elimination paper at http://dx.doi.org/10.1007/s10858-006-9007-z).
* d) Select the best model-free model for each spin system. This again requires precision modern techniques, with the best being AIC model select (see the [d'Auvergne and Gooley, 2003 ] model-free model selection paper at http://dx.doi.org/10.1023/A:1021902006114). If you are unaware that ANOVA statistics for model selection (hypothesis testing via chi-squared, F- and t-tests) was abandoned by the field of model selection over 100 years ago (a field which makes the NMR field look very, very small), then you should really look at that paper.
* e) Optimise the global model. This is the diffusion tensor plus the model-free models for all spin systems.
* f) Check for convergence (identical chi-squared values to a previous iteration, and not necessarily the last one). If no, then go back to b) and repeat. Note that the chi-squared value can go up significantly between iterations, but this is because the model is simplifying itself at a much faster rate by loosing parameters - it's Occam's razor at work. Again see the [d'Auvergne and Gooley, 2008b ] paper at http://dx.doi.org/10.1007/s10858-007-9213-3 for figures demonstrating this. The concept as to what is happening during this combined model-free optimisation and model selection algorithm is described in the [d'Auvergne and Gooley, 2007 MolBiosyst ] paper at http://dx.doi.org/10.1039/b702202f. It can take up to 20 iterations or more to reach convergence, depending upon the quality of the relaxation data and the 3D structure or the system in study.
* g) Once steps a-f have been completed for all global models (characterised by the spheroid, prolate spheroid, oblate spheroid, and
* h) Monte Carlo simulations for error analysis must be performed at the end.
* i) Elimination of failed Monte Carlo simulations is essential for keeping the errors to reasonable values for certain spin systems. This is also a relax-only feature (see the [d'Auvergne and Gooley, 2007 ] model elimination paper athttp://dx.doi.org/10.1007/s10858-006-9007-z).
These steps must be implemented independently of which software you use, as NONE implement the full protocol. Note however that the protocol I developed (in the [d'Auvergne and Gooley, 2007 ] theory paper at http://dx.doi.org/10.1039/b702202f and the [d'Auvergne and Gooley, 2008b ] paper at http://dx.doi.org/10.1007/s10858-007-9213-3) is fully implemented in relax, however this required multiple field strength data.
This is a rather large script located at '''auto_anlayses/dauvergne_protocol.py'''. This protocol is used by the GUI. So one option would be to copy this '''auto_anlayses/dauvergne_protocol.py''' script and modify it for the figure 7.2 protocol.
I must warn you about using single field strength data. It is now quite difficult to publish a model-free analysis with only single field strength data as most of the field know about the catastrophic analysis failures resulting in large amounts of artificial motion. These failures can also be much more subtle. Many reviewers will ask for such data to be collected as the results cannot not be trusted otherwise. For a model-free analysis, it is almost
essential to collect data at multiple field strengths, otherwise it can be sometimes impossible to distinguish between the anisotropic part of the Brownian tumbling of the molecule and internal motion - specifically due to the NH vectors in secondary structure elements all pointing in a similar direction. I have a much better explanation, as well as citations to all the relevant literature in: * [d'Auvergne E. J.and Gooley, Gooley P. R. (2007). Set theory formulation of the model-free problem and the diffusion seeded model-free paradigm. Mol. Biosyst., 3(7), 483-494. (http://dx.doi.org/10.1039/b702202f)]. In this paper, you will see reviewed both the artificial nanosecond motions of the Schurr 1994 paper and the artifical Rex motions of the Tjandra 1995 paper.
=== Recommendation ===
= References =
* [*d'Auvergne and Gooley 2006] d'Auvergne, E. J. and Gooley, P. R. (2006). Model-free model elimination: A new step in the model-free dynamic analysis of NMR relaxation data. ''J. Biomol. NMR'', '''35'''(2), 117-135. (DOI [http://dx.doi.org/10.1007/s10858-006-9007-z 10.1007/s10858-006-9007-z]).
* [*d'Auvergne and Gooley, 2007] d'Auvergne, E. J. and Gooley, P. R. (2007). Set theory formulation of the model-free problem and the diffusion seeded model-free paradigm. ''Mol. BioSyst.'', '''3'''(7), 483–494. (DOI: [http://dx.doi.org/10.1039/b702202f 10.1039/b702202f]).
* [*d'Auvergne and Gooley, 2008a] d'Auvergne, E. J. and Gooley, P. R. (2008). Optimisation of NMR dynamic models I. Minimisation algorithms and their performance within the model-free and Brownian rotational diffusion spaces. ''J. Biomol. NMR'', '''40'''(2), 107-119. (DOI: [http://dx.doi.org/10.1007/s10858-007-9214-2 10.1007/s10858-007-9214-2]).
* [*d'Auvergne and Gooley, 2008b] d'Auvergne, E. J. and Gooley, P. R. (2008). Optimisation of NMR dynamic models II. A new methodology for the dual optimisation of the model-free parameters and the Brownian rotational diffusion tensor. ''J. Biomol. NMR'', '''40'''(2), 121-133. (DOI: [http://dx.doi.org/10.1007/s10858-007-9213-3 10.1007/s10858-007-9213-3]).
* [*Orekhov et al., 1999] Orekhov, V. Y., Korzhnev, D. M., Diercks, T., Kessler, H., and Arseniev, A. S. (1999). H-1-N-15 NMR dynamic study of an isolated alpha-helical peptide (1-36)bacteriorhodopsin reveals the equilibrium helix-coil transitions. ''J. Biomol. NMR'', '''14'''(4), 345–356. (DOI: [http://dx.doi.org/10.1023/a:1008356809071 10.1023/a:1008356809071]).
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