Hi, I contacted a few people concerning the sharing of CPMG relaxation dispersion data to put in the system tests... These are data from papers published by the Kay, Wright and Torchia labs... I should have news from them in the coming week.
If someone knows of a few good papers for R1rho dispersion relaxation, it could be a great idea to contact them to get the raw and fitted data to input into our system tests... I can take care of that part too if I know who to contact and for which paper... Regards, Séb :) Edward d'Auvergne wrote: > On Thu, Jan 8, 2009 at 5:40 AM, Sébastien Morin > <[email protected]> wrote: > >> Hi, >> >> I thought about the task (Should I add the implementation of relaxation >> dispersion as a task on the Gna web site ?) and here is how I see the >> flow-through of an analysis... >> > > If you would like to add it as a task, feel free and then assign it to > yourself. > > > >> 1. >> A user will want to perform relaxation dispersion analysis. The user >> will first have to chose whether CPMG or R1rho experiments were >> recorded, the choice of which will determine if intensities are >> associated to different CPMG pulse train frequencies or to different >> R1rho spin lock field strength. >> >> Let's say the user types the command: >> relax_disp.exp_type('cpmg') >> and chooses the CPMG experiment. >> >> The intensities will then be associated to different CPMG pulse train >> frequencies, or to a null frequency (for the reference spectrum). >> > > Having a value of None or something like a string value of 'ref' for > the CPMG frequencies would be enough to identify the reference > spectrum. > > > >> We can now assume that the user recorded CPMG experiment from which >> R2eff values are extracted as follows: >> R2eff = ( 1 / T ) * Ln( Icpmg / Iref ) >> where T is the constant time relaxation delay, Icpmg is the peak height >> with CPMG, and Iref is the peak height without CPMG (reference). >> >> There should be a function to input the delay T used... Should this >> function be called something like: >> relax_disp.cpmg_delayT() >> and accept a float ? >> > > This should accept a float, and it should be a value in seconds. > Other suggestions for the name of the user function: > > relax_disp.tau_cpmg(), > relax_disp.CT_period(), > relax_disp.CT_length(), > relax_disp.cmpg_delay(), > etc. > > I'm not sure of the equation yet, but If we have to convert to the > unitless radial units this can be done later within relax. > > > >> From this step, peak intensities will be used to calculate R2eff for the >> different frequencies used. How should duplicated reference spectra (if >> present) be treated (since they have an effect in every R2eff calculated) ? >> > > I think they should be treated exactly the same as in the exponential > relaxation curve-fitting - i.e. treat each duplicate spectrum as a > separate time point. The exception would be the reference spectrum > where the average value must, obviously because of the equation you > give above, be used. I think a better error analysis for relaxation > dispersion would not be replicated spectra but rather measuring the > RMSD of the base plane noise and using that as the errors for the > minimisation and Monte Carlo simulations. Does anyone know how error > analysis is performed in relaxation dispersion? I'm not familiar with > it enough. > > > >> In the future, we could add a function for selecting this approach or >> another (if others exist and are of interest). >> > > All this infrastructure is already within relax, and is independent > from the analysis type. I developed it all in the 'spectral_errors' > branch which was merged back into the mainline at r8093. The only > code missing would be for handling duplicated reference spectra, but > this should be code specific to relaxation dispersion. And it could > be developed later on as I don't know if people collect the reference > spectrum in duplicate. > > > >> 2. >> The user will then need to chose which mathematical model to use, fast >> or slow exchange. Let's say the user types a command like: >> relax_disp.select_model('fast') >> and chooses fast exchange. >> >> (Should this function be renamed to something like: >> relax_disp.cpmg_select_model() since it is associated to CPMG experiments ?) >> > > For simplicity, I think that this function should handle both the CPMG > and the R1rho and simply raise a RelaxError if 'r1rho' is set and a > CMPG equation is selected (and probably raise a RelaxError if no > experiment type is set). Are there any equations shared between the > CMPG and R1rho analyses? If each model identifies the experiment type > unambiguously, then the relax_disp.exp_type() user function is not > necessary. I'm not sure what is best here until I see all the > equations - even those for the accordion type experiments. > > I saw the code you added here, and I think that putting the equations, > references, and descriptions into the user function docstrings as you > did in your Mathematica notebook would be very useful. > > > >> The R2eff values calculated in part 1 will then be input in the chosen >> equation and minimised. >> >> This is different as in standard curve fitting for R1 and R2, since the >> R2eff values are first calculated and then are passed to the dispersion >> equations to be minimised. >> >> Am I right ? >> > > This is correct. There should be a method added to the > specific_fns.relax_disp.Relax_disp class which calculates R2eff using > the equation you gave above. This method can then be called by > methods such as calc(), back_calc(), grid_search(), minimise(), etc. > to generate the R2eff values. > > > >> In the case of CPMG data in the fast-exchange limit, should the >> relaxation dispersion specific user commands be as follows: >> >> relax_disp.exp_type('cpmg') >> relax_disp.cpmg_delayT(0.040) >> relax_disp.cpmg_select_model('fast') >> >> and should the data go as follows: >> >> intensities --> calculated [R2eff] --> minimised [R2, Rex, kex] >> > > Here in the script you would use: > > spectrum.read_intensities() > spectrum.replicated() or spectrum.baseplane_rmsd() > spectrum.error_analysis() > frq.set() (set the spectrometer frequency) > relax_disp.nu_cmpg() (or maybe tcp_cmpg in seconds, and then relax > will convert to radial units) > grid_search(inc=11) > minimise('simplex', scaling=False, constraints=False) > > The grid_search() and minimise() functions should automatically at the > start calculate R2eff. > > > >> ====================================== >> ====================================== >> >> Moreover, what about the intermediate-exchange limit ? Is that of any >> importance and should it be implemented also in relax ? >> I propose yes, although I'll have to check for the equations, and >> everything... >> > > Again this can be treated as another model - which can be added later. > If the infrastructure for the other models is in place, then adding > this will be insanely trivial. > > > >> Also, as for choosing whether slow-, intermediate- or fast-exchange >> equations are best suited for the data, there is, of course, the use of >> model selection implemented into relax, but also the use of the alpha >> parameter (Millet et al., JACS, 2000, 122: 2867-2877 (equation 14)) >> which can help determining the exchange limit... >> How could we combine both ? >> > > If alpha is found within an equation and is optimised - then this is > again another model. This model tells you where in the exchange > regime you are. This can go directly into AIC and BIC model selection > for comparison to the other models such as 'fast', 'slow', etc. When > ever there is a different equation, even slightly, then this is a new > mathematical model. > > > >> Finally, what about group fitting of dispersion data to extract better >> parameters based on a common exchange rate (kex or kA, for fast- or >> slow-exchange, respectively) ? Should this be thought about in the >> beginning of the development or will it be easy to implement this when >> the rest works ? >> > > Do you mean have one parameter that spans multiple spins? This again > would be another model. It would be global though as it affects more > than one spin. It will require a different set of equations - ones > that loop over the relevant spins. It will be a bit like the global > model-free models where the diffusion tensor is optimised affecting > the model-free parameters of individual spins. This, like all new > models, can be added later with zero redesign of relax. The > infrastructure will be in place and you just have to code the > equations into a new function in maths_fns (and add some documentation > to the relax_disp.select_model() user function). > > Cheers, > > Edward > > _______________________________________________ relax (http://nmr-relax.com) This is the relax-devel mailing list [email protected] To unsubscribe from this list, get a password reminder, or change your subscription options, visit the list information page at https://mail.gna.org/listinfo/relax-devel
