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__TOC__
= Intro =
This tutorial is based on the analysis of NMR data from the paper:
<blockquote>
The inverted chevron plot measured by NMR relaxation reveals a native-like unfolding intermediate in acyl-CoA binding protein. <br>
</blockquote>
The data is recorded as FID interleaved.
= Preparation =
= Get the process helper scripts =
Go into the '''scripts''' directory and download these scripts to there.
# [[Tutorial_for_Relaxation_dispersion_analysis_cpmg_fixed_time_recorded_on_varian_as_fid_interleaved_scripts#convert_all.com | convert_all.com]]
# [[Tutorial_for_Relaxation_dispersion_analysis_cpmg_fixed_time_recorded_on_varian_as_fid_interleaved_scripts#fft_all.com | fft_all.com]]
# [[Tutorial_for_Relaxation_dispersion_analysis_cpmg_fixed_time_recorded_on_varian_as_fid_interleaved_scripts#CPMG_1_sort_pseudo3D_initialize_files.sh | CPMG_1_sort_pseudo3D_initialize_files.sh]]
# [[Tutorial_for_Relaxation_dispersion_analysis_cpmg_fixed_time_recorded_on_varian_as_fid_interleaved_scripts#CPMG_2_convert_and_process.sh | CPMG_2_convert_and_process.sh ]]
# [[Tutorial_for_Relaxation_dispersion_analysis_cpmg_fixed_time_recorded_on_varian_as_fid_interleaved_scripts#CPMG_3_fft_all.sh | CPMG_3_fft_all.sh]]
# [[Tutorial_for_Relaxation_dispersion_analysis_cpmg_fixed_time_recorded_on_varian_as_fid_interleaved_scripts#NMRPipe_to_Sparky.sh | NMRPipe_to_Sparky.sh]]
# [[Tutorial_for_Relaxation_dispersion_analysis_cpmg_fixed_time_recorded_on_varian_as_fid_interleaved_scripts#sparky_add.sh | sparky_add.sh]]
# [[Tutorial_for_Relaxation_dispersion_analysis_cpmg_fixed_time_recorded_on_varian_as_fid_interleaved_scripts#stPeakList.pl | stPeakList.pl]]
Then make them executable, and add to PATH.
<source lang="bash">
cd scripts
# Change shell
tcsh
# Make them executable
chmod +x *.sh *.com *.pl
# Add scripts to PATH
setenv PATH ${PWD}:${PATH}
# Go back to previous directory
= Extract interleaved spectra, process to NMRPipe and do spectral processing =
== Extract interleaved and change format to NMRPipe ==
sort out the interleaved fid with the script [https://raw.github.com/nmr-relax/relax_scripts/master/shell_scripts/[Tutorial_for_Relaxation_dispersion_analysis_cpmg_fixed_time_recorded_on_varian_as_fid_interleaved_scripts#CPMG_1_sort_pseudo3D_initialize_files.sh | CPMG_1_sort_pseudo3D_initialize_files.sh]].
<source lang="bash">
# Copy data
# Click 'Save script' to make 'fid.com' file, and 'Quit', and run the next CPMG script
Now it is time to convert all the fid from varian format to NMRPipe with the script [https://raw.github.com/nmr-relax/relax_scripts/master/shell_scripts/[Tutorial_for_Relaxation_dispersion_analysis_cpmg_fixed_time_recorded_on_varian_as_fid_interleaved_scripts#CPMG_2_convert_and_process.sh | CPMG_2_convert_and_process.sh]].
<source lang="bash">
CPMG_2_convert_and_process.sh
== Spectral processing ==
<source lang="bash">
CPMG_3_fft_all.sh
== Convert all *.ft2 files to ucsf format, so they can be opened in SPARKY ==
Done by the script [https://raw.github.com/nmr-relax/relax_scripts/master/shell_scripts/[Tutorial_for_Relaxation_dispersion_analysis_cpmg_fixed_time_recorded_on_varian_as_fid_interleaved_scripts#NMRPipe_to_Sparky.sh | NMRPipe_to_Sparky.sh]]
<source lang="bash">
NMRPipe_to_Sparky.sh
Ok
'''rp''' for read peaks. Find your peak file., which should be in format [[SPARKY_list]] <br>
../peak_lists/peaks.list
Click Create peaks, Close.
cd ../peak_lists/
</source>
We can add values to a column by using script
[[Tutorial_for_Relaxation_dispersion_analysis_cpmg_fixed_time_recorded_on_varian_as_fid_interleaved_scripts#sparky_add.sh | sparky_add.sh]]
Correct Nitrogen
As stated in the [[manual | relax manual]] section '''5.2.1 Temperature control and calibration''', the pulse sequence can put a lot of power into the sample. <br>
You could read these sections in the relax manual: <br>[http://www.nmr-relax.com/manual/Temperature_control_calibration.html Importance of Temperature control and calibration]<br>[http://www.nmr-relax.com/manual/relax_data_temp_control.html Temperature control]<br>[http://www.nmr-relax.com/manual/relax_data_temp_calibration.html Temperature calibration]<br> It is therefore good also good practice to inspect for peak movements, by overlaying all spectra:
Open all the files, and overlay them with SPARKY command '''ol'''.
=== Generate spectral point file ===
Create a file with spectral point information with script [https://raw.github.com/nmr-relax/relax_scripts/master/shell_scripts/[Tutorial_for_Relaxation_dispersion_analysis_cpmg_fixed_time_recorded_on_varian_as_fid_interleaved_scripts#stPeakList.pl | stPeakList.pl]].
<source lang="bash">
stPeakList.pl 0.fid/test.ft2 ../peak_lists/peaks_corr_final.list > peaks_list.tab
</source>
=Analyse in relax = == Extract the spectra settings from Varian procpar file ===Now we want to make a frequency setting settings file for we can read in relax.
<source lang="bash">
set NCYCLIST=`awk '/^ncyc /{f=1;next}f{print $0;exit}' procpar`; echo $NCYCLIST
end
cat ncyc.txt
</source>
== Measure the backgorund noise "RMSD" in each of the .ft2 files ==
=== RMSD via sparky ===
There exist two ways to get the background RMSD noise
# For whole spectrum: http://www.cgl.ucsf.edu/home/sparky/manual/views.html#Noise
# For a region: http://www.cgl.ucsf.edu/home/sparky/manual/extensions.html#RegionRMSD <br>
We take the full background noise, to save time. <br>
<source lang="bash">
sparky 0.fid/test.ucsf
</source>
Then '''st''' and recompute for '''10000''' points.<br>
It should give valuee of order: 2.47e+03 or similar. <br>
Add the values to ncyc.txt in next column.
Repeat for all spectra.
We will use '''ncyc.text''' to make the spectra settings later, and should look like this.<br>
'''ncyc time_T2 CPMG_frq sfrq RMSD'''
<source lang="text">
28 0.06 466.66666666666666666666 599.8908622 2.47e+03
0 0.06 0 599.8908622 2.34e+03
4 0.06 66.66666666666666666666 599.8908622 2.41e+03
32 0.06 533.33333333333333333333 599.8908622 2.42e+03
60 0.06 1000.00000000000000000000 599.8908622 2.45e+03
2 0.06 33.33333333333333333333 599.8908622 2.42e+03
10 0.06 166.66666666666666666666 599.8908622 2.42e+03
16 0.06 266.66666666666666666666 599.8908622 2.44e+03
8 0.06 133.33333333333333333333 599.8908622 2.39e+03
20 0.06 333.33333333333333333333 599.8908622 2.4e+03
50 0.06 833.33333333333333333333 599.8908622 2.42e+03
18 0.06 300.00000000000000000000 599.8908622 2.46e+03
40 0.06 666.66666666666666666666 599.8908622 2.41e+03
6 0.06 100.00000000000000000000 599.8908622 2.45e+03
12 0.06 200.00000000000000000000 599.8908622 2.45e+03
0 0.06 0 599.8908622 2.39e+03
24 0.06 400.00000000000000000000 599.8908622 2.45e+03
</source>
=== RMSD via nmrpipe showApod ===
We can also use the showApod rmsd.
<source lang="bash">
set FIDS=`cat ft2_files.ls`
set OUT=${PWD}/apod_rmsd.txt
set CWD=$PWD
rm $OUT
foreach I (`seq 1 ${#FIDS}`)
set FID=${FIDS[$I]}; set DIRN=`dirname $FID`
cd $DIRN
set apodrmsd=`showApod *.ft2 | grep "REMARK Automated Noise Std Dev in Processed Data:" | awk '{print $9}'`
echo $apodrmsd $DIRN >> $OUT
cd $CWD
end
cat $OUT
mv ncyc.txt ncyc_or.txt
paste ncyc_or.txt $OUT > ncyc.txt
</source>
== Prepare directory for relax run ==
Then we make a directory ready for relax
<source lang="bash">
mkdir ../relax
cp ncyc.txt ../relax
cp peaks_list* ../relax
cd ../relax
</source>
== relax script for setting experiment settings to spectra ==
Add the following python relax script file to the relax directory.
This can be modified as wanted.<br>
This is to save "time" on the tedious work on setting the experimental conditions for each spectra.
'''relax_3_spectra_settings.py'''
<source lang="python">
# Loop over the spectra settings.
ncycfile=open('ncyc.txt','r')
# Make empty ncyclist
ncyclist = []
i = 0
for line in ncycfile:
ncyc = line.split()[0]
time_T2 = float(line.split()[1])
vcpmg = line.split()[2]
set_sfrq = float(line.split()[3])
rmsd_err = float(line.split()[4])
print ncyc, time_T2, vcpmg
# Test if spectrum is a reference
if float(vcpmg) == 0.0:
vcpmg = None
else:
vcpmg = round(float(vcpmg),3)
# Add ncyc to list
ncyclist.append(int(ncyc))
# Set the current spectrum id
current_id = "Z_A%s"%(i)
# Set the current experiment type.
relax_disp.exp_type(spectrum_id=current_id, exp_type='SQ CPMG')
# Set the peak intensity errors, as defined as the baseplane RMSD.
spectrum.baseplane_rmsd(error=rmsd_err, spectrum_id=current_id)
# Set the NMR field strength of the spectrum.
spectrometer.frequency(id=current_id, frq=set_sfrq, units='MHz')
# Relaxation dispersion CPMG constant time delay T (in s).
relax_disp.relax_time(spectrum_id=current_id, time=time_T2)
# Set the relaxation dispersion CPMG frequencies.
relax_disp.cpmg_setup(spectrum_id=current_id, cpmg_frq=vcpmg)
i += 1
# Specify the duplicated spectra.
#spectrum.replicated(spectrum_ids=['Z_A1', 'Z_A15'])
# The automatic way
dublicates = map(lambda val: (val, [i for i in xrange(len(ncyclist)) if ncyclist[i] == val]), ncyclist)
for dub in dublicates:
ncyc, list_index_occur = dub
if len(list_index_occur) > 1:
id_list = []
for list_index in list_index_occur:
id_list.append('Z_A%s'%list_index)
# We don't setup replications, since we have RMSD values from background noise
print id_list
#spectrum.replicated(spectrum_ids=id_list)
# Delete replicate spectrum
spectrum.delete('Z_A15')
</source>
== Analyse ==
'''NOTES about speed up of model selection:'''
To speed up the model selection, see [[:Category:Time_of_running]].<br>
'''Monte-Carlo simulations:'''<br>
We will set the number of Monte-Carlo simulations to '''10''', for inspection.<br>
This will not affect model selection. <br>
For initial analyses where errors are not so important, the number of simulations can be dropped massively to speed things up. <br>
If errors are not important for specific cases, set the number of MC sims to 3-10, and the analysis will perform much more rapidly. <br>
The result is that the error estimates of the parameters are horrible but, but in some cases, excluding publication, that is not such a problem.
'''Which models to chose for running:''' This text is based on [http://article.gmane.org/gmane.science.nmr.relax.devel/4426 this email thread discussion]: <br>
When you are analysing data, you would probably limit the number of models to 2-3. <br>
For example if you know that all residues are experiencing '''slow exchange''', the '''LM63''' '''fast exchange''' model does not need to be used. <br>
It is interesting to see that sometimes the analytic models are selected and sometimes the numeric models. <br>
But this is an academic curiosity, it is probably not a practical question anyone analysing real dispersion data is interested in. <br>
The way an analysis would normally be performed is to first decide if the analytic or numeric approach is to be used. <br>
For the '''analytic approach''' with slow exchange, you only need the '''No Rex''' and '''CR72''' models. <br>
You could add the '''IT99''' model if you can see that pA >> pB in the spectra, i.e. the pB peak is tiny.
If you take the '''numeric approach''', then the 'No Rex' and 'NS 2-site expanded' models can be used. <br>
Once you perform an initial analysis of all residues separately, you can then look at the dynamics parameter values and judge which spins to
cluster together to have the same model of dynamics, then re-perform the analysis.
=== Analyse in GUI ===
Start relax in GUI mode
<source lang="python">
relax_disp -g -t log_relax_4_model_sel.log
</source>
# Ctrl+n for new analysis
# Select '''Relaxation dispersion analysis''' button -> Next
# Starting pipe: '''base pipe'''
# Pipe bundle: '''relax_disp''' -> Start
# We want to load the spins manually, so in next window, then go to "User functions (n-z) -> script"
# Select file_name: '''relax_2_spins.py''' -> OK
# Then click Spin Isotopes button:
# The nuclear isotope name: 15N
# The spin ID string: @N* -> OK
# The load spectra: Select button "Add" under spectra list:
# The file name: '''peaks_list_max_standard.ser'''
# The spectrum ID string: auto
# Leave the rest of the fields as they are, they are not used.
# Push "Apply" and then '''Cancel'''
# We want to change the spectra properties by a script.
# Go to "User functions (n-z) -> script"
# Select file_name: '''relax_3_spectra_settings.py''' -> OK
# Before executing, it would be a good idea to save the state, to save the current setup.
# This '''state''' file will also be used for loading, before a later cluster/global fit analysis.
# Shift+Ctrl+s OR File-> Save as... '''ini_setup.bz2'''
# Make a directory for the output of the results, f.ex: '''model_sel_analyt'''.
# Point '''Results directory''' to '''model_sel_analyt'''.
# Set Monte-Carlo Simulations to '''10'''
# Select models: Lets take '''"R2eff", "No Rex", "TSMFK01", "LM63", "CR72", "CR72 full", "IT99"'''
# Save the state again, so the settings for models, monte-carlo settings and result directory is preserved.
# Shift+Ctrl+s OR File-> Save as... '''ini_run.bz2''' in the '''model_sel_analyt''' directory.
# Now push "Execute"
The analysis will probably take between 4-10 hours.<br>
=== Analyse via script ===
Add the following python relax script file to the relax directory
'''relax_1_ini.py'''
<source lang="python">
# Taken from the relax disp manual, section 10.6.1 Dispersion script mode - the sample script
# Create the data pipe.
pipe_name = 'base pipe'
pipe_bundle = 'relax_disp'
pipe.create(pipe_name=pipe_name, bundle=pipe_bundle, pipe_type='relax_disp')
# Create the spins
spectrum.read_spins(file="peaks_list_max_standard.ser", dir=None)
# Name the isotope for field strength scaling.
spin.isotope(isotope='15N')
# Read the spectrum from NMRSeriesTab file. The "auto" will generate spectrum name of form: Z_A{i}
spectrum.read_intensities(file="peaks_list_max_standard.ser", dir=None, spectrum_id='auto', int_method='height')
# Set the spectra experimental properties/settings.
script(file='relax_3_spectra_settings.py', dir=None)
# Save the program state before run.
# This state file will also be used for loading, before a later cluster/global fit analysis.
state.save('ini_setup', force=True)
</source>
'''relax_4_model_sel.py'''
<source lang="python">
import os
from auto_analyses.relax_disp import Relax_disp
# Load the initial state setup
state.load(state='ini_setup.bz2')
# Set settings for run.
results_directory = os.path.join(os.getcwd(),"model_sel_analyt")
pipe_name = 'base pipe'; pipe_bundle = 'relax_disp'
MODELS = ['R2eff', 'No Rex', 'TSMFK01', 'LM63', 'CR72', 'CR72 full', 'IT99']
GRID_INC = 21; MC_NUM = 10; MODSEL = 'AIC'
# Execute
Relax_disp(pipe_name=pipe_name, pipe_bundle=pipe_bundle, results_dir=results_directory, models=MODELS, grid_inc=GRID_INC, mc_sim_num=MC_NUM, modsel=MODSEL)
</source>
And the just start relax with
<source lang="bash">
relax_disp relax_1_ini.py -t log_relax_1_ini.log
relax_disp relax_4_model_sel.py -t log_relax_4_model_sel.log
</source>
The analysis will probably take between 4-10 hours.<br>
== Rerun from a "ini_setup.bz2" file ==
If something goes wrong, you can open the '''ini_setup.bz2''' in the '''model_sel_analyt''' directory.
Just start relax:
relax_disp -g -t log_relax_4_model_sel.log
and open the '''ini_setup.bz2''' from File->"Open relax state".<br>
It should jump to the analysis window, make corrections, and you can then click "Execute".
In script, just follow section [[Tutorial_for_Relaxation_dispersion_analysis_cpmg_fixed_time_recorded_on_varian_as_fid_interleaved#Analyse_via_script | Analyse_via_script]].
= Inspecting results from the relax analysis =
In the main directory, there should be an auto-saved '''final_state.bz2''' which can
opened to inspect the results.
After the analysis, several folders should be available, with data for each fitted model.
<source lang="bash">
R2eff/
No Rex/
TSMFK01/
LM63/
CR72/
CR72 full/
IT99/
final/
</source>
In each of these folders, there is a [[grace2images.py]] python file, which will as standard convert the
grace script files to PNG files.
== Inspect graphs ==
You can convert all to PNG images, by:
<source lang="bash">
cd "R2eff"; ./grace2images.py; cd .. ;
cd "No Rex"; ./grace2images.py; cd .. ;
cd "TSMFK01"; ./grace2images.py; cd .. ;
cd "LM63"; ./grace2images.py; cd .. ;
cd "CR72"; ./grace2images.py; cd .. ;
cd "CR72 full"; ./grace2images.py; cd .. ;
cd "IT99"; ./grace2images.py; cd .. ;
cd "final"; ./grace2images.py; cd .. ;
find . -type f -name "*.png"
# And if you want to delete them.
find . -type f -name "*.png" -exec rm -f {} \;
</source>
You can then quickly go through the fitted graphs for the models.
== Convert log file to relax script ==
If you made a logfile, then you can do convert it to the full relax script.<br>
See [[Grep_log_file]] for this.
== Compare values ==
For the '''TSMFK01''' and for example the '''CR72''', the '''k_AB''' value can be compare
<source lang="bash">
cd model_sel_analyt
paste "TSMFK01/k_AB.out" "CR72/k_AB.out" | awk '{print $2, $3, $6, $13}'
</source>
== Inspect model selection for residues ==
=== Grep AIC selection from logfile ===
If you have a log file.
<source lang="bash">
set IN=log_relax_4_model_sel.log ;
set OUT=log_relax_4_model_sel_chosen_models.txt ;
set FROM=`grep -n "AIC model selection" $IN | cut -d":" -f1` ;
set TO=`grep -n "monte_carlo.setup(" $IN | cut -d":" -f1` ;
sed -n ${FROM},${TO}p $IN > $OUT ;
cat $OUT ;
</source>
=== get spin.model ===
See [[:Category:List_objects]] to get inspiration how to loop through the data class containers.
You should open the '''final_state.bz2''' in the result directory.
<source lang="python">
state.load(state='final_state.bz2')
# See which data is in the pipe
pipe.display()
# print the spin model, first import spin_loop
from pipe_control.mol_res_spin import spin_loop
print("%20s %20s" % ("# Spin ID", "Model"))
for spin, spin_id in spin_loop(return_id=True, skip_desel=True):
print("%20s %20s" % (repr(spin_id), spin.model))
</source>
You can also in the GUI see this in the '''Spin Viewer window''' under "View -> Spin Viewer (Ctrl+t)". <br>
Select a spin, and look for the Variable '''model'''.
= Execute a clustering analysis =
'''Notes about how to select residues for clustering. Based on [http://article.gmane.org/gmane.science.nmr.relax.devel/4442 this email thread:] '''<br>
Clustering is a manual operation and it should not be automated. <br>
It is based on human logic and is highly subjective. <br>
For example it could be decided that one analysis is performed whereby one motional process is assumed, i.e. one kex value for all exchanging
spins. <br>
Or it could be decided that there are two motional processes, so two clusters are created, each having their own kex. <br>
Some spins with bizarre dynamics may be left out as 'free spins' and not used in the cluster. <br>
If you just want all spins with '''Rex''' to be in one cluster, you could just use all spins where '''spin.model''' is not set to '''No Rex'''.
'''Notes about how clustering is performed in relax.'''<br>
All spins of one cluster ID will be optimised as one model. <br>
Several cluster ID will result in all those spins being optimised separately, but again with all spins together. <br>
Any spins not in a cluster ID (labelled as '''free spins''') will be optimised individually. <br>
Have a look at the '''model_loop()''' method of the '''specific_analyses.relax_disp.api''' module, <br>
and the function '''specific_analyses.relax_disp.disp_data.loop_cluster''' which it uses.
== Inspect residues for clustering ==
Let us select residues based on a criterion where the highest number of residues have been fitted to the same model.
Open the '''final_state.bz2''' in relax GUI. <br>
You can see the model select for each residue in the '''Spin viewer''' (View -> Spin viewer (Ctrl+T)). Look for the '''Variable''' '''model'''.
Open the relax prompt with '''Ctrl+p''' if you are in the GUI.<br>
Tip: You can copy the lines, and in the relax prompt, select "Paste Plus".
<source lang="python">
from pipe_control.mol_res_spin import spin_loop
# Open file for writing
cluster_file = "cluster_residues.txt"
f = open(cluster_file, 'w')
# Make a list to count number of models
resi_models = []
for spin, mol_name, res_num, res_name, spin_id in spin_loop(full_info=True, return_id=True, skip_desel=True):
# Write models to file
f.write( str(spin_id) + " ; " + str(spin.model) + " ; " + str(mol_name) + " ; " + str(res_num) + " ; " + str(res_name) + "\n" )
# Append models to list
resi_models.append(spin.model)
# Count resi_models
c_resi_models = dict((i,resi_models.count(i)) for i in resi_models)
print c_resi_models
# Write count result to file
for key, val in c_resi_models.items():
f.write( "# ; " + str(key) + " ; " + str(val) + "\n" )
#Close the file
f.close()
</source>
Copy '''cluster_residues.txt''' to the initial directory.
== Create new analysis clustering ==
For the clustered analysis, you need to start a new analysis. <br>
You should not load the results from the final pipe, since this will likely be fatal for the clustered analysis. <br>
The auto-analysis is designed to take the pre-run directory name and load the results files for each model itself (not the state file). <br>
Each results file will be loaded into a temporary data pipe and the initial parameter values copied from that. <br>
So Close relax, and then add these files.
=== Do clustering Analysis in GUI ===
Start relax in GUI mode
<source lang="python">
relax_disp -g -t log_relax_5_cluster.log
</source>
# Open the '''ini_setup.bz2''' from File->"Open relax state".
# Open the '''relax prompt''' with '''Ctrl+p'''. And paste this is.
<source lang="python">
# Cluster residues
cluster_file = "cluster_residues.txt"
f = open(cluster_file, 'r')
for line in f:
if line[0] == "#":
continue
else:
spinid = line.split(";")[0].strip()
spinmodel = line.split(";")[1].strip()
# Deselect those spins not showing exchange for further analysis.
if spinmodel == "No Rex":
deselect.spin(spin_id=spinid, change_all=False)
else:
relax_disp.cluster('model_cluster', spinid)
f.close()
# Check which are clustered
print cdp.clustering
# Check for selected/deselected spins.
for spin, spin_id in spin_loop(return_id=True, skip_desel=False):
print spin_id, spin.select
</source>
# Before executing, it would be a good idea to save the state after clustering.
# Shift+Ctrl+s OR File-> Save as... '''ini_setup_cluster.bz2'''
# Ctrl+d , right click "base pipe" and "Associate with a new auto-analysis"
# Close pipe viewer
# Make a directory for the output of the results, f.ex: '''model_clustering_analyt'''
# Point '''Results directory''' to '''model_clustering_analyt'''.
# Pint '''Previous run directory''' to previous result directory, where all the models had their folders. Values will be read from here. '''model_sel_analyt'''
# Set Monte-Carlo Simulations to '''50'''
# Select models: Lets take '''"R2eff", "No Rex", "TSMFK01"'''
# Now push "Execute"
=== Do clustering Analysis in script ===
Add the following python relax script file to the relax directory.
'''relax_5_cluster.py'''
<source lang="python">
"""Taken from the relax disp manual, section 10.6.1 Dispersion script mode - the sample script.
To run the script, simply type:
$ ../../../../../relax relax_5_cluster.py --tee relax_5_cluster.log
"""
import os
from auto_analyses.relax_disp import Relax_disp
from pipe_control.mol_res_spin import spin_loop
# Set settings for run.
pre_run_directory = os.path.join(os.getcwd(),"model_sel_analyt")
results_directory = os.path.join(os.getcwd(),"model_clustering_analyt")
cluster_file = "cluster_residues.txt"
# Load the previous final state with results.
state.load(state='final_state.bz2', dir=pre_run_directory, force=False)
# Open file for writing
f = open(cluster_file, 'w')
# Make a list to count number of models
resi_models = []
for spin, mol_name, res_num, res_name, spin_id in spin_loop(full_info=True, return_id=True, skip_desel=True):
# Write models to file
f.write( str(spin_id) + " ; " + str(spin.model) + " ; " + str(mol_name) + " ; " + str(res_num) + " ; " + str(res_name) + "\n" )
# Append models to list
resi_models.append(spin.model)
# Count resi_models
c_resi_models = dict((i,resi_models.count(i)) for i in resi_models)
print c_resi_models
# Write count result to file
for key, val in c_resi_models.items():
f.write( "# ; " + str(key) + " ; " + str(val) + "\n" )
#Close the file
f.close()
##################
# Cluster file for selection residues.
#################
# Load the initial state setup
state.load(state='ini_setup.bz2', force=True)
# Cluster residues
f = open(cluster_file, 'r')
for line in f:
if line[0] == "#":
continue
else:
spinid = line.split(";")[0].strip()
spinmodel = line.split(";")[1].strip()
# Deselect those spins not showing exchange for further analysis.
if spinmodel == "No Rex":
deselect.spin(spin_id=spinid, change_all=False)
else:
relax_disp.cluster('model_cluster', spinid)
f.close()
# Check which are clustered
print cdp.clustering
# Check for selected/deselected spins.
for spin, spin_id in spin_loop(return_id=True, skip_desel=False):
print spin_id, spin.select
# Save the program state before run.
state.save('ini_setup_cluster.bz2', force=True)
##################
# Run cluster analysis
#################
# Set settings for run.
pipe_name = 'base pipe'; pipe_bundle = 'relax_disp'
MODELS = ['R2eff', 'No Rex', 'TSMFK01']
GRID_INC = 21; MC_NUM = 50; MODSEL = 'AIC'
# Execute
Relax_disp(pipe_name=pipe_name, pipe_bundle=pipe_bundle, results_dir=results_directory, models=MODELS, grid_inc=GRID_INC, mc_sim_num=MC_NUM, modsel=MODSEL, pre_run_dir=pre_run_directory)
</source>
And the just start relax with
<source lang="bash">
relax_disp relax_5_cluster.py -t log_relax_5_cluster.log
</source>
= See also =
[[Category:Relaxation dispersion analysis]]
[[Category:Tutorials]]