CYANA Wiki - User contributions [en]

Peaklist preparation for eNOE pipeline

2022-04-08T07:45:38Z

Dima: /* Transfer of assignments to the NMRpipe peaks */

In this tutorial we will prepare the input data for the eNOE pipeline (assigned series of NOESY spectra with different mixing times) from manually / automatically generated assignments of a single NOESY spectrum.

In summary, our approach consists of following steps:
# NMRpipe peak peaking
# Transfer of manual or automatically generated assignments to the NMRpipe peaks
# Sequential assignment of NOESY spectra with different mixing times using NMRpipe

==== Software installation ====

This tutorial requires following software:
# CYANA
# Python3

==== Data preparation ====
Create the root project folder tutorial/
mkdir tutorial
cd tutorial
mkdir 01-Spectra
mkdir 02-PeakPeaking
mkdir 03-Assignment
mkdir 04-Series
mkdir 05-eNORA
mkdir 06-SingleState
mkdir 07-MultiState

Process all NOESY mixing times and save them in separate folders in 01-Spectra/ folder.

==== NMRpipe peak peaking ====
Peak the nitrogen region, aliphatic carbon region and aromatic carbon region of the NOESY with highest mixing time with NMRpipe. Optimize the number of the peaked peaks by adjusting the threshold such, that the total number of peaks is 2 to 5 times larger than the number of peaks in previously manually or automatically generated NOESY assignment (because NMRpipe also peaks water and a lot of "false positives" along the diagonal, peak even more for the aromatic peaks as they are highly diluted by backbone nitrogen peaks). Save the peaks as '''N15NOESY.tab''', '''C13NOESY_@ALI.tab''' and '''C13NOESY_@ARO.tab''' in the 02-PeakPeaking/ folder.

==== Transfer of assignments to the NMRpipe peaks ====
Create a fresh python environment
python -m venv venv
Activate it
source venv/bin/activate
Install necessary packages
pip install numpy
pip install pandas
pip install tqdm
pip install argparse
pip install matplotlib

This step has to be done only once. When you are finished with python scripts you can deactivate it
deactivate

And next time you can activate it again
source venv/bin/activate

Copy the peaked peaks to the 03-Assignment/ folder. Copy the assigned NOESY peaklist to the same folder. Save the transfer [[Media:Transfer.zip|script]] and execute it sequentially for the backbone, aliphatic and aromatic protons.
python convert.py /path/to/tutorial/03-Assignment/N15NOESY.tab /path/to/tutorial/03-Assignment/N15NOESY_@POS_@FLYA_asn.peaks N
python convert.py /path/to/tutorial/03-Assignment/C13NOESY_@ALI.tab /path/to/tutorial/03-Assignment/C13NOESY_@ALI_@FLYA_asn.peaks C_@ALI
python convert.py /path/to/tutorial/03-Assignment/C13NOESY_@ARO.tab /path/to/tutorial/03-Assignment/C13NOESY_@ARO@NEG_@FLYA_asn.peaks C_@ARO --w1_shift=HEAVY_ATOM_SPECTRAL_WIDTH_IN_PPM

Change the path/to/tutorial and HEAVY_ATOM_SPECTRAL_WIDTH_IN_PPM to the actual path and spectral width in heavy atom value.

This will create assigned NMRpipe tab files '''N15NOESY_asn.tab''', '''C13NOESY_@ALI_asn.tab''' and '''C13NOESY_@ARO_asn.tab''' in the 03-Assignment/ folder.

For quality control check that histograms of CYANA vs Pipe overlap for all 3 dimensions (for example file N_W1_overlay.png shows overlap between CYANA and Pipe shift histograms in the first dimensions for the nitrogen region of the NOESY).

==== Sequential assignment of NOESY spectra with different mixing times using NMRpipe ====
Copy assigned NMRpipe tab files to the 04-Series/ folder.

Create the NMRpipe script series3D_N.com for sequential assignment of the nitrogen spectrum with following content:
#!/bin/csh
set DX = 1
set DY = 3
set DZ = 2
set specList = (path/to/tutorial/01-Spectra/X/ft/HCNHnoesyHdirH%03d.ft3 path/to/tutorial/01-Spectra/Y/ft/HCNHnoesyHdirH%03d.ft3 path/to/tutorial/01-Spectra/Z/ft/HCNHnoesyHdirH%03d.ft3)
set inName = N15NOESY_asn.tab
echo -----------------------------------------------
echo Performing analysis by Fourier interpolation.
echo Updated peak table will be series.tab
echo
/bin/rm -f series.list
foreach i ($specList)
echo $i >> series.list
end
seriesTab -in $inName -list series.list -ndim 3 \
-out series.tab -dx $DX -dy $DY -dz $DZ \
-xzf 64 -yzf 64 -zzf 64 -adx 0 -ady 0 -adz 0 -max -verb

Change the path to the spectra such that X, Y, Z are spectra ordered from highest to lowest mixing times. If necessary, add additional entries.

Run the series script
csh
prepare nmrPipe
./series3D_N.com

This will create the series.tab file. Rename it to the series_N.tab and remove 2 lines from the header:
NULLVALUE -666
NULLSTRING *

Repeat this step for the aliphatic and aromatic protons. Combine all 3 series in series_all.tab files by keeping only one header. This combined series file can be directly used for [[ENORA and multi-state structure calculations]].

2022-04-08T07:08:45Z

File:NStates demo.zip

2021-09-29T16:21:08Z

Dima: uploaded a new version of "File:NStates demo.zip"

Demo data for the determination of the protein number of states tutorial

File:KeyNOE demo.zip

2021-09-29T16:20:16Z

Dima: uploaded a new version of "File:KeyNOE demo.zip"

Input data for the key NOE identification tutorial

File:Pop demo.zip

2021-09-29T16:19:49Z

Dima: uploaded a new version of "File:Pop demo.zip"

Demo data for the determination of protein state populations tutorial

Determination of the protein state populations

2021-09-07T11:42:47Z

Dima: /* Execution */

In this tutorial we will provide you with guided examples for determination of protein state populations.

In summary, our approach consists of following steps:
# We conduct a series of 10-state structure calculations with varying population parameter
# We evaluate structure calculations in terms of target function and correlations (using PDBcor)

==== Software installation ====

This tutorial requires following software:
# CYANA
# Python3
# UCSF Chimera
# PDBcor. In case if PDBcor is not yet installed, please go to this [https://github.com/dzmitryashkinadze/PDBCor link] and follow installation instructions. Later we will refer to the PDBcor installation path as '''PATH/To/PDBcor'''

==== Data preparation ====

Please follow the following steps:
# Download the [[Media:Pop_demo.zip|demo data]].
# Unpack the demo data

==== Execution ====

We recommend to use parallel computation for the execution as it will significantly reduce the total running time.

Please follow the following steps carefully (exact Linux commands are given below; you may copy them to a terminal):

First, edit the protein specific data in the folder '''data/'''. Make sure, that filenames are kept the same as in the demo. Advanced users can ignore it and fix PREPpop.cya for potential naming errors. Change the protein sequence in the root folder (same filename is not required).

Additionally, do following checks:

Check that '''init.cya''' and '''CALC.cya''' have '''rmsdrange''' variable (range to calculate RMSD of the protein bundle) set to correct value

Check that '''SPLITpop.cya''' has '''nres''' variable (number of residues) set to correct value. Keep in mind that in this case SPLITpop only takes one sample from the population A and population B. This means that SPLITpop will output a PDB with 40 conformers in 40 separate PDB models given both states A and B are populated or 20 conformers in 20 separate PDB models otherwise.

Then, test the '''data/''' folder by running a test calculation:
cp -r data test
cd test
cyana PREPpop.cya populations=5,5
cyana -n 20 CALC.cya '''#cyana CALC.cya if no parallel computing is available'''

Check that both PREP and CALC scripts run without errors.

Check in program Chimera that resulting structure '''bundle.pdb''' is correct.

Check that split structure '''splitall.pdb''' is split correct (each conformer 1-nres is a separate PDB model).

Then, edit RUN.cya:

Edit the cyana engine
cyana:=cyana (do it only if you are sure what it is)

If you do not have an access to parallel computation change the line of RUN.cya from
system "pwd; ls -l CALC.cya; $cyana -n 20 CALC inputseed=$seed"
to:
system "pwd; ls -l CALC.cya; $cyana CALC inputseed=$seed"

Then, run the series of two-state structure calculations:
cyana RUN.cya

This creates a folder for each 10-state structure calculation. Final structure is saved as bundle.pdb and final split structure (where each state is an independent model is saved as splitall.pdb).

If you do not have an access to parallel computation change the line of ANALYSE.cya from
system "bsub python '''PATH/To/PDBcor'''/correlationExtraction.py $dir/splitall.pdb --therm_iter=5"
to:
system "python '''PATH/To/PDBcor'''/correlationExtraction.py $dir/splitall.pdb --therm_iter=5"

Then, activate the PDBcor environment and run the correlation analysis:
source '''PATH/To/PDBcor'''/venv/bin/activate
cyana ANALYSE.cya

This creates a '''correlations/''' subfolder in each structure calculation folder with a correlation value that can be read from the '''correlations_backbone.txt''' file.

Finally, collect results in a single file:
cyana STAT_COLLECT.cya

This collects all correlation and target function values of all executed calculations in a single file using bash script '''extract_cor_value.sh''' into the output file '''results.txt'''.

File:Pop demo.zip

2021-09-07T11:39:58Z

Dima: uploaded a new version of "File:Pop demo.zip"

Demo data for the determination of protein state populations tutorial

Determination of the protein number of states

2021-09-07T11:32:09Z

Dima: /* Execution */

In this tutorial we will provide you with guided examples for determination of the protein number of states.

In summary, our approach consists of following steps:
# We conduct a series of structure calculations with 1-9 states
# We evaluate structure calculations in terms of target function and correlations (using PDBcor)

==== Software installation ====

This tutorial requires following software:
# CYANA
# Python3
# UCSF Chimera
# PDBcor. In case if PDBcor is not yet installed, please go to this [https://github.com/dzmitryashkinadze/PDBCor link] and follow installation instructions. Later we will refer to the PDBcor installation path as '''PATH/To/PDBcor'''

==== Data preparation ====

Please follow the following steps:
# Download the [[Media:Pop_demo.zip|demo data]].
# Unpack the demo data

==== Execution ====

We recommend to use parallel computation for the execution as it will significantly reduce the total running time.

Please follow the following steps carefully (exact Linux commands are given below; you may copy them to a terminal):

First, edit the protein specific data in the folder '''data/'''. Make sure, that filenames are kept the same as in the demo. Advanced users can ignore it and fix PREP.cya for potential naming errors. Change the protein sequence in the root folder (same filename is not required).

Additionally, do following checks:

Check that '''init.cya''' and '''CALC.cya''' have '''rmsdrange''' variable (range to calculate RMSD of the protein bundle) set to correct value

Check that '''SPLIT.cya''' has '''nres''' variable (number of residues) set to correct value

Then, test the '''data/''' folder by running a test calculation:
cp -r data test
cd test
cyana PREP.cya nbundle=3
cyana -n 20 CALC.cya '''#cyana CALC.cya if no parallel computing is available'''

Check that both PREP and CALC scripts run without errors.

Check in program Chimera that resulting structure '''bundle.pdb''' is correct.

Check that split structure '''splitall.pdb''' is split correct (each conformer 1-nres is a separate PDB model).

Then, edit RUN.cya:

Edit the cyana engine
cyana:=cyana (do it only if you are sure what it is)

If you do not have an access to parallel computation change the line of RUN.cya from
system "pwd; ls -l CALC.cya; $cyana -n 20 CALC inputseed=$seed"
to:
system "pwd; ls -l CALC.cya; $cyana CALC inputseed=$seed"

Then, run the series of two-state structure calculations:
cyana RUN.cya

This creates a folder for each multi-state structure calculation. Final structure is saved as bundle.pdb and final split structure (where each state is an independent model is saved as splitall.pdb).

If you do not have an access to parallel computation change the line of ANALYSE.cya from
system "bsub python '''PATH/To/PDBcor'''/correlationExtraction.py $dir/splitall.pdb --nstates=$ns --therm_iter=5"
to:
system "python '''PATH/To/PDBcor'''/correlationExtraction.py $dir/splitall.pdb --nstates=$ns --therm_iter=5"

Then, activate the PDBcor environment and run the correlation analysis:
source '''PATH/To/PDBcor'''/venv/bin/activate
cyana ANALYSE.cya

This creates a '''correlations/''' subfolder in each structure calculation folder with a correlation value that can be read from the '''correlations_backbone.txt''' file.

Since all structure calculations were done for different number of states their target functions are not directly comparable. We need first to normalize them.

For this execute bash script
sh normalize_tf.sh # it usually takes couple of minutes

Once the script is finished run it again and save the output
sh normalize_tf.sh > TF.txt

This file contains normalized target functions

Finally, collect results in a single file:
cyana STAT_COLLECT.cya

This collects all correlation and target function values of all executed calculations in a single file using bash script '''extract_cor_value.sh''' into the output file '''results.txt'''.

Identification of key NOEs

2021-09-07T11:31:37Z

Dima: /* Execution */

In this tutorial we will provide you with guided examples for identification of key NOEs for a two-state eNOE calculation. Keep in mind that listed approach can be generalized to any other type of calculation.

In summary, our approach consists of following steps:
# We extract all long range NOEs
# For each long range NOE we run a two-state structure calculation missing this particular NOE
# We evaluate structure calculations in terms of target function and correlations (using PDBcor)

==== Software installation ====

This tutorial requires following software:
# CYANA
# Python3
# UCSF Chimera
# PDBcor. In case if PDBcor is not yet installed, please go to this [https://github.com/dzmitryashkinadze/PDBCor link] and follow installation instructions. Later we will refer to the PDBcor installation path as '''PATH/To/PDBcor'''

==== Data preparation ====

Please follow the following steps:
# Download the [[Media:KeyNOE_demo.zip|demo data]].
# Unpack the demo data

==== Execution ====

We recommend to use parallel computation for the execution as it will significantly reduce the total running time.

Please follow the following steps carefully (exact Linux commands are given below; you may copy them to a terminal):

First, edit the protein specific data in the folder '''data/'''. Make sure, that filenames are kept the same as in the demo. Advanced users can ignore it and fix PREP.cya for potential naming errors (keep in mind that RUN.cya will modify the distance restraints to '''filtered.upl''' and '''filtered.lol''' by removing a particular selected long distance NOE). Change the protein sequence in the root folder (same filename is not required).

Additionally, do following checks:

Check that '''init.cya''' and '''CALC.cya''' have '''rmsdrange''' variable (range to calculate RMSD of the protein bundle) set to correct value

Check that '''SPLIT.cya''' has '''nres''' variable (number of residues) set to correct value

Then, test the '''data/''' folder by running a test calculation (we will create filtered restraints as in input only for test purposes):
cp -r data test
cd test
mv demo.upl filtered.upl
mv demo.lol filtered.upl
cyana PREP.cya
cyana -n 20 CALC.cya '''#cyana CALC.cya if no parallel computing is available'''

Check that both PREP and CALC scripts run without errors.

Check in program Chimera that resulting structure '''bundle.pdb''' is correct.

Check that split structure '''splitall.pdb''' is split correct (each conformer 1-nres is a separate PDB model).

Then, edit RUN.cya:

Edit upl and lol filenames
read upl data/demo.upl
read lol data/demo.lol

Edit the cyana engine
cyana:=cyana (do it only if you are sure what it is)

If you do not have an access to parallel computation change the line of RUN.cya from
system "pwd; ls -l CALC.cya; $cyana -n 20 CALC inputseed=$seed"
to:
system "pwd; ls -l CALC.cya; $cyana CALC inputseed=$seed"

Then, run the series of two-state structure calculations:
cyana RUN.cya

This creates a folder for each two-state structure calculation. In the calculation folder omitted NOE restraint is saved as a '''omitedrestraint.txt''' file and new restraint files are saved as '''filtered.upl''' and '''filtered.lol'''. Final structure is saved as bundle.pdb and final split structure (where each state is an independent model is saved as splitall.pdb).

If you do not have an access to parallel computation change the line of ANALYSE.cya from
system "bsub python '''PATH/To/PDBcor'''/correlationExtraction.py $dir/splitall.pdb --therm_iter=5"
to:
system "python '''PATH/To/PDBcor'''/correlationExtraction.py $dir/splitall.pdb --therm_iter=5"

Then, activate the PDBcor environment and run the correlation analysis:
source '''PATH/To/PDBcor'''/venv/bin/activate
cyana ANALYSE.cya

This creates a '''correlations/''' subfolder in each structure calculation folder with a correlation value that can be read from the '''correlations_backbone.txt''' file.

Finally, collect results in a single file:
cyana STAT_COLLECT.cya

This collects all correlation and target function values of all executed calculations in a single file using bash script '''extract_cor_value.sh''' into the output file '''results.txt''' and then sorts them according to the target function ('''results_tf.txt''') and correlations ('''results_cor.txt''').

Determination of the protein state populations

2021-09-07T11:30:58Z

Dima: /* Execution */

In this tutorial we will provide you with guided examples for determination of protein state populations.

In summary, our approach consists of following steps:
# We conduct a series of 10-state structure calculations with varying population parameter
# We evaluate structure calculations in terms of target function and correlations (using PDBcor)

==== Software installation ====

This tutorial requires following software:
# CYANA
# Python3
# UCSF Chimera
# PDBcor. In case if PDBcor is not yet installed, please go to this [https://github.com/dzmitryashkinadze/PDBCor link] and follow installation instructions. Later we will refer to the PDBcor installation path as '''PATH/To/PDBcor'''

==== Data preparation ====

Please follow the following steps:
# Download the [[Media:Pop_demo.zip|demo data]].
# Unpack the demo data

==== Execution ====

We recommend to use parallel computation for the execution as it will significantly reduce the total running time.

Please follow the following steps carefully (exact Linux commands are given below; you may copy them to a terminal):

First, edit the protein specific data in the folder '''data/'''. Make sure, that filenames are kept the same as in the demo. Advanced users can ignore it and fix PREPpop.cya for potential naming errors. Change the protein sequence in the root folder (same filename is not required).

Additionally, do following checks:

Check that '''init.cya''' and '''CALC.cya''' have '''rmsdrange''' variable (range to calculate RMSD of the protein bundle) set to correct value

Check that '''SPLIT.cya''' has '''nres''' variable (number of residues) set to correct value

Then, test the '''data/''' folder by running a test calculation:
cp -r data test
cd test
cyana PREPpop.cya populations=5,5
cyana -n 20 CALC.cya '''#cyana CALC.cya if no parallel computing is available'''

Check that both PREP and CALC scripts run without errors.

Check in program Chimera that resulting structure '''bundle.pdb''' is correct.

Check that split structure '''splitall.pdb''' is split correct (each conformer 1-nres is a separate PDB model).

Then, edit RUN.cya:

Edit the cyana engine
cyana:=cyana (do it only if you are sure what it is)

If you do not have an access to parallel computation change the line of RUN.cya from
system "pwd; ls -l CALC.cya; $cyana -n 20 CALC inputseed=$seed"
to:
system "pwd; ls -l CALC.cya; $cyana CALC inputseed=$seed"

Then, run the series of two-state structure calculations:
cyana RUN.cya

This creates a folder for each 10-state structure calculation. Final structure is saved as bundle.pdb and final split structure (where each state is an independent model is saved as splitall.pdb).

If you do not have an access to parallel computation change the line of ANALYSE.cya from
system "bsub python '''PATH/To/PDBcor'''/correlationExtraction.py $dir/splitall.pdb --therm_iter=5"
to:
system "python '''PATH/To/PDBcor'''/correlationExtraction.py $dir/splitall.pdb --therm_iter=5"

Then, activate the PDBcor environment and run the correlation analysis:
source '''PATH/To/PDBcor'''/venv/bin/activate
cyana ANALYSE.cya

This creates a '''correlations/''' subfolder in each structure calculation folder with a correlation value that can be read from the '''correlations_backbone.txt''' file.

Finally, collect results in a single file:
cyana STAT_COLLECT.cya

This collects all correlation and target function values of all executed calculations in a single file using bash script '''extract_cor_value.sh''' into the output file '''results.txt'''.

Determination of the protein number of states

2021-09-06T17:38:19Z

Dima: Created page with "In this tutorial we will provide you with guided examples for determination of the protein number of states. In summary, our approach consists of following steps: # We condu..."

In this tutorial we will provide you with guided examples for determination of the protein number of states.

In summary, our approach consists of following steps:
# We conduct a series of structure calculations with 1-9 states
# We evaluate structure calculations in terms of target function and correlations (using PDBcor)

==== Software installation ====

This tutorial requires following software:
# CYANA
# Python3
# UCSF Chimera
# PDBcor. In case if PDBcor is not yet installed, please go to this [https://github.com/dzmitryashkinadze/PDBCor link] and follow installation instructions. Later we will refer to the PDBcor installation path as '''PATH/To/PDBcor'''

==== Data preparation ====

Please follow the following steps:
# Download the [[Media:Pop_demo.zip|demo data]].
# Unpack the demo data

==== Execution ====

We recommend to use parallel computation for the execution as it will significantly reduce the total running time.

Please follow the following steps carefully (exact Linux commands are given below; you may copy them to a terminal):

First, edit the protein specific data in the folder '''data/'''. Make sure, that filenames are kept the same as in the demo. Advanced users can ignore it and fix PREP.cya for potential naming errors. Change the protein sequence in the root folder (same filename is not required).

Additionally, do following checks:

Check that '''init.cya''' and '''CALC.cya''' have '''rmsdrange''' variable (range to calculate RMSD of the protein bundle) set to correct value

Check that '''SPLIT.cya''' has '''nres''' variable (number of residues) set to correct value

Then, test the '''data/''' folder by running a test calculation:
cp -r data test
cd test
cyana PREP.cya nbundle=3
cyana -n 20 CALC.cya '''#cyana CALC.cya if no parallel computing is available'''

Check that both PREP and CALC scripts run without errors.

Check in program Chimera that resulting structure '''bundle.pdb''' is correct.

Check that split structure '''splitall.pdb''' is split correct (each conformer 1-nres is a separate PDB model).

Then, edit RUN.cya:

Edit the cyana engine
cyana:=cyana (do it only if you are sure what it is)

If you do not have an access to parallel computation change the line of RUN.cya from
system "pwd; ls -l CALC.cya; $cyana -n 20 CALC inputseed=$seed"
to:
system "pwd; ls -l CALC.cya; $cyana CALC inputseed=$seed"

Then, run the series of two-state structure calculations:
cyana RUN.cya

This creates a folder for each multi-state structure calculation. Final structure is saved as bundle.pdb and final split structure (where each state is an independent model is saved as splitall.pdb).

If you do not have an access to parallel computation change the line of ANALYSE.cya from
system "bsub python ~/Python/allosteryExtraction/correlationExtraction.py $dir/splitall.pdb --nstates=$ns --therm_iter=5"
to:
system "python ~/Python/allosteryExtraction/correlationExtraction.py $dir/splitall.pdb --nstates=$ns --therm_iter=5"

Then, activate the PDBcor environment and run the correlation analysis:
source '''PATH/To/PDBcor'''/venv/bin/activate
cyana ANALYSE.cya

This creates a '''correlations/''' subfolder in each structure calculation folder with a correlation value that can be read from the '''correlations_backbone.txt''' file.

Since all structure calculations were done for different number of states their target functions are not directly comparable. We need first to normalize them.

For this execute bash script
sh normalize_tf.sh # it usually takes couple of minutes

Once the script is finished run it again and save the output
sh normalize_tf.sh > TF.txt

This file contains normalized target functions

Finally, collect results in a single file:
cyana STAT_COLLECT.cya

This collects all correlation and target function values of all executed calculations in a single file using bash script '''extract_cor_value.sh''' into the output file '''results.txt'''.

Determination of the protein state populations

2021-09-06T17:32:22Z

Dima:

In this tutorial we will provide you with guided examples for determination of protein state populations.

In summary, our approach consists of following steps:
# We conduct a series of 10-state structure calculations with varying population parameter
# We evaluate structure calculations in terms of target function and correlations (using PDBcor)

==== Software installation ====

This tutorial requires following software:
# CYANA
# Python3
# UCSF Chimera
# PDBcor. In case if PDBcor is not yet installed, please go to this [https://github.com/dzmitryashkinadze/PDBCor link] and follow installation instructions. Later we will refer to the PDBcor installation path as '''PATH/To/PDBcor'''

==== Data preparation ====

Please follow the following steps:
# Download the [[Media:Pop_demo.zip|demo data]].
# Unpack the demo data

==== Execution ====

We recommend to use parallel computation for the execution as it will significantly reduce the total running time.

Please follow the following steps carefully (exact Linux commands are given below; you may copy them to a terminal):

First, edit the protein specific data in the folder '''data/'''. Make sure, that filenames are kept the same as in the demo. Advanced users can ignore it and fix PREPpop.cya for potential naming errors. Change the protein sequence in the root folder (same filename is not required).

Additionally, do following checks:

Check that '''init.cya''' and '''CALC.cya''' have '''rmsdrange''' variable (range to calculate RMSD of the protein bundle) set to correct value

Check that '''SPLIT.cya''' has '''nres''' variable (number of residues) set to correct value

Then, test the '''data/''' folder by running a test calculation:
cp -r data test
cd test
cyana PREPpop.cya populations=5,5
cyana -n 20 CALC.cya '''#cyana CALC.cya if no parallel computing is available'''

Check that both PREP and CALC scripts run without errors.

Check in program Chimera that resulting structure '''bundle.pdb''' is correct.

Check that split structure '''splitall.pdb''' is split correct (each conformer 1-nres is a separate PDB model).

Then, edit RUN.cya:

Edit the cyana engine
cyana:=cyana (do it only if you are sure what it is)

If you do not have an access to parallel computation change the line of RUN.cya from
system "pwd; ls -l CALC.cya; $cyana -n 20 CALC inputseed=$seed"
to:
system "pwd; ls -l CALC.cya; $cyana CALC inputseed=$seed"

Then, run the series of two-state structure calculations:
cyana RUN.cya

This creates a folder for each 10-state structure calculation. Final structure is saved as bundle.pdb and final split structure (where each state is an independent model is saved as splitall.pdb).

If you do not have an access to parallel computation change the line of ANALYSE.cya from
system "bsub python ~/Python/allosteryExtraction/correlationExtraction.py $dir/splitall.pdb --therm_iter=5"
to:
system "python ~/Python/allosteryExtraction/correlationExtraction.py $dir/splitall.pdb --therm_iter=5"

Then, activate the PDBcor environment and run the correlation analysis:
source '''PATH/To/PDBcor'''/venv/bin/activate
cyana ANALYSE.cya

This creates a '''correlations/''' subfolder in each structure calculation folder with a correlation value that can be read from the '''correlations_backbone.txt''' file.

Finally, collect results in a single file:
cyana STAT_COLLECT.cya

This collects all correlation and target function values of all executed calculations in a single file using bash script '''extract_cor_value.sh''' into the output file '''results.txt'''.

Determination of the protein state populations

2021-09-06T17:31:59Z

Dima:

In this tutorial we will provide you with guided examples for determination of protein state populations.

In summary, our approach consists of following steps:
# We conduct a series of 10-state structure calculations with varying population parameter
# We evaluate structure calculations in terms of target function and correlations (using PDBcor)

==== Software installation ====

This tutorial requires following software:
# CYANA
# Python3
# UCSF Chimera
# PDBcor. In case if PDBcor is not yet installed, please go to this [https://github.com/dzmitryashkinadze/PDBCor link] and follow installation instructions. Later we will refer to the PDBcor installation path as '''PATH/To/PDBcor'''

==== Data preparation ====

Please follow the following steps:
# Download the [[Media:Pop_demo.zip|demo data]].
# Unpack the demo data

==== Execution ====

We recommend to use parallel computation for the execution as it will significantly reduce the total running time.

Please follow the following steps carefully (exact Linux commands are given below; you may copy them to a terminal):

First, edit the protein specific data in the folder '''data/'''. Make sure, that filenames are kept the same as in the demo. Advanced users can ignore it and fix PREP.cya for potential naming errors. Change the protein sequence in the root folder (same filename is not required).

Additionally, do following checks:

Check that '''init.cya''' and '''CALC.cya''' have '''rmsdrange''' variable (range to calculate RMSD of the protein bundle) set to correct value

Check that '''SPLIT.cya''' has '''nres''' variable (number of residues) set to correct value

Then, test the '''data/''' folder by running a test calculation:
cp -r data test
cd test
cyana PREPpop.cya populations=5,5
cyana -n 20 CALC.cya '''#cyana CALC.cya if no parallel computing is available'''

Check that both PREP and CALC scripts run without errors.

Check in program Chimera that resulting structure '''bundle.pdb''' is correct.

Check that split structure '''splitall.pdb''' is split correct (each conformer 1-nres is a separate PDB model).

Then, edit RUN.cya:

Edit the cyana engine
cyana:=cyana (do it only if you are sure what it is)

If you do not have an access to parallel computation change the line of RUN.cya from
system "pwd; ls -l CALC.cya; $cyana -n 20 CALC inputseed=$seed"
to:
system "pwd; ls -l CALC.cya; $cyana CALC inputseed=$seed"

Then, run the series of two-state structure calculations:
cyana RUN.cya

This creates a folder for each 10-state structure calculation. Final structure is saved as bundle.pdb and final split structure (where each state is an independent model is saved as splitall.pdb).

If you do not have an access to parallel computation change the line of ANALYSE.cya from
system "bsub python ~/Python/allosteryExtraction/correlationExtraction.py $dir/splitall.pdb --therm_iter=5"
to:
system "python ~/Python/allosteryExtraction/correlationExtraction.py $dir/splitall.pdb --therm_iter=5"

Then, activate the PDBcor environment and run the correlation analysis:
source '''PATH/To/PDBcor'''/venv/bin/activate
cyana ANALYSE.cya

This creates a '''correlations/''' subfolder in each structure calculation folder with a correlation value that can be read from the '''correlations_backbone.txt''' file.

Finally, collect results in a single file:
cyana STAT_COLLECT.cya

This collects all correlation and target function values of all executed calculations in a single file using bash script '''extract_cor_value.sh''' into the output file '''results.txt'''.

Tutorials

2021-09-06T17:30:42Z

Dima: /* CYANA example calculations */

== Downloads ==

* Latest [[Media:cyana-3.98.13bin-210120Demo.tgz‎|demo version of CYANA 3.98.13]] for Linux and MacOS (20.01.2021)<br>The demo version has the full functionality of the program for the protein sequences that are used in the example calculations.
* On certain Mac systems, you must execute the command 'xattr -r -d com.apple.quarantine cyana-3.98.13' after unpacking to allow execution of the programs.

== INCLAN Tutorials ==

* [[Writing and using INCLAN macros]]
* [[Using INCLAN variables]]
* [[Using INCLAN control statements]]

== CYANA Tutorials ==

* [[Defining non-standard residues]]


== CYANA example calculations ==

* [[Basic structure calculation starting from given restraints]]
* [[Structure calculation using manually assigned NOESY peak lists]]
* [[Structure calculation with automated NOESY assignment]]
* [[Homodimer structure calculation with automated NOESY assignment]]
* [[ENORA and multi-state structure calculations]]
* [[Identification of key NOEs]]
* [[Determination of the protein state populations]]
* [[Determination of the protein number of states]]


== Courses ==

=== EMBO Practical Course: Structure, dynamics and function of biomacromolecules by NMR 2019 ===

Garching bei München, 26 July - 2 August 2019 ([http://embo2019.bnmrz.org/ course homepage])

* [[Structure calculation and automated NOESY assignment with CYANA (EMBO 2019)|Structure calculation and automated NOESY assignment with CYANA]]
* [[Automated resonance assignment with FLYA (EMBO 2019)|Automated resonance assignment with FLYA]]



== Input file formats ==

<div style="column-count:2;-moz-column-count:2;-webkit-column-count:2">
* [[Residue library file]] (.lib)
* [[Sequence file]] (.seq)
* [[Distance restraint file]] (.upl, .lol)
* [[Torsion angle restraint file]] (.aco)
* [[Residual dipolar coupling restraint file]] (.rdc)
* [[Pseudocontact shift restraint file]] (.pcs)
* [[DG Cartesian coordinate file]] (.cor)
* [[PDB coordinate file]] (.pdb)
* [[Torsion angle file]] (.ang)
* [[XEASY chemical shift list file]] (.prot)
* [[BMRB chemical shift list file]] (.bmrb)
* [[XEASY peak list file]] (.peaks)
* [[NMRView peak list file]] (.xpk)
</div>

Determination of protein state populations

2021-09-06T17:29:44Z

Dima: moved Determination of protein state populations to Determination of the protein state populations: Spelling

#REDIRECT [[Determination of the protein state populations]]

Determination of the protein state populations

2021-09-06T17:29:44Z

Dima: moved Determination of protein state populations to Determination of the protein state populations: Spelling

In this tutorial we will provide you with guided examples for determination of protein state populations.

In summary, our approach consists of following steps:
# We conduct a series of 10-state structure calculations with varying population parameter
# We evaluate structure calculations in terms of target function and correlations (using PDBcor)

==== Software installation ====

This tutorial requires following software:
# CYANA
# Python3
# UCSF Chimera
# PDBcor. In case if PDBcor is not yet installed, please go to this [https://github.com/dzmitryashkinadze/PDBCor link] and follow installation instructions. Later we will refer to the PDBcor installation path as '''PATH/To/PDBcor'''

==== Data preparation ====

Please follow the following steps:
# Download the [[Media:Pop_demo.zip|demo data]].
# Unpack the demo data

==== Execution ====

We recommend to use parallel computation for the execution as it will significantly reduce the total running time.

Please follow the following steps carefully (exact Linux commands are given below; you may copy them to a terminal):

First, edit the protein specific data in the folder '''data/'''. Make sure, that filenames are kept the same as in the demo. Advanced users can ignore it and fix PREPpop.cya for potential naming errors. Change the protein sequence in the root folder (same filename is not required).

Additionally, do following checks:

Check that '''init.cya''' and '''CALC.cya''' have '''rmsdrange''' variable (range to calculate RMSD of the protein bundle) set to correct value

Check that '''SPLIT.cya''' has '''nres''' variable (number of residues) set to correct value

Then, test the '''data/''' folder by running a test calculation:
cp -r data test
cd test
cyana PREPpop.cya populations=5,5
cyana -n 20 CALC.cya '''#cyana CALC.cya if no parallel computing is available'''

Check that both PREP and CALC scripts run without errors.

Check in program Chimera that resulting structure '''bundle.pdb''' is correct.

Check that split structure '''splitall.pdb''' is split correct (each conformer 1-nres is a separate PDB model).

Then, edit RUN.cya:

Edit the cyana engine
cyana:=cyana (do it only if you are sure what it is)

If you do not have an access to parallel computation change the line of RUN.cya from
system "pwd; ls -l CALC.cya; $cyana -n 20 CALC inputseed=$seed"
to:
system "pwd; ls -l CALC.cya; $cyana CALC inputseed=$seed"

Then, run the series of two-state structure calculations:
cyana RUN.cya

This creates a folder for each 10-state structure calculation. Final structure is saved as bundle.pdb and final split structure (where each state is an independent model is saved as splitall.pdb).

If you do not have an access to parallel computation change the line of ANALYSE.cya from
system "bsub python ~/Python/allosteryExtraction/correlationExtraction.py $dir/splitall.pdb --therm_iter=5"
to:
system "python ~/Python/allosteryExtraction/correlationExtraction.py $dir/splitall.pdb --therm_iter=5"

Then, activate the PDBcor environment and run the correlation analysis:
source '''PATH/To/PDBcor'''/venv/bin/activate
cyana ANALYSE.cya

This creates a '''correlations/''' subfolder in each structure calculation folder with a correlation value that can be read from the '''correlations_backbone.txt''' file.

Finally, collect results in a single file:
cyana STAT_COLLECT.cya

This collects all correlation and target function values of all executed calculations in a single file using bash script '''extract_cor_value.sh''' into the output file '''results.txt'''.

Tutorials

2021-09-06T17:29:06Z

Dima: /* CYANA example calculations */

== Downloads ==

* Latest [[Media:cyana-3.98.13bin-210120Demo.tgz‎|demo version of CYANA 3.98.13]] for Linux and MacOS (20.01.2021)<br>The demo version has the full functionality of the program for the protein sequences that are used in the example calculations.
* On certain Mac systems, you must execute the command 'xattr -r -d com.apple.quarantine cyana-3.98.13' after unpacking to allow execution of the programs.

== INCLAN Tutorials ==

* [[Writing and using INCLAN macros]]
* [[Using INCLAN variables]]
* [[Using INCLAN control statements]]

== CYANA Tutorials ==

* [[Defining non-standard residues]]


== CYANA example calculations ==

* [[Basic structure calculation starting from given restraints]]
* [[Structure calculation using manually assigned NOESY peak lists]]
* [[Structure calculation with automated NOESY assignment]]
* [[Homodimer structure calculation with automated NOESY assignment]]
* [[ENORA and multi-state structure calculations]]
* [[Identification of key NOEs]]
* [[Determination of protein state populations]]
* [[Determination of the protein number of states]]


== Courses ==

=== EMBO Practical Course: Structure, dynamics and function of biomacromolecules by NMR 2019 ===

Garching bei München, 26 July - 2 August 2019 ([http://embo2019.bnmrz.org/ course homepage])

* [[Structure calculation and automated NOESY assignment with CYANA (EMBO 2019)|Structure calculation and automated NOESY assignment with CYANA]]
* [[Automated resonance assignment with FLYA (EMBO 2019)|Automated resonance assignment with FLYA]]



== Input file formats ==

<div style="column-count:2;-moz-column-count:2;-webkit-column-count:2">
* [[Residue library file]] (.lib)
* [[Sequence file]] (.seq)
* [[Distance restraint file]] (.upl, .lol)
* [[Torsion angle restraint file]] (.aco)
* [[Residual dipolar coupling restraint file]] (.rdc)
* [[Pseudocontact shift restraint file]] (.pcs)
* [[DG Cartesian coordinate file]] (.cor)
* [[PDB coordinate file]] (.pdb)
* [[Torsion angle file]] (.ang)
* [[XEASY chemical shift list file]] (.prot)
* [[BMRB chemical shift list file]] (.bmrb)
* [[XEASY peak list file]] (.peaks)
* [[NMRView peak list file]] (.xpk)
</div>

2021-09-06T11:09:21Z

Dima: /* Execution */

In this tutorial we will provide you with guided examples for identification of key NOEs for a two-state eNOE calculation. Keep in mind that listed approach can be generalized to any other type of calculation.

In summary, our approach consists of following steps:
# We extract all long range NOEs
# For each long range NOE we run a two-state structure calculation missing this particular NOE
# We evaluate structure calculations in terms of correlations (using PDBcor)

==== Software installation ====

This tutorial requires following software:
# CYANA
# Python3
# PDBcor. In case if PDBcor is not yet installed, please go to this [https://github.com/dzmitryashkinadze/PDBCor link] and follow installation instructions. Later we will refer to the PDBcor installation path as '''PATH/To/PDBcor'''

==== Data preparation ====

Please follow the following steps:
# Download the [[Media:KeyNOE_demo.zip|demo data]].
# Unpack the demo data

==== Execution ====

We recommend to use parallel computation for the execution as it will significantly reduce the total running time.

Please follow the following steps carefully (exact Linux commands are given below; you may copy them to a terminal):

First, edit the protein specific data in the folder '''data/'''. Make sure, that filenames are kept the same as in the demo. Advanced users can ignore it and fix PREP.cya for potential naming errors (keep in mind that RUN.cya will modify the distance restraints to '''filtered.upl''' and '''filtered.lol''' by removing a particular selected long distance NOE). Change the protein sequence in the root folder (same filename is not required).

Then, test the '''data''' folder by running a test calculation (we will create filtered restraints as in input only for test purposes):
cp -r data test
cd test
mv demo.upl filtered.upl
mv demo.lol filtered.upl
cyana PREP.cya
cyana -n 20 CALC.cya # cyana CALC.cya if no parallel computing is available

If both PREP and CALC scripts run without errors then proceed, otherwise fix errors.

Then, edit RUN.cya:
Edit upl and lol filenames
read upl data/demo.upl
read lol data/demo.lol
Edit the cyana engine
cyana:=cyana (do it only if you are sure what it is)

If you do not have an access to parallel computation change the line of RUN.cya from
system "pwd; ls -l CALC.cya; $cyana -n 20 CALC inputseed=$seed"
to:
system "pwd; ls -l CALC.cya; $cyana CALC inputseed=$seed"

Then, run the series of two-state structure calculations:
cyana RUN.cya

This creates a folder for each two-state structure calculation. In the calculation folder omitted NOE restraint is saved as a '''omitedrestraint.txt''' file and new restraint files are saved as '''filtered.upl''' and '''filtered.lol'''. Final structure is saved as bundle.pdb and final split structure (where each state is an independent model is saved as splitall.pdb).

If you do not have an access to parallel computation change the line of ANALYSE.cya from
system "bsub python ~/Python/allosteryExtraction/correlationExtraction.py $dir/splitall.pdb --therm_iter=5"
to:
system "python ~/Python/allosteryExtraction/correlationExtraction.py $dir/splitall.pdb --therm_iter=5"

Then, activate the PDBcor environment and run the correlation analysis:
source '''PATH/To/PDBcor'''/venv/bin/activate
cyana ANALYSE.cya

This creates a '''correlation/''' subfolder in each structure calculation folder with a correlation value that can be read from the '''correlations_backbone.txt''' file.

Finally, collect results in a single file ('''results.txt'''):
cyana STAT_COLLECT.cya

This collects all correlation and target function values of all executed calculations in a single file using bash script '''extract_cor_value.sh'''.