DISI - User contributions [en]

FEP+ for GPCR

2021-03-09T07:16:22Z

Chasemwebb:

2/25/2021 Ying Yang
3/08/2021 Chase Webb

Steps for setting up a FEP prediction for membrane protein
[[File:workflow_FEP.png|thumb|center|550px]]

== Protein side ==
* Its always good practice to specify a relevant working directory and save the project. Maestro is prone to crashing randomly, especially when remotely accessing the license. Wouldn't want to lose all of your hard work.

* Begin by importing the structure of interest with a ligand that you are confident about. (FEP works best with an experimentally determined structure but can work with a docked pose if you are careful).

* Run the protein preparation wizard on your protein. Think carefully about protonation, you might even consider using the interactive protonation function to optimize the protonation state of the binding site residues or allosteric control points manually.

'''Protein model completeness'''
Protein preparation should include fixing any chain breaks, modeling in any loop conformations and adding any missing side chains. Chain breaks near the active site will likely lead to poor results. Disulfide bridges should be created and termini residues capped where applicable.

=== Equilibration of complex structure (with confident binding pose) ===

* Build membrane using the '''System Builder''' tool. Use residue information obtained from OPM database (https://opm.phar.umich.edu/), add salts, add solvent. Default settings work well for most situations.
res.num 76-97,112-136,141,143,146-171,194-215,227-229,231-256,323-345,347,360-380,382,398

* Use the '''Molecular Dynamics''' tool to write out a simulation file to run on the cluster. Load the system you built in the previous step from the workspace. Specify a simulation time, for high confidence complexes, 20 ns should suffice. For more exploratory complexes and to assess stability of the ligand extend to 50-100 ns. Use the default Schrodinger protocol to relax the model system before starting the production run of the simulation. For ease of evaluation, you can check the box to output interaction analysis at the completion of the job. Instead of clicking run, click the gear to the left of the run button and write the file out to the disk.

* Replace the gimel-biggpu in the written out simulation job with the correct gpu location, gimel5.heavygpu then transfer (scp) the simulation job to gimel5, and submit

sed -i 's/gimel-biggpu/gimel5.heavygpu/g' desmond_md_job_1.sh
bash desmond_md_job_1.sh

* Analyze the MD simulation using the '''Simulation Interactions Diagram''' tool from the TASKS menu. Import the -out.cms file from the simulation to analyze, it takes 5-10 minutes to load. If you are satisfied with the simulation as evidenced by things such as stability of protein/ligand, proceed. If not, potentially try another docked pose or run the simulation for a longer time.

Visualize the trajectory and analyze the simulation with SID tool
[[File:SID_analysis.png|thumb|center|350px]]

* Use the schrodinger command line tools to convert the last frame of the simulation -out.cms into .mae file.
$SCHRODINGER/run membrane_cms2fep.py -ligand 'ligand' 2A_NBOH_MD-out.cms -o relax_2A_NBOH_pv.mae

You have now prepared the receptor and the initial state for an FEP+ map. Marvel at your accomplishments.

== Useful Schrodinger Commands ==

* Kill a submitted or running job:
$SCHRODINGER/jobcontrol -kill <jobID>

== Ligand side ==

Careful preparation of the ligands is critical to a successful FEP+ prediction. Best practices include running LigPrep on all the compounds to exhaustively enumerate all the stereoisomers and likely protonation states of the ligands. Note that basic tertiary amines cannot invert their stereogenic centers during the simulation, making it important to model both protomers.

* Import the ligands into the workspace, recommend that you import smiles or a mol2 file. Use the '''LigPrep''' tool to build the ligands into a 3D dockable format. Consider generating a good amount of stereoisomers if you are unsure about the sterochemistry or you only have relative stereochemical information. Generate the relevant protomers at the relevant pH.

* Build a grid for docking from the .mae file you generated above using the '''Receptor Grid Generation''' tool.

* Simultaneously as above, use '''Force field builder''' to add potential energy functions for torsions that are missing from the Schrodinger catalog but present in your ligands. This is a time consuming step so do this as soon as you know which ligands you want to run FEP on.
Run force field builder for all ligands

* Flexible ligand alignment OR core constrain docking [when in doubt, always just do core constrained docking]
Depends on how similar/different are the ligands to the reference/center ligand

* Create FEP maps

* Write out the submission file; change host; submit on gimel5 via slurm

FEP+ for GPCR

2021-03-09T03:15:34Z

Chasemwebb:

2/25/2021 Ying Yang

Steps for setting up a FEP prediction for membrane protein
[[File:workflow_FEP.png|thumb|center|550px]]

== Protein side ==
* Its always good practice to specify a relevant working directory and save the project. Maestro is prone to crashing randomly, especially when remotely accessing the license. Wouldn't want to lose all of your hard work.

* Begin by importing the structure of interest with a ligand that you are confident about. (FEP works best with an experimentally determined structure but can work with a docked pose if you are careful).

* Run the protein preparation wizard on your protein. Think carefully about protonation, you might even consider using the interactive protonation function to optimize the protonation state of the binding site residues or allosteric control points manually.

'''Protein model completeness'''
Protein preparation should include fixing any chain breaks, modeling in any loop conformations and adding any missing side chains. Chain breaks near the active site will likely lead to poor results. Disulfide bridges should be created and termini residues capped where applicable.

=== Equilibration of complex structure (with confident binding pose) ===

* Build membrane using the '''System Builder''' tool. Use residue information obtained from OPM database (https://opm.phar.umich.edu/), add salts, add solvent. Default settings work well for most situations.
res.num 76-97,112-136,141,143,146-171,194-215,227-229,231-256,323-345,347,360-380,382,398

* Use the '''Molecular Dynamics''' tool to write out a simulation file to run on the cluster. Load the system you built in the previous step from the workspace. Specify a simulation time, for high confidence complexes, 20 ns should suffice. For more exploratory complexes and to assess stability of the ligand extend to 50-100 ns. Use the default Schrodinger protocol to relax the model system before starting the production run of the simulation. For ease of evaluation, you can check the box to output interaction analysis at the completion of the job. Instead of clicking run, click the gear to the left of the run button and write the file out to the disk.

* Replace the gimel-biggpu in the written out simulation job with the correct gpu location, gimel5.heavygpu then transfer (scp) the simulation job to gimel5, and submit

sed -i 's/gimel-biggpu/gimel5.heavygpu/g' desmond_md_job_1.sh
bash desmond_md_job_1.sh

* Analyze the MD simulation using the '''Simulation Interactions Diagram''' tool from the TASKS menu. Import the -out.cms file from the simulation to analyze, it takes 5-10 minutes to load. If you are satisfied with the simulation as evidenced by things such as stability of protein/ligand, proceed. If not, potentially try another docked pose or run the simulation for a longer time.

Visualize the trajectory and analyze the simulation with SID tool
[[File:SID_analysis.png|thumb|center|350px]]

* Use the schrodinger command line tools to convert the last frame of the simulation -out.cms into .mae file.
$SCHRODINGER/run membrane_cms2fep.py -ligand 'ligand' 2A_NBOH_MD-out.cms -o relax_2A_NBOH_pv.mae

You have now prepared the receptor and the initial state for an FEP+ map. Marvel at your accomplishments.

== Useful Schrodinger Commands ==

* Kill a submitted or running job:
$SCHRODINGER/jobcontrol -kill <jobID>

== Ligand side ==

Careful preparation of the ligands is critical to a successful FEP+ prediction. Best practices include running LigPrep on all the compounds to exhaustively enumerate all the stereoisomers and likely protonation states of the ligands. Note that tertiary amines cannot invert stereochemistry during the simulation, making it important to model both pseudo-stereoisomers. Note: This is especially important for basic amines that are protonated. Make sure to model the proton on both sides!

* Import the ligands into the workspace, recommend that you import smiles or a mol2 file. Use the '''LigPrep''' tool to build the ligands into a 3D dockable format. Consider generating a good amount of stereoisomers if you are unsure about the sterochemistry or you only have relative stereochemical information. Generate the relevant protomers at the relevant pH.

* Build a grid for docking from the .mae file you generated above using the '''Receptor Grid Generation''' tool.

* Simultaneously as above, use '''Force field builder''' to add potential energy functions for torsions that are missing from the Schrodinger catalog but present in your ligands. This is a time consuming step so do this as soon as you know which ligands you want to run FEP on.
Run force field builder for all ligands

* Flexible ligand alignment OR core constrain docking [when in doubt, always just do core constrained docking]
Depends on how similar/different are the ligands to the reference/center ligand

* Create FEP maps

* Write out the submission file; change host; submit on gimel5 via slurm

FEP+ for GPCR

2021-03-09T03:09:57Z

Chasemwebb:

2/25/2021 Ying Yang

Steps for setting up a FEP prediction for membrane protein
[[File:workflow_FEP.png|thumb|center|550px]]

== Protein side ==
* Its always good practice to specify a relevant working directory and save the project. Maestro is prone to crashing randomly, especially when remotely accessing the license. Wouldn't want to lose all of your hard work.

* Begin by importing the structure of interest with a ligand that you are confident about. (FEP works best with an experimentally determined structure but can work with a docked pose if you are careful).

* Run the protein preparation wizard on your protein. Think carefully about protonation, you might even consider using the interactive protonation function to optimize the protonation state of the binding site residues or allosteric control points manually.

'''Protein model completeness'''
Protein preparation should include fixing any chain breaks, modeling in any loop conformations and adding any missing side chains. Chain breaks near the active site will likely lead to poor results. Disulfide bridges should be created and termini residues capped where applicable.

=== Equilibration of complex structure (with confident binding pose) ===

* Build membrane using the '''System Builder''' tool. Use residue information obtained from OPM database (https://opm.phar.umich.edu/), add salts, add solvent. Default settings work well for most situations.
res.num 76-97,112-136,141,143,146-171,194-215,227-229,231-256,323-345,347,360-380,382,398

* Use the '''Molecular Dynamics''' tool to write out a simulation file to run on the cluster. Load the system you built in the previous step from the workspace. Specify a simulation time, for high confidence complexes, 20 ns should suffice. For more exploratory complexes and to assess stability of the ligand extend to 50-100 ns. Use the default Schrodinger protocol to relax the model system before starting the production run of the simulation. For ease of evaluation, you can check the box to output interaction analysis at the completion of the job. Instead of clicking run, click the gear to the left of the run button and write the file out to the disk.

* Replace the gimel-biggpu in the written out simulation job with the correct gpu location, gimel5.heavygpu then transfer (scp) the simulation job to gimel5, and submit

sed -i 's/gimel-biggpu/gimel5.heavygpu/g' desmond_md_job_1.sh
bash desmond_md_job_1.sh

* Analyze the MD simulation using the '''Simulation Interactions Diagram''' tool from the TASKS menu. Import the -out.cms file from the simulation to analyze, it takes 5-10 minutes to load. If you are satisfied with the simulation as evidenced by things such as stability of protein/ligand, proceed. If not, potentially try another docked pose or run the simulation for a longer time.

Visualize the trajectory and analyze the simulation with SID tool
[[File:SID_analysis.png|thumb|center|350px]]

* Use the schrodinger command line tools to convert the last frame of the simulation -out.cms into .mae file.
$SCHRODINGER/run membrane_cms2fep.py -ligand 'ligand' 2A_NBOH_MD-out.cms -o relax_2A_NBOH_pv.mae

You have now prepared the receptor and the initial state for an FEP+ map. Marvel at your accomplishments.

== Useful Schrodinger Commands ==

* Kill a submitted or running job:
$SCHRODINGER/jobcontrol -kill <jobID>

== Ligand side ==

Careful preparation of the ligands is critical to a successful FEP+ prediction. Best practices include running LigPrep on all the compounds to exhaustively enumerate all the stereoisomers and likely protonation states of the ligands. Note that tertiary amines cannot invert stereochemistry during the simulation, making it important to model both pseudo-stereoisomers. Note: This is especially important for basic amines that are protonated. Make sure to model the proton on both sides!

* Import the ligands into the workspace, recommend that you import smiles or a mol2 file. Use the '''LigPrep''' tool to build the ligands into a 3D dockable format. Consider generating a good amount of stereoisomers if you are unsure about the sterochemistry or you only have relative stereochemical information. Generate the relevant protomers at the relevant pH.

* Force field builder
Run force field builder for all ligands

* Flexible ligand alignment OR core constrain docking
Depends on how similar/different are the ligands to the reference/center ligand

* Create FEP maps

* Write out the submission file; change host; submit on gimel5 via slurm

FEP+ for GPCR

2021-03-09T03:01:10Z

Chasemwebb:

2/25/2021 Ying Yang

Steps for setting up a FEP prediction for membrane protein
[[File:workflow_FEP.png|thumb|center|550px]]

== Protein side ==
* Its always good practice to specify a relevant working directory and save the project. Maestro is prone to crashing randomly, especially when remotely accessing the license. Wouldn't want to lose all of your hard work.

* Begin by importing the structure of interest with a ligand that you are confident about. (FEP works best with an experimentally determined structure but can work with a docked pose if you are careful).

* Run the protein preparation wizard on your protein. Think carefully about protonation, you might even consider using the interactive protonation function to optimize the protonation state of the binding site residues or allosteric control points manually.

'''Protein model completeness'''
Protein preparation should include fixing any chain breaks, modeling in any loop conformations and adding any missing side chains. Chain breaks near the active site will likely lead to poor results. Disulfide bridges should be created and termini residues capped where applicable.

=== Equilibration of complex structure (with confident binding pose) ===

* Build membrane using the '''System Builder''' tool. Use residue information obtained from OPM database (https://opm.phar.umich.edu/), add salts, add solvent. Default settings work well for most situations.
res.num 76-97,112-136,141,143,146-171,194-215,227-229,231-256,323-345,347,360-380,382,398

* Use the '''Molecular Dynamics''' tool to write out a simulation file to run on the cluster. Load the system you built in the previous step from the workspace. Specify a simulation time, for high confidence complexes, 20 ns should suffice. For more exploratory complexes and to assess stability of the ligand extend to 50-100 ns. Use the default Schrodinger protocol to relax the model system before starting the production run of the simulation. For ease of evaluation, you can check the box to output interaction analysis at the completion of the job. Instead of clicking run, click the gear to the left of the run button and write the file out to the disk.

* Replace the gimel-biggpu in the written out simulation job with the correct gpu location, gimel5.heavygpu then transfer (scp) the simulation job to gimel5, and submit

sed -i 's/gimel-biggpu/gimel5.heavygpu/g' desmond_md_job_1.sh
bash desmond_md_job_1.sh

* Analyze the MD simulation using the '''Simulation Interactions Diagram''' tool from the TASKS menu. Import the -out.cms file from the simulation to analyze, it takes 5-10 minutes to load. If you are satisfied with the simulation as evidenced by things such as stability of protein/ligand, proceed. If not, potentially try another docked pose or run the simulation for a longer time.

Visualize the trajectory and analyze the simulation with SID tool
[[File:SID_analysis.png|thumb|center|350px]]

* Use the schrodinger command line tools to convert the last frame of the simulation -out.cms into .mae file.
$SCHRODINGER/run membrane_cms2fep.py -ligand 'ligand' 2A_NBOH_MD-out.cms -o relax_2A_NBOH_pv.mae

You have now prepared the receptor and the initial state for an FEP+ map.

== Useful Schrodinger Commands ==

* Kill a submitted or running job:
$SCHRODINGER/jobcontrol -kill <jobID>

== Ligand side ==

Careful preparation of the ligands is critical to a successful FEP+ prediction. Best practices include running LigPrep on all the compounds to exhaustively enumerate all the stereoisomers and likely protonation states of the ligands. Note that triply-substituted ammonium cannot invert stereochemistry during the simulation, making it important to model both pseudo-stereoisomers.

* Force field builder
Run force field builder for all ligands

* Flexible ligand alignment OR core constrain docking
Depends on how similar/different are the ligands to the reference/center ligand

* Create FEP maps

* Write out the submission file; change host; submit on gimel5 via slurm

FEP+ for GPCR

2021-03-09T03:00:14Z

Chasemwebb:

2/25/2021 Ying Yang

Steps for setting up a FEP prediction for membrane protein
[[File:workflow_FEP.png|thumb|center|550px]]

== Protein side ==
* Its always good practice to specify a relevant working directory and save the project. Maestro is prone to crashing randomly, especially when remotely accessing the license. Wouldn't want to lose all of your hard work.

* Begin by importing the structure of interest with a ligand that you are confident about. (FEP works best with an experimentally determined structure but can work with a docked pose if you are careful).

* Run the protein preparation wizard on your protein. Think carefully about protonation, you might even consider using the interactive protonation function to optimize the protonation state of the binding site residues or allosteric control points manually.

'''Protein model completeness'''
Protein preparation should include fixing any chain breaks, modeling in any loop conformations and adding any missing side chains. Chain breaks near the active site will likely lead to poor results. Disulfide bridges should be created and termini residues capped where applicable.

=== Equilibration of complex structure (with confident binding pose) ===

* Build membrane using the '''System Builder''' tool. Use residue information obtained from OPM database (https://opm.phar.umich.edu/), add salts, add solvent. Default settings work well for most situations.
res.num 76-97,112-136,141,143,146-171,194-215,227-229,231-256,323-345,347,360-380,382,398

* Use the '''Molecular Dynamics''' tool to write out a simulation file to run on the cluster. Load the system you built in the previous step from the workspace. Specify a simulation time, for high confidence complexes, 20 ns should suffice. For more exploratory complexes and to assess stability of the ligand extend to 50-100 ns. Use the default Schrodinger protocol to relax the model system before starting the production run of the simulation. For ease of evaluation, you can check the box to output interaction analysis at the completion of the job. Instead of clicking run, click the gear to the left of the run button and write the file out to the disk.

* Replace the gimel-biggpu in the written out simulation job with the correct gpu location, gimel5.heavygpu then transfer (scp) the simulation job to gimel5, and submit

sed -i 's/gimel-biggpu/gimel5.heavygpu/g' desmond_md_job_1.sh
bash desmond_md_job_1.sh

* Analyze the MD simulation using the '''Simulation Interactions Diagram''' tool from the TASKS menu. Import the -out.cms file from the simulation to analyze, it takes 5-10 minutes to load. If you are satisfied with the simulation as evidenced by things such as stability of protein/ligand, proceed. If not, potentially try another docked pose or run the simulation for a longer time.

Visualize the trajectory and analyze the simulation with SID tool
[[File:SID_analysis.png|thumb|center|350px]]

* Use the schrodinger command line tools to convert the last frame of the simulation -out.cms into .mae file.
$SCHRODINGER/run membrane_cms2fep.py -ligand 'ligand' 2A_NBOH_MD-out.cms -o relax_2A_NBOH_pv.mae

You have now prepared the receptor and the initial state for an FEP+ map.

== Useful Schrodinger Commands ==

* Kill a submitted or running job:
$SCHRODINGER/jobcontrol -kill <jobID>

== Ligand side ==

Careful preparation of the ligands is critical to a successful FEP+ prediction. Best practices include running LigPrep on all the compounds to exhaustively enumerate all the stereoisomers and likely protonation states of the ligands. Note that triply-substituted ammonium cannot invert stereochemistry during the simulation, making it important to model both pseudo-stereoisomers.

* Force field builder
Run force field builder for all ligands

* Flexible ligand alignment OR core constrain docking
Depends on how similar/different are the ligands to the reference/center ligand

* Create FEP maps

* Write out the submission file; change host; submit on gimel5 via slurm

FEP+ for GPCR

2021-03-09T02:53:51Z

Chasemwebb:

2/25/2021 Ying Yang

Steps for setting up a FEP prediction for membrane protein
[[File:workflow_FEP.png|thumb|center|550px]]

== Protein side ==
* Begin by importing the structure of interest with a ligand that you are confident about. (FEP works best with an experimentally determined structure but can work with a docked pose if you are careful).

* Run the protein preparation wizard on your protein. Think carefully about protonation, you might even consider using the interactive protonation function to optimize the protonation state of the binding site residues or allosteric control points manually.

'''Protein model completeness'''
Protein preparation should include fixing any chain breaks, modeling in any loop conformations and adding any missing side chains. Chain breaks near the active site will likely lead to poor results. Disulfide bridges should be created and termini residues capped where applicable.

=== Equilibration of complex structure (with confident binding pose) ===

* Build membrane using the '''System Builder''' tool. Use residue information obtained from OPM database (https://opm.phar.umich.edu/), add salts, add solvent. Default settings work well for most situations.
res.num 76-97,112-136,141,143,146-171,194-215,227-229,231-256,323-345,347,360-380,382,398

* Use the '''Molecular Dynamics''' tool to write out a simulation file to run on the cluster. Load the system you built in the previous step from the workspace. Specify a simulation time, for high confidence complexes, 20 ns should suffice. For more exploratory complexes and to assess stability of the ligand extend to 50-100 ns. Use the default Schrodinger protocol to relax the model system before starting the production run of the simulation. For ease of evaluation, you can check the box to output interaction analysis at the completion of the job. Instead of clicking run, click the gear to the left of the run button and write the file out to the disk.

* Replace the gimel-biggpu in the written out simulation job with the correct gpu location, gimel5.heavygpu then transfer (scp) the simulation job to gimel5, and submit

sed -i 's/gimel-biggpu/gimel5.heavygpu/g' desmond_md_job_1.sh
bash desmond_md_job_1.sh

* Analyze the MD simulation
Visualize the trajectory and analyze the simulation with SID tool
[[File:SID_analysis.png|thumb|center|350px]]

* Convert -out.cms into mae
$SCHRODINGER/run membrane_cms2fep.py -ligand 'ligand' 2A_NBOH_MD-out.cms -o relax_2A_NBOH_pv.mae

* Kill a submitted or running job:
$SCHRODINGER/jobcontrol -kill <jobID>

== Ligand side ==

Careful preparation of the ligands is critical to a successful FEP+ prediction. Best practices include running LigPrep on all the compounds to exhaustively enumerate all the stereoisomers and likely protonation states of the ligands. Note that triply-substituted ammonium cannot invert stereochemistry during the simulation, making it important to model both pseudo-stereoisomers.

* Force field builder
Run force field builder for all ligands

* Flexible ligand alignment OR core constrain docking
Depends on how similar/different are the ligands to the reference/center ligand

* Create FEP maps

* Write out the submission file; change host; submit on gimel5 via slurm

FEP+ for GPCR

2021-03-09T02:52:33Z

Chasemwebb:

2/25/2021 Ying Yang

Steps for setting up a FEP prediction for membrane protein
[[File:workflow_FEP.png|thumb|center|550px]]

== Protein side ==
* Begin by importing the structure of interest with a ligand that you are confident about. (FEP works best with an experimentally determined structure but can work with a docked pose if you are careful).

* Run the protein preparation wizard on your protein. Think carefully about protonation, you might even consider using the interactive protonation function to optimize the protonation state of the binding site residues or allosteric control points manually.

'''Protein model completeness'''
Protein preparation should include fixing any chain breaks, modeling in any loop conformations and adding any missing side chains. Chain breaks near the active site will likely lead to poor results. Disulfide bridges should be created and termini residues capped where applicable.

=== Equilibration of complex structure (with confident binding pose) ===

* Build membrane using the '''System Builder''' tool. Use residue information obtained from OPM database (https://opm.phar.umich.edu/), add salts, add solvent. Default settings work well for most situations.
res.num 76-97,112-136,141,143,146-171,194-215,227-229,231-256,323-345,347,360-380,382,398

* Use the '''Molecular Dynamics''' tool to write out a simulation file to run on the cluster. Load the system you built in the previous step from the workspace. Specify a simulation time, for high confidence complexes, 20 ns should suffice. For more exploratory complexes and to assess stability of the ligand extend to 50-100 ns. Use the default Schrodinger protocol to relax the model system before starting the production run of the simulation. For ease of evaluation, you can check the box to output interaction analysis at the completion of the job. Instead of clicking run, click the gear to the left of the run button and write the file out to the disk.

* Replace the gimel-biggpu in the written out simulation job with the correct gpu location, gimel5.heavygpu then transfer (scp) the simulation job to gimel5, and submit

sed -i 's/gimel-biggpu/gimel5.heavygpu/g' desmond_md_job_1.sh
bash desmond_md_job_1.sh

* Kill a submitted or running job:
$SCHRODINGER/jobcontrol -kill <jobID>

* Analyze the MD simulation
Visualize the trajectory and analyze the simulation with SID tool
[[File:SID_analysis.png|thumb|center|350px]]

* Convert -out.cms into mae
$SCHRODINGER/run membrane_cms2fep.py -ligand 'ligand' 2A_NBOH_MD-out.cms -o relax_2A_NBOH_pv.mae

== Ligand side ==

Careful preparation of the ligands is critical to a successful FEP+ prediction. Best practices include running LigPrep on all the compounds to exhaustively enumerate all the stereoisomers and likely protonation states of the ligands. Note that triply-substituted ammonium cannot invert stereochemistry during the simulation, making it important to model both pseudo-stereoisomers.

* Force field builder
Run force field builder for all ligands

* Flexible ligand alignment OR core constrain docking
Depends on how similar/different are the ligands to the reference/center ligand

* Create FEP maps

* Write out the submission file; change host; submit on gimel5 via slurm

FEP+ for GPCR

2021-03-09T02:50:02Z

Chasemwebb:

2/25/2021 Ying Yang

Steps for setting up a FEP prediction for membrane protein
[[File:workflow_FEP.png|thumb|center|550px]]

== Protein side ==
* Begin by importing the structure of interest with a ligand that you are confident about. (FEP works best with an experimentally determined structure but can work with a docked pose if you are careful).

* Run the protein preparation wizard on your protein. Think carefully about protonation, you might even consider using the interactive protonation function to optimize the protonation state of the binding site residues or allosteric control points manually.

'''Protein model completeness'''
Protein preparation should include fixing any chain breaks, modeling in any loop conformations and adding any missing side chains. Chain breaks near the active site will likely lead to poor results. Disulfide bridges should be created and termini residues capped where applicable.

=== Equilibration of complex structure (with confident binding pose) ===

* Build membrane using the '''System Builder''' tool. Use residue information obtained from OPM database (https://opm.phar.umich.edu/), add salts, add solvent. Default settings work well for most situations.
res.num 76-97,112-136,141,143,146-171,194-215,227-229,231-256,323-345,347,360-380,382,398

* Use Desmond to write an MD job submission file using the '''Molecular Dynamics''' tool. Load the system you built in the previous step from the workspace. Specify a simulation time, for high confidence complexes, 20 ns should suffice. For more exploratory complexes and to assess stability of the ligand extend to 50-100 ns. Use the default Schrodinger protocol to relax the model system before starting the production run of the simulation. For ease of evaluation, you can check the box to output interaction analysis at the completion of the job. Instead of clicking run, click the gear to the left and write the file out to the disk.

* Replace the gimel-biggpu in the written out simulation job with the correct gpu location, then transfer (scp) the simulation job to gimel5, and submit
sed -i 's/gimel-biggpu/gimel5.heavygpu/g' desmond_md_job_1.sh
bash desmond_md_job_1.sh

* Kill a submitted or running job:
$SCHRODINGER/jobcontrol -kill <jobID>

* Analyze the MD simulation
Visualize the trajectory and analyze the simulation with SID tool
[[File:SID_analysis.png|thumb|center|350px]]

* Convert -out.cms into mae
$SCHRODINGER/run membrane_cms2fep.py -ligand 'ligand' 2A_NBOH_MD-out.cms -o relax_2A_NBOH_pv.mae

== Ligand side ==

Careful preparation of the ligands is critical to a successful FEP+ prediction. Best practices include running LigPrep on all the compounds to exhaustively enumerate all the stereoisomers and likely protonation states of the ligands. Note that triply-substituted ammonium cannot invert stereochemistry during the simulation, making it important to model both pseudo-stereoisomers.

* Force field builder
Run force field builder for all ligands

* Flexible ligand alignment OR core constrain docking
Depends on how similar/different are the ligands to the reference/center ligand

* Create FEP maps

* Write out the submission file; change host; submit on gimel5 via slurm

FEP+ for GPCR

2021-03-09T02:35:51Z

Chasemwebb:

2/25/2021 Ying Yang

Steps for setting up a FEP prediction for membrane protein
[[File:workflow_FEP.png|thumb|center|550px]]

== Protein side ==
* Begin by importing the structure of interest with a ligand that you are confident about. (FEP works best with an experimentally determined structure but can work with a docked pose if you are careful).

* Run the protein preparation wizard on your protein. Think carefully about protonation, you might even consider using the interactive protonation function to optimize the protonation state of the binding site residues or allosteric control points manually.

'''Protein model completeness'''
Protein preparation should include fixing any chain breaks, modeling in any loop conformations and adding any missing side chains. Chain breaks near the active site will likely lead to poor results. Disulfide bridges should be created and termini residues capped where applicable.

=== Equilibration of complex structure (with confident binding pose) ===

* Build membrane using the '''System Builder''' tool. Use residue information obtained from OPM database (https://opm.phar.umich.edu/), add salts, add solvent. Default settings work well for most situations.
res.num 76-97,112-136,141,143,146-171,194-215,227-229,231-256,323-345,347,360-380,382,398

* Use Desmond to write an MD job submission file using the '''Molecular Dynamics''' tool. Replace gimel-biggpu to gimel5.heavygpu
sed -i 's/gimel-biggpu/gimel5.heavygpu/g' desmond_md_job_1.sh

* Transfer (scp) to gimel5, and submit
bash desmond_md_job_1.sh

* Kill a submitted or running job:
$SCHRODINGER/jobcontrol -kill <jobID>

* Analyze the MD simulation
Visualize the trajectory and analyze the simulation with SID tool
[[File:SID_analysis.png|thumb|center|350px]]

* Convert -out.cms into mae
$SCHRODINGER/run membrane_cms2fep.py -ligand 'ligand' 2A_NBOH_MD-out.cms -o relax_2A_NBOH_pv.mae

== Ligand side ==

Careful preparation of the ligands is critical to a successful FEP+ prediction. Best practices include running LigPrep on all the compounds to exhaustively enumerate all the stereoisomers and likely protonation states of the ligands. Note that triply-substituted ammonium cannot invert stereochemistry during the simulation, making it important to model both pseudo-stereoisomers.

* Force field builder
Run force field builder for all ligands

* Flexible ligand alignment OR core constrain docking
Depends on how similar/different are the ligands to the reference/center ligand

* Create FEP maps

* Write out the submission file; change host; submit on gimel5 via slurm

FEP+ for GPCR

2021-03-09T02:33:21Z

Chasemwebb:

2/25/2021 Ying Yang

Steps for setting up a FEP prediction for membrane protein
[[File:workflow_FEP.png|thumb|center|550px]]

== Protein side ==
* Begin by importing the structure of interest with a ligand that you are confident about. (FEP works best with an experimentally determined structure but can work with a docked pose if you are careful).

* Run the protein preparation wizard on your protein. Think carefully about protonation, you might even consider using the interactive protonation function to optimize the protonation state of the binding site residues or allosteric control points manually.

'''Protein model completeness'''
Protein preparation should include fixing any chain breaks, modeling in any loop conformations and adding any missing side chains. Chain breaks near the active site will likely lead to poor results. Disulfide bridges should be created and termini residues capped where applicable.

=== Equilibration of complex structure (with confident binding pose) ===

* Build membrane using the '''System Builder''' tool. Use residue information obtained from OPM database (https://opm.phar.umich.edu/), add salts, add solvent
res.num 76-97,112-136,141,143,146-171,194-215,227-229,231-256,323-345,347,360-380,382,398

* write job submission file, and replace gimel-biggpu to gimel5.heavygpu
sed -i 's/gimel-biggpu/gimel5.heavygpu/g' desmond_md_job_1.sh

* Transfer (scp) to gimel5, and submit
bash desmond_md_job_1.sh

* Kill a submitted or running job:
$SCHRODINGER/jobcontrol -kill <jobID>

* Analyze the MD simulation
Visualize the trajectory and analyze the simulation with SID tool
[[File:SID_analysis.png|thumb|center|350px]]

* Convert -out.cms into mae
$SCHRODINGER/run membrane_cms2fep.py -ligand 'ligand' 2A_NBOH_MD-out.cms -o relax_2A_NBOH_pv.mae

== Ligand side ==

Careful preparation of the ligands is critical to a successful FEP+ prediction. Best practices include running LigPrep on all the compounds to exhaustively enumerate all the stereoisomers and likely protonation states of the ligands. Note that triply-substituted ammonium cannot invert stereochemistry during the simulation, making it important to model both pseudo-stereoisomers.

* Force field builder
Run force field builder for all ligands

* Flexible ligand alignment OR core constrain docking
Depends on how similar/different are the ligands to the reference/center ligand

* Create FEP maps

* Write out the submission file; change host; submit on gimel5 via slurm

FEP+ for GPCR

2021-03-09T02:29:42Z

Chasemwebb:

2/25/2021 Ying Yang

Steps for setting up a FEP prediction for membrane protein
[[File:workflow_FEP.png|thumb|center|550px]]

== Protein side ==
Begin by importing the structure of interest with a ligand that you are confident about. (FEP works best with an experimentally determined structure but can work with a docked pose if you are careful).

Run the protein preparation wizard on your protein. Think carefully about protonation, you might even consider using the interactive protonation function to optimize the protonation state of the binding site residues or allosteric control points manually.
Carefully look into the binding site and make sure the residues are correctly protonated...

'''Protein model completeness'''
Protein preparation should include fixing any chain breaks, modeling in any loop conformations and adding any missing side chains. Chain breaks near the active site will likely lead to poor results. Disulfide bridges should be created and termini residues capped where applicable.

=== Equilibration of complex structure (with confident binding pose) ===

* Build membrane (use the OPM database), add salts, add solvent
res.num 76-97,112-136,141,143,146-171,194-215,227-229,231-256,323-345,347,360-380,382,398

* write job submission file, and replace gimel-biggpu to gimel5.heavygpu
sed -i 's/gimel-biggpu/gimel5.heavygpu/g' desmond_md_job_1.sh

* Transfer (scp) to gimel5, and submit
bash desmond_md_job_1.sh

* Kill a submitted or running job:
$SCHRODINGER/jobcontrol -kill <jobID>

* Analyze the MD simulation
Visualize the trajectory and analyze the simulation with SID tool
[[File:SID_analysis.png|thumb|center|350px]]

* Convert -out.cms into mae
$SCHRODINGER/run membrane_cms2fep.py -ligand 'ligand' 2A_NBOH_MD-out.cms -o relax_2A_NBOH_pv.mae

== Ligand side ==

Careful preparation of the ligands is critical to a successful FEP+ prediction. Best practices include running LigPrep on all the compounds to exhaustively enumerate all the stereoisomers and likely protonation states of the ligands. Note that triply-substituted ammonium cannot invert stereochemistry during the simulation, making it important to model both pseudo-stereoisomers.

* Force field builder
Run force field builder for all ligands

* Flexible ligand alignment OR core constrain docking
Depends on how similar/different are the ligands to the reference/center ligand

* Create FEP maps

* Write out the submission file; change host; submit on gimel5 via slurm

Analyze ligand geometries using the Cambridge Structural Database (CSD)

2019-05-21T23:16:49Z

Chasemwebb:

Written by Chase Webb 20190521

The generation of torsion angle distributions to determine conformational preference about single rotatable bonds is one of the most common uses of the CSD. This tutorial illustrates how '''Mogul''', part of the CSD software can be used to rapidly determine the torsion angle preferences of substituted oxamides by inspecting occurrences of this motif in the CSD.

Source the current environment for CSD-2019
source /nfs/soft/csd/csd-2019/env.csh

Open mogul interface by running the following command:
/nfs/soft/csd/csd-2019/CSD_2019/bin/mogul

If prompted for the license, it lives here:
/nfs/soft/csd/csd-2019/CSD_2019/csd_licence.dat

You can save a copy in your home directory so that you are not prompted for the location every time you use the CSD.

In the CCDC Mogul 1.8.1 interface, import smiles by clicking load, or draw the motif you are interested in assessing by clicking on the Draw button to bring up a drawing window.

Hit search to submit the query and you should find see a histogram of the reported torsion angles for your query.

Analyze ligand geometries using the Cambridge Structural Database (CSD)

2019-05-21T22:18:52Z

Chasemwebb:

Written by Chase Webb 20190521

The generation of torsion angle distributions to determine conformational preference about single rotatable bonds is one of the most common uses of the CSD. This tutorial illustrates how '''Mogul''', part of the CSD software can be used to rapidly determine the torsion angle preferences of substituted oxamides by inspecting occurrences of this motif in the CSD.

Source the current environment for CSD-2019
source /nfs/soft/csd/csd-2019/env.csh

Open mogul it by running the following command:
/nfs/soft/csd/csd-2019/CSD_2019/bin/mogul

If prompted for the license, it lives here:
/nfs/soft/csd/csd-2019/CSD_2019/csd_licence.dat

In the CCDC Mogul 1.8.1 interface, import smiles by clicking load, or draw the motif you are interested in assessing by clicking on the Draw button to bring up a drawing window.

Hit search to submit the query and you should find see a histogram of the reported torsion angles for your query.

Analyze ligand geometries using the Cambridge Structural Database (CSD)

2019-05-21T21:53:17Z

Chasemwebb:

Written by Chase Webb 20190521

The generation of torsion angle distributions to determine conformational preference about single rotatable bonds is one of the most common uses of the CSD. This tutorial illustrates how '''Mogul''', part of the CSD software can be used to rapidly determine the torsion angle preferences of substituted oxamides by inspecting occurrences of this motif in the CSD.

Source the current environment for CSD-2019
source /nfs/soft/csd/csd-2019/env.csh

Open mogul it by running the following command:
/nfs/soft/csd/csd-2019/CSD_2019/bin/mogul

If prompted for the license, it lives here:
/nfs/soft/csd/csd-2019/CSD_2019/csd_licence.dat

In the CCDC Mogul 1.8.1 interface, import smiles by clicking load, or draw the motif you are interested in assessing by clicking on the Draw button to bring up a drawing window.

Analyze ligand geometries using the Cambridge Structural Database (CSD)

2019-05-21T21:32:02Z

Chasemwebb: Created page with "Often, it is useful to determine if a torsion or conformer is not energetically favourable. This can be accomplished from an ab initio QM calculation but in the Shoichet Lab, ..."

Often, it is useful to determine if a torsion or conformer is not energetically favourable. This can be accomplished from an ab initio QM calculation but in the Shoichet Lab, it is more common to rule out unfavourable conformers by comparing a questionable torsion to the deposited structures in the CSD.

To use the CSD, first open it by running the following command:
/nfs/soft/csd/csd-2018/CSD_2018/bin/mercury

Next,

Other Useful Stuff

2019-05-21T21:29:19Z

Chasemwebb:

* [[Useful chimera commands]]

* [[calculate volume of the binding site and molecules]]

* [[PDB surface points for figures]]

* [[Analyze ligand geometries using the Cambridge Structural Database (CSD)]]

Filtering ligands for novelty

2018-10-01T23:31:01Z

Chasemwebb:

Written by Chase Webb 09-01-2018

After a large scale docking campaign, it is important to remove prospective ligands that are too similar to compounds that are already known to modulate the receptor. In this way, we can focus on assessing new chemical interactions. This is best completed after clustering has been conducted as specified here:[http://wiki.bkslab.org/index.php/How_to_process_results_from_a_large-scale_docking Processing Results from LSD]

=This process proceeds in the following steps:=

Make a new directory to do similarity filtering.

Make a symbolic link to the location where clustering occurred.

1. '''Generate a list of smiles for the known compounds.''' The most simple way to do this is to download them from ZINC. For the Mu opioid receptor (OPRM1) for instance, go here: [https://zinc15.docking.org/genes/home/ ZINC15 Genes]
[[File:filter_ligands_image1.png|thumb|center|1000px|Search for Your Molecules in ZINC Using the UNIPROT Ascension ID for Your Target, for example OPRM1 for the Mu Receptor]]

2. '''Generate Fingerprints for the known compounds.''' Run the following script written by TEB and JKL. The inputs are name of the knowns file and the name of the output fingerprint file.
python ~jklyu/zzz.github/ChemInfTools/utils/teb_chemaxon_cheminf_tools/generate_chemaxon_fingerprints.py knowns_list.smi knowns

3. '''Convert the fingerprints from binary to unsigned integers.''' Run the following script written by TEB and JKL. The inputs are the bitstrings generated from the above script, the smiles file used to generate the above script, and the prefix of the output file. You will need to do this for the knowns and the clusterheads that were calculated in the previous tutorial: [http://wiki.bkslab.org/index.php/How_to_process_results_from_a_large-scale_docking Processing Results from LSD]
python ~jklyu/zzz.github/ChemInfTools/utils/convert_fp_2_fp_in_16unit/convert_fp_2_fp_in_uint16 knowns.fp knowns.fp knowns_list.smi knowns
python ~jklyu/zzz.github/ChemInfTools/utils/convert_fp_2_fp_in_16unit/convert_fp_2_fp_in_uint16 extract_all.topN.sort.uniq.fp extract_all.topN.zincid.sort.uniq.smi topN_clusterhead

4. '''Calculate an all by all TC matrix for the knowns against the clusterheads.''' Run the following script written by TEB and JKL:
nohup ~jklyu/zzz.github/ChemInfTools/utils/cal_Tc_matrix_uint16/cal_Tc_matrix_uint16 topN_clusterhead_uint16.fp extract_all.topN.zincid.sort.uniq.smi topN_clusterhead_uint16.count knowns_uint16.fp knowns_list.smi knowns_uint16.count tc_matrix > log &

The arguments supplied to this script are as follows:
(1) topN_clusterhead_uint16.fp
(2) extract_all.topN.zincid.sort.uniq.smi
(3) topN_clusterhead_uint16.count
(4) knowns_uint16.fp
(5) knowns_list.smi
(6) knowns_uint16.count
(7) prefix for output file

To view the progress of this script, use the command ps -fu or ls -l the directory where the script is running and check for the log file.

Filtering ligands for novelty

2018-10-01T22:14:53Z

Chasemwebb:

Filtering ligands for novelty

2018-10-01T21:56:11Z

Chasemwebb:

File:Filter ligands image1.png

2018-10-01T21:36:39Z

Chasemwebb: Chasemwebb uploaded a new version of "File:Filter ligands image1.png"

Navigating ZINC15 by gene name. Use the UNIPROT geneID to find a specific target.

Filtering ligands for novelty

2018-10-01T21:36:17Z

Chasemwebb:

Filtering ligands for novelty

2018-10-01T21:31:29Z

Chasemwebb:

File:Filter ligands image1.png

2018-10-01T21:30:55Z

Chasemwebb: Navigating ZINC15 by gene name. Use the UNIPROT geneID to find a specific target.

Navigating ZINC15 by gene name. Use the UNIPROT geneID to find a specific target.

Filtering ligands for novelty

2018-10-01T21:29:35Z

Chasemwebb:

Filtering ligands for novelty

2018-10-01T21:28:53Z

Chasemwebb:

Filtering ligands for novelty

2018-10-01T21:22:44Z

Chasemwebb: Created page with "Written by Chase Webb 09-01-2018 After a large scale docking campaign, it is important to remove prospective ligands that are too similar to compounds that are already known ..."

DOCK 3.7

2018-10-01T21:17:21Z

Chasemwebb:

= About =

DOCK 3.7 the current version in the [[DOCK 3]] series of docking programs developed and used by the [[Shoichet Lab]]. Please read and cite the DOCK 3.7 paper
[http://www.plosone.org/article/info:doi/10.1371/journal.pone.0075992 Coleman, Carchia, Sterling, Irwin & Shoichet, PLOS ONE 2013.]

DOCK 3.7 is written in Fortran and some C. It is an update of [[DOCK 3.6]] with many improved features. DOCK 3.7 comes with all the tools necessary to prepare a
protein for docking and some tools necessary to build ligands, though some tools must be obtained externally. It uses new Flexibase/DB2 files found in [[ZINC15]]. It includes tools to prepare receptors, and several auxiliary scripts.

DOCK 3.7 is available at [http://dock.compbio.ucsf.edu/DOCK3.7/ http://dock.compbio.ucsf.edu/DOCK3.7/].

{{TOCright}}

= Start here =
* [[So you want to set up a lab]] - only if you don't already have hardware ready.
* [[Install DOCK 3.7]]
* [[DOCK 3.7 2014/09/25 FXa Tutorial]]
* [[DOCK 3.7 2015/04/15 abl1 Tutorial]] superseded
* [[DOCK 3.7 2018/06/05 abl1 Tutorial]]
* [[DOCK 3.7 2016/09/16 Tutorial for Enrichment Calculations (Trent & Jiankun)]]
* [[DOCK 3.7 tutorial (Anat)]]
* [[DOCK 3.7 with GIST tutorials]]
* [[DOCK 3.7 tutorial based on Webinar 2017/06/28]]
* [[Getting started with DOCK 3.7]]
* [[Blastermaster]] - Prepare input for and then run [[DOCK 3.7]].
* [[Ligand preparation 3.7]] - Create dockable databases for [[DOCK 3.7]].
* [[Ligand preparation]] - different version.
* [[Ligand prep Irwin Nov 2016]] - John's current version
* [[Mol2db2 Format 2]] - details on the database formate.
* [[Running docking 3.7]] - how to actually run docking.
* [[DOCK 3.7 Development]] - for software developers
=== For DOCKovalent, start here ===
* [[DOCKovalent_3.7]]
* [[DOCKovalent linker design tutorial]]
* [[DOCKovalent cysteine inhibitor design tutorial]]

= Prepare Receptor =
* [[Protein Target Preparation]]
* [[Adding Static Waters to the Protein Structure]]
* [[Flexible Docking]]
* [[Visualize docking grids]]
* [[Minimize protein-ligand complex with AMBER]]

= Prepare Screening Library =
* [[mol2db2]] is the program that creates [[mol2db2 format]] database files which are read by [[DOCK 3.7]]
* [[ligand preparation 3.7]]
* [[generating decoys (Reed's way)]]

= Running Docking =
* [[Running docking 3.7]] - JJI currently working on this.
* [[Running DOCK 3.7]] - this seems to be slightly dated.
* [[INDOCK 3.7]] - file format used by [[DOCK 3.7]]
* [[DOCK3.7_INDOCK_Minimization_Parameter]] - How to run DOCK 3.7.1rc1 (and latter versions) with the minimization.
* Interpreting the [[OUTDOCK 3.7]] file.

= Analysis =
* [[Analyzing DOCK Results]]
* [http://autodude.docking.org/ Auto-DUD-E Test Set] (external site)
* [[Other Useful Stuff]]

= Post Docking Clustering=
* [[How to process results from a large-scale docking]]
* [[Large-scale SMILES Requesting and Fingerprints Converting]]
* [[ECFP4 Best First Clustering]]
* [[Bemis-Murcko Scaffold Analysis]]

= Post Docking Filters=
* [[Large-scale TC Calculations]]
* [[Whole Library TC to Knowns Calculations]]
* [[Filtering ligands for novelty]]
= Redocking with Enhanced Sampling =
*[[Sample Additional Ring Puckers ]]
= Rescoring =
*[[Rescoring_with_DOCK_3.7]]

= Available Libraries =
* [[ZINC Subset DB2 file locations]]
* how to get db2 files from zinc15.docking.org

= Analog by Catalog=
* [[Substructure searching]]
* [[TC analog searching in ZINC]]

= Previous verisons and compatibility =
DOCK 3.7 is part of the [[DOCK 3]] series. It differs substantially from its immediate predecessor [[DOCK 3.6]],
which uses a different format of database files that cannot be read by [[DOCK 3.7]], and vice versa.

= How to Cite =
To cite the DOCK 3.7 paper, please use
[http://www.plosone.org/article/info:doi/10.1371/journal.pone.0075992 Coleman, Carchia, Sterling, Irwin & Shoichet, PLOS ONE 2013.]

= How to Download =
DOCK 3.7 is available at [http://dock.compbio.ucsf.edu/DOCK3.7/ http://dock.compbio.ucsf.edu/DOCK3.7/].

= Implementation =
DOCK 3.7 is written in Fortran and some C. Scripts are mostly in [[python]] and [[perl]].

{{Template:CC-BY-SA-30}}
{{Template:Coleman}}

[[Category:DOCK 3.7]]
[[Category:Software]]
[[Category:Freecom]]

Sample Additional Ring Puckers

2018-06-21T16:08:15Z

Chasemwebb:

1. Create a directory and cd into it
mkdir ZINC000001664886
cd ZINC000001664886

2. Get the protonated smi from zinc:
http://zinc15.docking.org/protomers/342955170/

Copy to clipboard and past it into a file add the zinc name to file, so that it looks like this:
CC1CCC([NH3+])CC1 ZINC000001664886

Or copy the protonated smiles into the current directory if you already generated protomers:
cp ../../normal_db2_gen/isomers/manual_gen/ZINC000001664886/ZINC000001664886.ism .
awk '{print $1" "$2}' ZINC000001664886.ism > ZINC000001664886.smi

3. For each isomer, give it a suffix, like _1, _2.
vim ZINC000001664886.smi
4. run corina with enhanced sampling of ring puckers
/nfs/soft/corina/current/corina -i t=smiles -o t=mol2 -d rc,flapn,de=10,mc=10,wh ZINC000001664886.smi ZINC000001664886.mol2

5. Split the whole mol2 file into individual conformers
python ~tbalius/zzz.scripts/separate_mol2_more10000_mod.py ZINC000001664886.mol2 test

6. Run build_ligand_mol2 for each mol2 conformer
foreach name (`ls ZINC000001664886*_test_*.mol2`)
mkdir $name:t:r
cd $name:t:r
cp ../${name} .
$DOCKBASE/ligand/generate/build_ligand_mol2.sh $name
cd ../
end

Sample Additional Ring Puckers

2018-06-21T16:07:50Z

Chasemwebb:

1. Create a directory and cd into it
mkdir ZINC000001664886
cd ZINC000001664886

2. Get the protonated smi from zinc:
http://zinc15.docking.org/protomers/342955170/

Copy to clipboard and past it into a file add the zinc name to file, so that it looks like this:
CC1CCC([NH3+])CC1 ZINC000001664886

Or copy the protonated smiles into the current directory if you already generated protomers:
cp ../../normal_db2_gen/isomers/manual_gen/ZINC000001664886/ZINC000001664886.ism .
awk '{print $1" "$2}' ZINC000001664886.ism > ZINC000001664886.smi

3. For each isomer, give it a suffix, like _1, _2.
vim ZINC000001664886.smi
4. run corina with enhanced sampling or ring puckers
/nfs/soft/corina/current/corina -i t=smiles -o t=mol2 -d rc,flapn,de=10,mc=10,wh ZINC000001664886.smi ZINC000001664886.mol2

5. Split the whole mol2 file into individual conformers
python ~tbalius/zzz.scripts/separate_mol2_more10000_mod.py ZINC000001664886.mol2 test

6. Run build_ligand_mol2 for each mol2 conformer
foreach name (`ls ZINC000001664886*_test_*.mol2`)
mkdir $name:t:r
cd $name:t:r
cp ../${name} .
$DOCKBASE/ligand/generate/build_ligand_mol2.sh $name
cd ../
end

How to do parameter scanning

2018-05-15T23:38:04Z

Chasemwebb: updated paths to scripts

Written by Jiankun Lyu, 2017/01/18

The hierarchy of the directories:

thin_spheres_parameter_scanning----- std_dockprep
|
|------ dockfiles
| |
| |----- working
| |
| ------ rec.pdb, xtal-lig.pdb, INDOCK and other files generated balstermaster.py
|
------- script ------ dockprep_thin_spheres_in_batches.csh
|
|------ submit_dockprep_thin_spheres.csh
|
|------ dockprep_thin_spheres.csh
|
|------ lig-decoy_enrichment.csh
|
|------ combineScoresAndPoses.csh
|
|------ AUCplot_of-lig-decoys.csh
|
|------ mk_matrix_logAUC.py
|
|------ sph_lib.py
|
|------ pdb_lib.py
|
------- close_sph.py

1) Make those directories above.
mkdir thin_spheres_parameter_scanning
cd thin_spheres_parameter_scanning
mkdir std_dockprep
mkdir script

2) Run blastermaster.py in std_dockprep. This will generate two directories: working and dockfiles

3) Download sph_lib.py, pdb_lib.py and close_sph.py files into the script directory
cd script
curl http://docking.org/~tbalius/code/for_dock_3.7/sph_lib.py > sph_lib.py
curl http://docking.org/~tbalius/code/for_dock_3.7/pdb_lib.py > pdb_lib.py
curl http://docking.org/~tbalius/code/for_dock_3.7/close_sph.py > close_sph.py

4) Copy scripts from my path, and modify as necessary.

cd script

cp /mnt/nfs/ex5/work/jklyu/large_scale_docking/DRD2/struct_20180322/A122I_add_polarH_mini_HID/thin_spheres_parameter_scanning/scripts/*dockprep* .

/mnt/nfs/reshwork/jklyu/D2R/scripts/lig-decoy_enrichment_submit.csh
/mnt/nfs/reshwork/jklyu/D2R/scripts/combineScoresAndPoses.csh
/mnt/nfs/reshwork/jklyu/D2R/scripts/mk_matrix_logAUC.py

5) Run parameter scanning.
cd ../ # go back to thin_spheres_parameter_scanning folder
csh /path/to/script/dockprep_thin_spheres_in_batches.csh /path/to/script/ /path/to/std_dockprep

Note:- you can edit dockprep_thin_spheres_in_batches.csh to include more CPUs in Job Bound

6) make the following subfolders

mkdir ligands-decoys
cd ligands-decoys
mkdir ligands
mkdir decoys

now copy your decoys.db2.gz to decoys
now copy your ligands.db2.gz to ligands
now copy decoys.smi to the folder
now copy ligands.smi to the folder

7) Submit DOCK and enrichment calculation.
csh /path/to/script/lig-decoy_enrichment.csh

8) Combine and analyze the docking results.
csh /path/to/script/combineScoresAndPoses.csh #1st change the path inside the script to your own dir
csh /path/to/script/AUCplot_of-lig-decoys.csh #1st change the path inside the script to your own dir

9) Visualize the logAUC by heatmap.
python /path/to/script/mk_matrix_logAUC.py

How to do parameter scanning

2018-05-15T23:32:42Z

Chasemwebb: updated paths to scripts

Written by Jiankun Lyu, 2017/01/18

The hierarchy of the directories:

thin_spheres_parameter_scanning----- std_dockprep
|
|------ dockfiles
| |
| |----- working
| |
| ------ rec.pdb, xtal-lig.pdb, INDOCK and other files generated balstermaster.py
|
------- script ------ dockprep_thin_spheres_in_batches.csh
|
|------ submit_dockprep_thin_spheres.csh
|
|------ dockprep_thin_spheres.csh
|
|------ lig-decoy_enrichment.csh
|
|------ combineScoresAndPoses.csh
|
|------ AUCplot_of-lig-decoys.csh
|
|------ mk_matrix_logAUC.py
|
|------ sph_lib.py
|
|------ pdb_lib.py
|
------- close_sph.py

1) Make those directories above.
mkdir thin_spheres_parameter_scanning
cd thin_spheres_parameter_scanning
mkdir std_dockprep
mkdir script

2) Run blastermaster.py in std_dockprep. This will generate two directories: working and dockfiles

3) Download sph_lib.py, pdb_lib.py and close_sph.py files into the script directory
cd script
curl http://docking.org/~tbalius/code/for_dock_3.7/sph_lib.py > sph_lib.py
curl http://docking.org/~tbalius/code/for_dock_3.7/pdb_lib.py > pdb_lib.py
curl http://docking.org/~tbalius/code/for_dock_3.7/close_sph.py > close_sph.py

4) Copy scripts from my path.

cd script
cp /mnt/nfs/ex5/work/jklyu/large_scale_docking/DRD2/struct_20180322/A122I_add_polarH_mini_HID/thin_spheres_parameter_scanning/scripts/*dockprep* .

cp /mnt/nfs/work/jklyu/AmpC/script/dockprep_thin_spheres_in_batches.csh .
cp /mnt/nfs/work/jklyu/AmpC/script/submit_dockprep_thin_spheres.csh .
cp /mnt/nfs/work/jklyu/AmpC/script/dockprep_thin_spheres.csh .

cp /mnt/nfs/work/jklyu/AmpC/script/lig-decoy_enrichment.csh .
cp /mnt/nfs/work/jklyu/AmpC/script/combineScoresAndPoses.csh .
cp /mnt/nfs/work/jklyu/AmpC/script/AUCplot_of-lig-decoys.csh .
cp /mnt/nfs/work/jklyu/AmpC/script/mk_matrix_logAUC.py .

5) Run parameter scanning.
cd ../ # go back to thin_spheres_parameter_scanning folder
csh /path/to/script/dockprep_thin_spheres_in_batches.csh /path/to/script/ /path/to/std_dockprep

Note:- you can edit dockprep_thin_spheres_in_batches.csh to include more CPUs in Job Bound

6) make the following subfolders

mkdir ligands-decoys
cd ligands-decoys
mkdir ligands
mkdir decoys

now copy your decoys.db2.gz to decoys
now copy your ligands.db2.gz to ligands
now copy decoys.smi to the folder
now copy ligands.smi to the folder

7) Submit DOCK and enrichment calculation.
csh /path/to/script/lig-decoy_enrichment.csh

8) Combine and analyze the docking results.
csh /path/to/script/combineScoresAndPoses.csh #1st change the path inside the script to your own dir
csh /path/to/script/AUCplot_of-lig-decoys.csh #1st change the path inside the script to your own dir

9) Visualize the logAUC by heatmap.
python /path/to/script/mk_matrix_logAUC.py