Launching Templates

PelePlatform (cluster)

Last Update: 14-02-2021

PelePlatform - v1.6.3, latest release

#!/bin/bash
#SBATCH -J peleplat_tests
#SBATCH --output=report_%j.out
#SBATCH --error=report_%j.err
#SBATCH --ntasks=2
#SBATCH --mem-per-cpu=1000

module purge

module load intel
module load intel-oneapi
module load imkl

source /shared/work/NBD_Utilities/miniconda3/etc/profile.d/conda.sh
conda activate /shared/work/NBD_Utilities/PELE/PELE_Softwares/PelePlatform/envs/peleplatform-1.6.3

export SCHRODINGER="/sNow/easybuild/centos/7.4.1708/Skylake/software/schrodinger2017-4/"
export SCHRODINGER_PYTHONPATH="/sNow/easybuild/centos/7.4.1708/Skylake/software/schrodinger2017-4/internal/lib/python2.7/site-packages"
export PELE="/shared/work/NBD_Utilities/PELE/PELE_Softwares/bin/PELE1.7.1/"

export LC_ALL=C; unset LANGUAGE
export I_MPI_PMI_LIBRARY=/usr/lib64/libpmi.so
export LD_LIBRARY_PATH=/shared/work/NBD_Utilities/PELE/PELE_Softwares/local_deps/pele_deps/boost_1_52/lib:$LD_LIBRARY_PATH
export SRUN=1  # this is to avoid having to set usesrun: true in input.yaml

python -c "import pele_platform; print('Using PELEPlatform, version', pele_platform.__version__)"
python -m pele_platform.main input_fast.yaml

PelePlatform - v1.6.2

#!/bin/bash
#SBATCH -J peleplat_tests
#SBATCH --output=report_%j.out
#SBATCH --error=report_%j.err
#SBATCH --ntasks=2
#SBATCH --mem-per-cpu=1000

module purge

module load intel
module load intel-oneapi
module load imkl

source /shared/work/NBD_Utilities/miniconda3/etc/profile.d/conda.sh
conda activate /shared/work/NBD_Utilities/PELE/PELE_Softwares/PelePlatform/envs/peleplatform-1.6.2

export SCHRODINGER="/sNow/easybuild/centos/7.4.1708/Skylake/software/schrodinger2017-4/"
export SCHRODINGER_PYTHONPATH="/sNow/easybuild/centos/7.4.1708/Skylake/software/schrodinger2017-4/internal/lib/python2.7/site-packages"
export PELE="/shared/work/NBD_Utilities/PELE/PELE_Softwares/bin/PELE1.7.1/"

export LC_ALL=C; unset LANGUAGE
export I_MPI_PMI_LIBRARY=/usr/lib64/libpmi.so
export LD_LIBRARY_PATH=/shared/work/NBD_Utilities/PELE/PELE_Softwares/local_deps/pele_deps/boost_1_52/lib:$LD_LIBRARY_PATH
export SRUN=1  # this is to avoid having to set usesrun: true in input.yaml

python -c "import pele_platform; print('Using PELEPlatform, version', pele_platform.__version__)"
python -m pele_platform.main input_fast.yaml

PelePlatform - v1.6.1

#!/bin/bash
#SBATCH -J peleplat_tests
#SBATCH --output=report_%j.out
#SBATCH --error=report_%j.err
#SBATCH --ntasks=2
#SBATCH --mem-per-cpu=1000

module purge

module load intel
module load intel-oneapi
module load imkl

source /shared/work/NBD_Utilities/miniconda3/etc/profile.d/conda.sh
conda activate /shared/work/NBD_Utilities/PELE/PELE_Softwares/PelePlatform/envs/peleplatform-1.6.1

export SCHRODINGER="/sNow/easybuild/centos/7.4.1708/Skylake/software/schrodinger2017-4/"
export SCHRODINGER_PYTHONPATH="/sNow/easybuild/centos/7.4.1708/Skylake/software/schrodinger2017-4/internal/lib/python2.7/site-packages"
export PELE="/shared/work/NBD_Utilities/PELE/PELE_Softwares/bin/PELE1.7.1/"

export LC_ALL=C; unset LANGUAGE
export I_MPI_PMI_LIBRARY=/usr/lib64/libpmi.so
export LD_LIBRARY_PATH=/shared/work/NBD_Utilities/PELE/PELE_Softwares/local_deps/pele_deps/boost_1_52/lib:$LD_LIBRARY_PATH
export SRUN=1  # this is to avoid having to set usesrun: true in input.yaml

python -c "import pele_platform; print('Using PELEPlatform, version', pele_platform.__version__)"
python -m pele_platform.main input_fast.yaml

PelePlatform - v1.6

#!/bin/bash
#SBATCH -J peleplat_tests
#SBATCH --output=report_%j.out
#SBATCH --error=report_%j.err
#SBATCH --ntasks=5
#SBATCH --mem-per-cpu=1000

module purge
module load intel-oneapi
module load imkl

source /shared/work/NBD_Utilities/miniconda3/etc/profile.d/conda.sh
conda activate /shared/work/NBD_Utilities/PELE/PELE_Softwares/PelePlatform/envs/peleplatform-1.6.0

export SCHRODINGER="/sNow/easybuild/centos/7.4.1708/Skylake/software/schrodinger2017-4/"
export SCHRODINGER_PYTHONPATH="/sNow/easybuild/centos/7.4.1708/Skylake/software/schrodinger2017-4/internal/lib/python2.7/site-packages"
export PELE="/shared/work/NBD_Utilities/PELE/PELE_Softwares/bin/PELE1.7.1/"

export LC_ALL=C; unset LANGUAGE
export I_MPI_PMI_LIBRARY=/usr/lib64/libpmi.so
export LD_LIBRARY_PATH=/shared/work/NBD_Utilities/PELE/PELE_dependencies/boost-1.66.0/lib:$LD_LIBRARY_PATH
export SRUN=1  # this is to avoid having to set usesrun: true in input.yaml

python -m pele_platform.main input.yaml

PelePlatform - v1.5.1

#!/bin/bash
#SBATCH -J PELE_MPI_test
#SBATCH --output=report_%j.out
#SBATCH --error=report_%j.err
#SBATCH --ntasks=5
#SBATCH --mem-per-cpu=1000

# DO NOT CHANGE ###########################

module purge

export SCHRODINGER="/sNow/easybuild/centos/7.4.1708/Skylake/software/schrodinger2017-4/"
export SCHRODINGER_PYTHONPATH="/sNow/easybuild/centos/7.4.1708/Skylake/software/schrodinger2017-4/internal/lib/python2.7/site-packages"
export PELE="/shared/work/NBD_Utilities/PELE/PELE_Softwares/bin/PELE1.6/"
export LC_ALL=C; unset LANGUAGE

unset PYTHONPATH

module load impi/2018.1.163-iccifort-2018.1.163-GCC-6.4.0-2.28 wjelement/1.3-intel-2018a
module load Crypto++/6.1.0-intel-2018a OpenBLAS/0.2.20-GCC-6.4.0-2.28

export I_MPI_PMI_LIBRARY=/usr/lib64/libpmi.so
export LD_LIBRARY_PATH=/shared/work/NBD_Utilities/PELE/PELE_Softwares/local_deps/pele_deps/boost_1_52/lib:$LD_LIBRARY_PATH
export PYTHONPATH="/shared/work/NBD_Utilities/PELE/PELE_Softwares/PelePlatform/depend:$PYTHONPATH"
export SRUN=1  # this is to avoid having to set usesrun: true in input.yaml

###################################################################

/shared/work/NBD_Utilities/PELE/PELE_Softwares/PelePlatform/depend/bin/python -m pele_platform.main input.yaml

PelePlatform + singularity (cluster)

Last Update: 21-12-2021

PelePlatform - v1.6.1 + PELE ++ Singularity v1.7.1

#!/bin/bash
#SBATCH -J peleplat
#SBATCH --output=peleplat_%j.out
#SBATCH --error=peleplat_%j.err
#SBATCH --ntasks=5
#SBATCH --mem-per-cpu=1000

##################################################################
module purge
module load OpenMPI/3.1.4-GCC-8.3.0

export PELE="/opt/PELE"
export SCHRODINGER="/sNow/easybuild/centos/7.4.1708/Skylake/software/schrodinger2017-4"
export SCHRODINGER_PYTHONPATH="/sNow/easybuild/centos/7.4.1708/Skylake/software/schrodinger2017-4/internal/lib/python2.7/site-packages"
export PELE_LICENSE="/shared/work/NBD_Utilities/PELE/PELE_Softwares/bin/PELE1.7.1/licenses"
export SINGULARITY_BIND="$PELE_LICENSE"
export PELE_MPI_PARAMS="--verbose"
export SINGULARITY_EXEC="/shared/work/NBD_Utilities/PELE/PELE_Softwares/containers/pele1.7.1_release.sif"
export LC_ALL=C; unset LANGUAGE

source /shared/work/NBD_Utilities/miniconda3/etc/profile.d/conda.sh
conda activate /shared/work/NBD_Utilities/PELE/PELE_Softwares/PelePlatform/envs/peleplatform-1.6.1
##################################################################


python -m pele_platform.main input.yaml

PelePlatform (tirant)

Last Update: 17-12-2021

PelePlatform - v1.6.2, latest release

#!/bin/bash
#SBATCH -J peleplat_test
#SBATCH --output=report_%j.out
#SBATCH --error=report_%j.err
#SBATCH --ntasks=5
#SBATCH --mem-per-cpu=1000

module purge
module load impi mkl
module load miniconda

source activate /storage/projects/cns14/Utilities/platform/envs/peleplatform-1.6.2

export SCHRODINGER="/storage/projects/cns14/Utilities/schrodinger2021-4/"
export SCHRODINGER_PYTHONPATH="/storage/projects/cns14/Utilities/schrodinger2021-4/internal/lib/python3.8/site-packages"
export PELE="/storage/projects/cns14/Utilities/PELE/PELE1.7.1/"

export LC_ALL=C; unset LANGUAGE
export I_MPI_PMI_LIBRARY=/usr/lib64/libpmi.so
export LD_LIBRARY_PATH=/storage/projects/cns14/Utilities/PELE/dependencies/boost-1.66.0/lib:$LD_LIBRARY_PATH
export SRUN=1  # this is to avoid having to set usesrun: true in input.yaml

python -m pele_platform.main input.yaml

**PelePlatform - v1.6.1

#!/bin/bash
#SBATCH -J peleplat_test
#SBATCH --output=report_%j.out
#SBATCH --error=report_%j.err
#SBATCH --ntasks=5
#SBATCH --mem-per-cpu=1000

module purge
module load impi mkl
module load miniconda

source activate /storage/projects/cns14/Utilities/platform/envs/peleplatform-1.6.1

export SCHRODINGER="/storage/projects/cns14/Utilities/schrodinger2021-4/"
export SCHRODINGER_PYTHONPATH="/storage/projects/cns14/Utilities/schrodinger2021-4/internal/lib/python3.8/site-packages"
export PELE="/storage/projects/cns14/Utilities/PELE/PELE1.7.1/"

export LC_ALL=C; unset LANGUAGE
export I_MPI_PMI_LIBRARY=/usr/lib64/libpmi.so
export LD_LIBRARY_PATH=/storage/projects/cns14/Utilities/PELE/dependencies/boost-1.66.0/lib:$LD_LIBRARY_PATH
export SRUN=1  # this is to avoid having to set usesrun: true in input.yaml

python -m pele_platform.main input.yaml

**PelePlatform - v1.6.0

#!/bin/bash
#SBATCH -J peleplat_test
#SBATCH --output=report_%j.out
#SBATCH --error=report_%j.err
#SBATCH --ntasks=5
#SBATCH --mem-per-cpu=1000

module purge
module load impi mkl
module load miniconda

source activate /storage/projects/cns14/Utilities/platform/envs/peleplatform-1.6.0

export SCHRODINGER="/storage/projects/cns14/Utilities/schrodinger2021-4/"
export SCHRODINGER_PYTHONPATH="/storage/projects/cns14/Utilities/schrodinger2021-4/internal/lib/python3.8/site-packages"
export PELE="/storage/projects/cns14/Utilities/PELE/PELE1.7.1/"

export LC_ALL=C; unset LANGUAGE
export I_MPI_PMI_LIBRARY=/usr/lib64/libpmi.so
export LD_LIBRARY_PATH=/storage/projects/cns14/Utilities/PELE/dependencies/boost-1.66.0/lib:$LD_LIBRARY_PATH
export SRUN=1  # this is to avoid having to set usesrun: true in input.yaml

python -m pele_platform.main input.yaml

PeleSimulationAnalysis (cluster)

Last Update: 05-07-2021

This script allows the user to create a dispersion plot of any two metrics from a PELE simulation. A third metric can be represented with a colorbar.

# Generate plot with interactive
interactive -n 1 -X

# Import modules
module purge
module load intel-oneapi
module load imkl

# Set up conda
source /shared/work/NBD_Utilities/miniconda3/etc/profile.d/conda.sh

# Activate conda environment
conda activate /shared/work/NBD_Utilities/PELE/PELE_Softwares/conda_envs/pele_analysis

# Run PELEPlot.py
python -m PELEPlot.run -i output/0/report_* -X 7 -Y 5

# Or call its helper
python -m PELEPlot.run --help

#Generate plot with interactive
interactive -n 1 -X

export PYTHONPATH=/work/NBD_Utilities/PELE/PELE_Softwares/adaptive_types/adaptive_python_2015_GNU/AdaptivePELE_1.5.1

module load Python/2.7.10-foss-2015a

python /work/NBD_Utilities/PELE/PELE_Softwares/adaptive_types/adaptive_python_2015_GNU/AdaptivePELE_1.5.1/AdaptivePELE/analysis/interactivePlot.py  5 6 8 --top topology.pdb --cpus 15 --min_clustsize 5

(5 6) columns in run_report that will be plotted, starting by 1
(8) number of adaptive steps
(--top topology.pdb) if working with .xtc format instead of .pdb


#Clustering of all the trajectories and selection of centroid representative

module load Python/2.7.10-foss-2015a

export PYTHONPATH=/work/NBD_Utilities/PELE/PELE_Softwares/adaptive_types/adaptive_python_2015_GNU/AdaptivePELE_1.5.1/

#If using xtc
python -m AdaptivePELE.analysis.clusterAdaptiveRun 200 5 6 STR --traj trajectory_ --report report_ --top topology.pdb --CA --cpus 50

#If using pdb
python -m AdaptivePELE.analysis.clusterAdaptiveRun 200 5 6 STR --traj trajectory_ --report report_ --CA --cpus 50


(200) number of clusters - decide based on plot
Output: ClusterMap.pdf
--use_pdb (only in case of having pdb files with .xtc extension)


#Generate movie of a selected trajectory:

interactive -n 1 -X

module load Python/2.7.14-intel-2018a

export PYTHONPATH=/work/NBD_Utilities/PELE/PELE_Softwares/adaptive_types/adaptive_python_2_intel/AdaptivePELE_1.5.1/

python /work/NBD_Utilities/PELE/PELE_Softwares/adaptive_types/adaptive_python_2015_GNU/AdaptivePELE_1.5.1/AdaptivePELE/analysis/backtrackAdaptiveTrajectory.py  5 123 1

(5) epoch
(123) trajectory number
(2) snapshot

#Genenerate n bestStructures from a metric

interactive -n 1 -X

module load Python/2.7.14-intel-2018a

export PYTHONPATH=/work/NBD_Utilities/PELE/PELE_Softwares/adaptive_types/adaptive_python_2_intel/AdaptivePELE_1.5.1/

python /work/NBD_Utilities/PELE/PELE_Softwares/adaptive_types/adaptive_python_2015_GNU/AdaptivePELE_1.5.1/AdaptivePELE/analysis/backtrackAdaptiveTrajectory.py  COM Distance -n 50


#Plot with Gnuplot

export PYTHONPATH=/work/NBD_Utilities/PELE/PELE_Softwares/adaptive_types/adaptive_python_2_intel/AdaptivePELE_1.5.1/

module load Python/2.7.14-intel-2018a

python /work/NBD_Utilities/PELE/PELE_Softwares/adaptive_types/adaptive_python_2_intel/AdaptivePELE_1.5.1/AdaptivePELE/analysis/plotAdaptive.py 8 5 6 run_report_ -points -zcol 4 | gnuplot -p
python /work/NBD_Utilities/PELE/PELE_Softwares/adaptive_types/adaptive_python_2_intel/AdaptivePELE_1.5.1/AdaptivePELE/analysis/plotAdaptive.py 8 5 6 run_report_ -points -traj_col | gnuplot -p

# Normal PELE

export PYTHONPATH=/work/NBD_Utilities/PELE/PELE_Softwares/adaptive_types/adaptive_python_2_intel/AdaptivePELE_1.5.1/

gnuplot

plot for [i=1:X] 'run_report_'.i.'' using 5:6 title ''.i.''

(X=number of processors in PELE)

$ #Path:
$ /shared/work/NBD_Utilities/PELE/PELE_Templates/analisis.sl

Launch with: Copy and paste the analysis part you want to run to commandline

Pele++ (cluster)

Last Update: 09-06-2021

PELE-1.7.1

#!/bin/bash
#SBATCH -J PELE_MPI
#SBATCH --output=mpi_%j.out
#SBATCH --error=mpi_%j.err
#SBATCH --ntasks=5        # Change this value to match CPUs in mpi run --ntasks=XXX
#SBATCH --mem-per-cpu=1000

##################################################################
module purge
export LC_ALL=C; unset LANGUAGE
module load intel-oneapi
module load imkl
export I_MPI_PMI_LIBRARY=/usr/lib64/libpmi.so
export LD_LIBRARY_PATH=/shared/work/NBD_Utilities/PELE/PELE_dependencies/boost-1.66.0/lib:$LD_LIBRARY_PATH
export PATH=/shared/work/NBD_Utilities/PELE/PELE_Softwares/bin/PELE1.7.1/bin:$PATH
module list
##################################################################

#CHOOSE NUMBER OF PROCESSORS TO USE i.e. XXX = 5
#srun -n XXX Pele_mpi pele.conf
srun -n 5 Pele_mpi pele.conf

$ # Path:
$ /shared/work/NBD_Utilities/PELE/PELE_Templates/PELE_mpi/run_pele-1.7.1_nbd.sl

Launch with: sbatch run_pele-1.7.1_nbd.sl

PELE-1.6

#!/bin/bash
#SBATCH -J PELE_MPI
#SBATCH --output=mpi_%j.out
#SBATCH --error=mpi_%j.err
#SBATCH --ntasks=5        # Change this value to match CPUs in mpi run --ntasks=XXX
#SBATCH --mem-per-cpu=1000

##################################################################
module purge
export LC_ALL=C; unset LANGUAGE
module load impi/2018.1.163-iccifort-2018.1.163-GCC-6.4.0-2.28 wjelement/1.3-intel-2018a
module load Crypto++/6.1.0-intel-2018a OpenBLAS/0.2.20-GCC-6.4.0-2.28
export I_MPI_PMI_LIBRARY=/usr/lib64/libpmi.so
export LD_LIBRARY_PATH=/shared/work/NBD_Utilities/PELE/PELE_Softwares/local_deps/pele_deps/boost_1_52/lib:$LD_LIBRARY_PATH
export PATH=/shared/work/NBD_Utilities/PELE/PELE_Softwares/bin/PELE1.6/bin:$PATH
module list
##################################################################

#CHOOSE NUMBER OF PROCESSORS TO USE i.e. XXX = 5
#srun -n XXX Pele_mpi pele.conf
srun -n 5 Pele_mpi pele.conf

$ # Path:
$ /shared/work/NBD_Utilities/PELE/PELE_Templates/PELE_mpi/run_pele-1.6_nbd.sl

Launch with: sbatch run_pele-1.6_nbd.sl

AI Environment (cluster)

Last Update: 02-03-2022

#!/bin/bash
#SBATCH -J ai_env
#SBATCH --output=report_%j.out
#SBATCH --error=report_%j.err
#SBATCH --ntasks=2
#SBATCH --mem-per-cpu=1000

module purge
source /shared/work/NBD_Utilities/miniconda3/etc/profile.d/conda.sh
conda activate /shared/work/NBD_Utilities/conda_envs/aienv

python ai_script.py

AnalogsSearch (Office)

Last Update: 19-03-2020

#Read the docs for more info in the possible searches and commands:
# https://danielsoler93.github.io/analogs_finder/

export PYTHONPATH=/data/software/python_scripts/analogs_finder/v1.1/
python -m analogs_finder.main database.sd query.sdf --combi_subsearch

$ # Path:
$ /shared/data-nbdcalc01/software/python_scripts/analogs_finder/analogs.shi

Launch with: bash analogs.sh

MDPocket (Office)

Last Update: 02-01-2020

 README MDpocket
 SUWIPA April 4, 2019.


1. Cpptraj to extract snapshots from the autoimage & stripwater trajectory
    #####
    parm ../run1_stripwatmembrane.top

    trajin ../run1_trajautoimage_md14-md19_pro450ns_stripwater.x 1 45000 10

    reference ../initial_stripwatmembrane.crd

    rms reference :1-296@CA,C,N,O
    trajout snapshots/snap.pdb pdb multi
    go
    #####
    Noted !! use multi command to save pdb into seperate files.


 2. To convert snap.pdb.#  to snap_#.pdb

    under directory snapshots run command:
    ls | cut -f3 -d"." | awk '{print "mv snap.pdb."$0" snap_"$0".pdb"}' | sh


 3. use python script to create mdpocket input file (containing all snap_#.pdb files)

    python createMDPocketInputFile.py /home/ssaenoon/Alm_Par/MD_PAR2/AMBERMD/cutNter/run1/Analysis/MDpocket/snapshots mdpocket_input.txt

 4. Run MDpocket using fpoket V2:
    /dist/fpocket/fpocket2/bin/mdpocket -L mdpocket_input.txt

 5. Visulize the density grid (output "mdpout_dens_grid.dx" in VMD) - adjusting the isovalue for your selected pocket to be separated.

 6. Use this python script to extract the selected_mdpoket_iso#.pdb

     usage: python extractISOPdb.py path/my_dx_file.dx outputname.pdb isovalue

     example: python extractISOPdb.py  mdpout_dens_grid.dx mdpout_dens_iso3.pdb 3

 7. In PYMOL: open file "mdpout_dens_iso3.pdb"
     in mode - selecting "molecule"  --> select your selected pocket  then save
     > Export Molecule
     > Swlwct "sele"
     > save file.pdb   eg. mdpocket_selected_pocket_iso3.pdb

 8. Then run /dist/fpocket/fpocket2/bin/mdpocket -L mdpocket_input.txt -f  mdpocket_selected_pocket_iso3.pdb -v 10000

 9. In the output file "mdpout_descriptors.txt" containing many columns, use:

    > awk '{print $1}' mdpout_descriptors.txt > snapshot.txt
    > awk '{print $2}' mdpout_descriptors.txt > volume.txt
    ...
    paste snapshot.txt volume.txt > snapshot_volume.txt

$ # Path:
$ /shared/data-nbdcalc01/software/MDpocket/mdpocket_readme_suwipa/README_MDpocket

Launch with: Follow instructions and copy paste to command line

CAVIAR (cluster)

Last Update: 08-07-2021

#!/bin/bash
#SBATCH -J caviar
#SBATCH --output=report_%j.out
#SBATCH --error=report_%j.err
#SBATCH --ntasks=1
#SBATCH --mem-per-cpu=1000

module purge
module load intel-oneapi
module load imkl

source /shared/work/NBD_Utilities/miniconda3/etc/profile.d/conda.sh
conda activate /shared/work/NBD_Utilities/miniconda3/envs/caviar

caviar -code 1dwc

PSI4 (cluster)

Last Update: 15-11-2021

#!/bin/bash
#SBATCH -J PSI4
#SBATCH --output=report_%j.out
#SBATCH --error=report_%j.err
#SBATCH --ntasks=16
#SBATCH --time=1:00:00

module purge
source /shared/work/NBD_Utilities/PSI4/psi4conda/etc/profile.d/conda.sh
conda activate

psi4 -i input.in -o output.dat -n $SLURM_NPROCS