Pele_scripts

Group of small scripts to perform pele analysis.

Main Pele's Software

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1. Protein Preparation for Pele
2. PlopRotTemp_SCHR2017
3. Adaptive PELE
4. PELE(comercial software)
5. Analysis Tools
6. Ligand Growing
7. MSM_PELE

Analysis Tools

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• bestStructs.py

  • Description:
    Parse all the reports files under the current directory and sort them all by the chosen criteria. Then, output the n best structures. Must be run from adaptive's root folder or Pele's result folder
  • Requested arguments:
    $python best_structs.py <criteria (column_number or column_name)>
    e.g.1. python /home/dsoler/best_structs.py Binding Energy.
    e.g.2. python /home/dsoler/best_structs.py 5.
    e.g.3. python /home/dsoler/best_structs.py -n 20 -f 1 -o Dani distance Note: The criteria must be one of the report's column name/number after the column numberOfAcceptedSteps.
  • Optional arguments:
    -s "max or min" ( max to order from higher to lower values, min from lower to higher) --> Default: min.
    i.e: -s max
    -f frequency the Pele's controlfile save the output --> Default:1.
    i.e: -f 4
    Note: Important in case the output save frequency of your control file is >1
    -n Structures to be outputted --> Default:10.
    i.e: -n 10
    -o Output Folder --> Default Criteria's name
    i.e: -o PRR_apo_Binding_energies
    -nm Non numerical folders --> Default: False
    i.e: -nm
  • Output:
    The script will create a folder called {criteria} or {output} if -o option. Inside that one, you will have the structures named as: traj_{epoch}.{report}.{step}{cirteria}{value}.pdb

• rangeOfValues.py

  • Description:
    Parse all the reports under the current directory and sort them all by the chosen criteria and output the value range from [users_minimum_value:users_max_value]. Must be run from adaptive's root folder or Pele's result folder
  • Requested arguments:
    $python best_structs.py <min_value> <max_value> <criteria> <br /> e.g.1. python /home/dsoler/rangeOfValues.py -50 -40 Binding Energy.
    e.g.2 python /home/dsoler/rangeOfValues.py -50 -40 5.
    e.g.3 python /home/dsoler/rangeOfValues.py -o Dani -f 1 -50 -40 5.
    Note: The criteria must be one of the report's metric names/number.
  • Optional arguments:
    -f frequency the Pele's control file save the output --> Default:1.
    i.e: -f 4
    Note: Important in case the output save frequency of your control file is >1
    -o Output Folder --> Default Criteria's name
    i.e: -o PRR_apo_Binding_energies -nm Non numerical folders --> Default: False
    i.e: -nm
  • Output:
    The script will create a folder called {criteria} or {output} if -o option. Inside that one, you will have the structures named as: traj_{epoch}.{report}.{step}{criteria}{value}.pdb

• box.py

  • Description:
    Create box of control file.
  • Requested arguments:
    $python box.py <control_file>
    e.g. python box.py pele.conf
  • Optional arguments:
    -f "file" (Output file) --> default: ./box.pdb The script will create a box.pdb with a cubic box showing pele's conformational space.
  • Output:
    The script will create a box.pdb with the exploration box inside the control file.

• plotAdaptive.py

  • Description:
    Write a string that creates a valid gnuplot command for plotting adaptive simulations. Should be run from the adaptive simulation folders

  • Requested arguments:
    $python plotAdaptive.py steps_epoch xcol ycol filename -type_plot
    e.g. python /path/plotAdaptive.py 4 2 5 report_ -rmsd

  • Optional arguments:
    --zcol Column of a metric to use as color instead of the epoch --> default: None (use epoch as color)
    --traj_range Range of trajectories to plot (has to be contigous) --> Default: plot all trajectories -points Plot using points -lines Plot using lines -traj_col Color differently each trajectory

  • Output:
    The script prints a gnuplot valid command that can be piped to gnuplot or pasted inside a gnuplot script for tweaking

• backtrackAdaptiveTrajectory.py

  • Description:
    Recreate the trajectory fragments to the led to the discovery of a snapshot, specified by the tuple (epoch, trajectory, snapshot) and write as a pdb file. Should be run from the adaptive simulation folders
  • Requested arguments:
    $python backtrackAdaptiveTrajectory.py epoch_number trajectory_number snapshot_number
    e.g. python /path/backtrackAdaptiveTrajectory.py 4 12 3
  • Optional arguments:
    -o "output_folder" (name of the folder where to store the pdb file) --> default: "" (store in current folder)
    --name Name of the file to store the trajectory --> Default: "pathway.pdb" --top Name of the pdb topology for loading non-pdb trajectories
  • Output:
    The script will create a folder (if asked via the -o option) and inside you will have the file containing the trajectory that lead to snapshot of interest. If the filename already exists, a number will be appended to distinguish it, i.e. pathway.pdb --> pathway_1.pdb

• numberOfClusters.py

  • Description:
    Plot basic information about the clustering in an adaptive simulation. Should be run from the adaptive simulation folders
  • Requested arguments:
    $python numberOfClusters.py
    e.g. python /path/numberOfClusters.py
  • Optional arguments:
    -o "output" Name of the folder where to store the plots --> default: "" (store in current folder)
    -f "filename" Name of the file to store the trajectory --> Default: "" (don't save the plots to disk)
  • Output:
    The script will generate 3 plots:
    1. The number of clusters per density value at each epoch
    2. The number of clusters per threshold value at each epoch
    3. The histogram of the cluster representatives contact ratios

• plotSpawningClusters.py

  • Description:
    Plot basic information about the spawning in an adaptive simulation. Should be run from the adaptive simulation folders
  • Requested arguments:
    $python plotSpawningClusters.py
    e.g. python /path/plotSpawningClusters.py
  • Optional arguments:
    -o "output" Name of the folder where to store the plots --> default: "" (store in current folder)
    -f "filename" Name of the file to store the trajectory --> Default: "" (don't save the plots to disk)
  • Output:
    The script will generate a plot showing the number of spawned processors from cluster categorized by their size

• writeClusteringStructures.py

  • Description:
    Write specified cluster represenatative structures as a pdb file.
  • Requested arguments:
    $python writeClusteringStructures.py clustering_object output_folder structures_to_print
    e.g. python /path/writeClusteringStructures.py 23/clustering/object.pkl cluster_struct/cluster.pdb 3 15 25 67
  • Optional arguments:
    --threshold Print all cluster structures that are below this threshold --> default: None (print strctures selected)
    
  • Output:
    The script will create a folder (if passed in the output_folder option) and inside you will have the requested pdb files. The output_folder option has to contain the name of the output files as well as the output folder (if desired), i.e. path/cluster.pdb --> cluster/cluster_1.pdb, cluster/cluster_2.pdb, cluster/cluster_3.pdb, etc.

• interactivePlot.py

  • Description:
    Plot two metrics and get the structures within the rectangle drawn with the mouse.
  • Requested arguments:
    $python interactivePlot.py column_of_report_to_be_the_xaxis column_of_report_to_be_the_yaxis pelesteps_per_epoch
    e.g. python /path/interactivePlot.py 5 6 1 (plot 5&6 with a 1 pelestep per epoch)
  • Optional arguments:
    --path Path where to find pele reports. Default: folder path
    -f frequency the Pele's control file save the output --> Default:1.
    i.e: -f 4
    Note: Important in case the output save frequency of your control file is >1
    -o Output Folder --> Default Criteria's name
    i.e: -o PRR_apo_Binding_energies
    -nm Non numerical folders --> Default: False
    i.e: -nm
  • Output:
    The script will create a folder (if passed in the output_folder option) and inside you will have the pdb files under the area selcted on the plot.

• counter.py

  • Description:
    Plot a histogram of how many steps inside each interval of the chosen metric.
  • Requested arguments:
    $python counter.py metric_column_number number_of_bins
    e.g. python /path/interactivePlot.py 6 10 (create histogram of metric number 6 with 10 intervals)
  • Optional arguments:
    -o OUT, -o OUT Output Path.
    i.e: BindingEnergies_apo
    -nm Only parse numerical folders
    i.e: -nm
    
  • Output:
    The script will create a hist.png with the histogram.