Synopsis

toolbx_pdb is a set of tools for manipulation and execution of tasks on PDB file ensembles.

Examples

Below are example of uses cases for the toolbx_pdb scripts.

Binding pocket conformation ensemble analysis

Generate an interaction fingerprints (IFP) diagram Generate an IFP diagram for all binding pockets in "complex_ensemble" directory. The complexes are ligand bound proteins. Each residue that interact with its bound ligand is listed, and the combined list of all residues is used to generate the IFP. A custom set of interaction types can be defined with the -customFprint flag.

pdb_confEns.py 'IFP_example' 'complex_ensemble' -customFprint '11111111000'

Cluster complexes by IFP Cluster the complexes based on their IFP and generate a dendrogram of that clustering. An additional template complex is added for comparison.

pdb_confEns.py 'IFP_cluster_example' 'complex_ensemble' -templatePath 'structures/pocket1.pdb' -dendro

Analyze binding pocket ensemble conformation with principal component analysis (PCA) Analyze the range of motion across the binding pocket ensemble with PCA on all binding pocket carbon alphas. A template and additional binding pocket conformation are added here for comparisons.

pdb_confEns.py 'PCA_example' 'complex_ensemble' -templatePath 'structures/pocket1.pdb' -additionalPaths 'structures/pocket2.pdb' -pca

Batch protein superimposition

Superimpose all proteins from the directory "complex_ensemble" onto the structure of "structures/pocket1.pdb"

pdb_superimpose.py "complex_ensemble" "structures/pocket1.pdb"

Batch add ligand charge

Guess the charge on the bound ligand by updating its protonation state. Proper treatment of the ligand charge is necessary for IFP calculations.

pdb_addLigand.py "complex_ensemble"

Batch get B-Factor

Display B-factor data from X-ray structure PDB files.

pdb_bfac.py "pdb_structures"

Motivation

This set of tools simplifies the analysis of a ligand-bound binding pocket ensemble. IFPs capture information of both the ligand and binding pocket conformation by focusing on their interaction pattern. PCA on binding pocket residue captures the main collective movements in a simple visual output. These scripts were developed by Thomas Coudrat during his PhD.

Contributions

Feel free to create an issue or submit a pull request. You can also contact me (Thomas Coudrat) if you have questions related to this project.

Installation

• Install Anaconda for Python 3.5 (required)
• Install OpenEye OEChem toolkit (OpenEye-python3-ubuntu-12.04-x64-2014.10.2) (optional: for interaction fingerprints)
  • set path: export PYTHONPATH="path/to/OpenEye-python3-ubuntu-12.04-x64-2014.10.2"
  • Requires software license
• Install ICM 3.8-4 (optional: for superimposition)
  • set environment variable: export ICMHOME="path/to/icmDirectory"
  • Requires software license

License

This project is licensed under the MIT license