Large protein conformational moves with PCA-PELE

Introduction

This package aims to analyse and enhance the receptor movement by performing the principal component analysis (PCA) on three or more trajectory snapshots, which is especially useful when the protein undergoes big conformational changes. The best binding modes of the simulated ligand are ranked and extracted for user inspection.

Inputs

  • protein-ligand PDB file

  • 3 identical PDB files (only changing coordinates) which define a motion

  • YAML file with parameters

Default parameters

Exact parameters depend on the selected simulation type, the PCA only enhances the receptor movements.

Computational time: 5h

1. Complex Preparation

Prepare the system with Maestro (Protein Preparation Wizard) and output a complex.pdb. The file must contain the protein-ligand in the desired initial configuration.

Make sure the ligand has:

  • unique chain ID

  • no atom names with spaces or single letter

  • any residue name except UNK

2. Input Preparation

Prepare the input file input.yml:

system: 'complex.pdb' # protein-ligand complex
chain: 'L' # chain name of the ligand
resname: 'LIG' # residue name of the ligand
seed: 12345

# Calculate PCA by giving 3 pdb snapshots
# defining a motion
pca_traj:
- "snap1.pdb"
- "snap2.pdb"
- "snap3.pdb"
# Do not constraint structure
remove_constraints: true
# Big sampling of the receptor while making binding
spawning: independent
out_in: true #You can change the method to any other (induced_fit, rescoring...)

For more optional flags please refer to optional flags.

3. Run simulation

To run the system launch the simulation with the following command:

python -m pele_platform.main input.yml

4. Output

Clusters

Upon completion of the simulation, all trajectories are clustered based on ligand heavy atom coordinates. Then, a cluster representative with the best binding energy (or metric of your choice) is selected. Ranked cluster representatives can be found in:

working_folder/results/clusters

Best snapshots

In addition, top 100 structures with the best binding energy (or metric of your choice) are retrieved. This is done to ensure the clustering algorithm did not skip any valuable results. They are stored in:

working_folder/results/top_poses