Refine docking poses by accounting with receptor flexibility

This simulation aims to enhance docking poses by having into account receptor flexibility. To do this, a curated side chain prediction and ANM algorithms were developed and benchmarked against standard docking techniques.

Article: https://www.ncbi.nlm.nih.gov/pubmed/27545443

Input (further explained below):

  • Protein-ligand.pdb

Output (further explained below):

  • Ranked binding modes

Computational time: 2h

1. Complex Preparation

Prepare the system with maestro (Protein Preparation Wizard) and output a complex.pdb. The complex.pdb must contain the docked protein-ligand.

Make sure the ligand has:

  • Unique chain

  • No atomnames with spaces or single letter

  • Any residuename except UNK

2. Input Preparation

Prepare the input file input.yml:

system: 'docking2grid6n4b_thc.pdb' #Protein ligand pdb
chain: 'L' #Ligand chain name
resname: 'THC' # Ligand residue name
seed: 12345
#Distance to track along the simulation
atom_dist:
- "A:2:CA" #First atom to make the distance to
- "B:3:CG" #Second atom to make the distance to
cpus: 60
induced_fit_fast: true #2-3h less sampling but faster
#induced_fit_exhaustive: true #6h sim but much more sampling

For more optional flags please refer to optative falgs

3. Run simulation

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

python -m pele_platform.main input.yml

4. Output

Best ranked clusters:

working_folder/results/clusters

Best ranked poses:

working_folder/results/BestStructs/