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/