Induced fit docking

Introduction

The induced fit simulation aims to enhance docking poses by taking into account the receptor flexibility. In order to achieve this, we developed a curated side chain prediction and ANM algorithms, which were benchmarked against standard docking techniques.

Both induced fit protocols use AdaptivePELE, which enhances the exploration by iteratively running short simulations, assessing the exploration with clustering, and spawning new trajectories in the most relevant regions.

Inputs

  • protein-ligand complex PDB file

  • YAML file with parameters

Default parameters

Induced fit fast

  • adaptive iterations: 25

  • steps per iteration: 12

  • average time: 4h

Induced fit long

  • adaptive iterations: 10

  • steps per iteration: 100

  • average time: 10 - 12h

1. Complex Preparation

Prepare the system consisting of protein and docked ligand with Schrödinger Protein Preparation Wizard. We would usually recommend protonating the protein (obligatory), deleting water molecules more than 5Å away from ligands and ions as well as filling in missing loops and side chains.

Make sure the ligand has:

  • unique chain ID

  • unique PDB atom names with no spaces or single letters

  • any residue name except for UNK

2. Input Preparation

Prepare the input file input.yml:

system: 'docking2grid6n4b_thc.pdb' # Protein-ligand PDB
chain: 'L' # Ligand chain ID
resname: 'THC' # Ligand residue name
seed: 12345
# Distance between two atoms to track the simulation
atom_dist:
- "A:2:CA" # First atom (chain ID:residue number:atom name)
- "B:3:CG" # Second atom
cpus: 60
induced_fit_fast: true  # less sampling but faster (2-3 h)
#induced_fit_long: true  # 6h simulation but a lot more sampling

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

Raw output

Trajectory and report files for each simulation are located in working_folder/output. That’s where you can find detailed information on each snapshot (PDB file, binding energy, metrics, etc.).

Selected poses

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