opendock 0.0.1

Zelixir Open-Docking Framework.

OpenDock: a versitile protein-ligand docking framework with diverse scoring functions.

Aim

The project is intended to provide a open-source framework for protein-ligand docking by implementing several different traditional and machine learning scoring functions.

Installation

Before you can use the framework, you may install the following python packages:

pip install pytorch 
pip install pandas 

Install OpenDock package by using pip install:

cd opendock/
pip install . 

Or install OpenDock package by using pip install from web:

pip install opendock #(not functional by now...)

Framework architecture

Applications

Some simple examples

1. create a simple Monte Carlo based sampling strategy with Vinascore for scoring. In this example, the ligand is parsed by the LigandConformation class, and the receptor is defined by the ReceptorConformation class. The scoring function here is VinaSF, which needs the ligand object and the receptor object. Then the sampler (MonteCarloSampler) is defined by providing the ligand the receptor object as well as the scoring function object. After 100 steps of the sampling, the ligand poses are output. In this example, the lbfgs_minimizer is a minimizer function that could be used to finely control the ligand or receptor conformations guided by the scoring function object.

   from opendock.core.conformation import ReceptorConformation
   from opendock.core.conformation import LigandConformation
   from opendock.scorer.vina import VinaSF
   from opendock.scorer.deeprmsd import DeepRmsdSF, CNN, DRmsdVinaSF
   from opendock.scorer.constraints import rmsd_to_reference
   from opendock.core import io
   
   # define a flexible ligand object 
   ligand = LigandConformation(sys.argv[1])
   # define the receptor object
   receptor = ReceptorConformation(sys.argv[2], 
                                   ligand.init_heavy_atoms_coords)
   receptor.init_sidechain_cnfrs()
   
   # define scoring function
   sf = VinaSF(receptor, ligand)
   vs = sf.scoring()
   print("Vina Score ", vs)
   
   # ligand center
   xyz_center = ligand._get_geo_center().detach().numpy()[0]
   print("Ligand XYZ COM", xyz_center)
   
   # define sampler
   print("Cnfrs: ",ligand.cnfrs_, receptor.cnfrs_)
   mc = MonteCarloSampler(ligand, receptor, sf, 
                          box_center=xyz_center, 
                          box_size=[20, 20, 20], 
                          random_start=True,
                          minimizer=lbfgs_minimizer,
                          )
   init_score = mc._score(ligand.cnfrs_, receptor.cnfrs_)
   print("Initial Score", init_score)
   
   # run mc sampling
   mc._random_move()
   mc.sampling(100)
   
   # save ligand conformations
   mc.save_traj("traj_saved_100.pdb")
   

2. In the following example, a GeneticAlgorithmSampler is used for sampling. Similarly, the ligand and receptor objects are required. Here, 100 chromosomes are created and the scoring function is VinaSF class.

   from opendock.sampler.ga import GeneticAlgorithmSampler
   
   # define scoring function
   sf = VinaSF(receptor, ligand)
   vs = sf.scoring()
   print("Vina Score ", vs)
   
   # ligand center
   xyz_center = ligand._get_geo_center().detach().numpy()[0]
   print("Ligand XYZ COM", xyz_center)
   
   # initialize GA
   GA = GeneticAlgorithmSampler(ligand, receptor, sf, 
                                box_center=xyz_center, 
                                box_size=[20, 20, 20], )
   GA._initialize()
   GA.run(n_gen=4)
   
   _vars = GA.best_chrom_history[-1][1:]
   _lcnfrs, _rcnfrs = GA._variables2cnfrs(_vars)
   
   print("Last Ligand Cnfrs ", _lcnfrs)
   

3. Sometimes, it could be better to define some hybrid scoring functions for more accurate sampling and docking. In the following example, two scoring functions VinaSF and DeepRmsdSF are implemented and combined together by different weights. This scoring function (hybrid scoring function by VinaSF and DeepRmsdSF) could be used to guide pose optimization or global docking.

   # define scoring function
   sf1 = VinaSF(receptor, ligand)
   vs = sf1.scoring()
   print("Vina Score ", vs)
   
   # define scoring function
   sf2 = DeepRmsdSF(receptor, ligand)
   vs = sf2.scoring()
   print("DeepRMSD Score ", vs)
   
   # combined scoring function
   sf = HybridSF(receptor, ligand, scorers=[sf1, sf2], weights=[0.8, 0.2])
   vs = sf.scoring()
   print("HybridSF Score ", vs)
   

4. The following hybrid scoring function could be used for sampling.

   from opendock.scorer.hybrid import HybridSF
   
   # sf is the hybrid scoring function
   sf = HybridSF(receptor, ligand, scorers=[sf1, sf2], weights=[0.8, 0.2])
   
   # ligand center of the initial input ligand pose
   xyz_center = ligand._get_geo_center().detach().numpy()[0]
   print("Ligand XYZ COM", xyz_center)
   
   # define sampler
   print("Cnfrs: ",ligand.cnfrs_, receptor.cnfrs_)
   mc = MonteCarloSampler(ligand, receptor, scoring_function=sf, 
                          box_center=xyz_center, 
                          box_size=[20, 20, 20], 
                          random_start=True,
                          minimizer=lbfgs_minimizer,
                          )
   init_score = mc._score(ligand.cnfrs_, receptor.cnfrs_)
   print("Initial Score", init_score)
   

5. Atom selection example. In the following example, the heavy atom indices of residue GLU5 in chain A are determined.

   from opendock.core.asl import AtomSelection 
   
   asl = AtomSelection(molecule=receptor)
   indices = asl.select_atom(atomnames=['OE1,OE2',], chains=['A'], residx=['5'], resnames=['GLU'])
   print(indices)
   
   asl = AtomSelection(molecule=receptor)
   indices_r = asl.select_atom(atomnames=['C,O,N,CA',], chains=['A'], residx=['120-122'])
   print(indices_r, receptor.dataframe_ha_.head())
   
   asl = AtomSelection(molecule=ligand)
   indices_l = asl.select_atom(atomnames=['N2,C13',])
   print(indices_l, ligand.dataframe_ha_.head())
   
   # constraints
   cnstr = DistanceConstraintSF(receptor, ligand, 
                                grpA_ha_indices=indices_r, 
                                grpB_ha_indices=indices_l, 
                                )
   print(cnstr.scoring())
   

Performance

Documentation

Citation