Usage
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)
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) ```
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, )
# Performance
# Documentation
# Citation