def dock_ligand(pose, partners='A_X', jobs=1, job_output ,cst_file):
# 1. Setup the docking FoldTree
dock_jump = 1
setup_foldtree(pose, partners, Vector1([dock_jump]) )
# 2. Create centroid <--> fullatom conversion Movers and a Mover to
# recover sidechain conformations.
to_fullatom = SwitchResidueTypeSetMover('fa_standard')
recover_sidechains = ReturnSidechainMover(pose)
# 3. Make sure its full atom for high res docking
to_fullatom.apply(pose)
# 4. Create ScoreFunctions for centroid and fullatom docking and add
# constraints.
scorefxn_ligand = create_score_function('ligand')
set_constraints = ConstraintSetMover()
set_constraints.constraint_file(cst_file)
scorefxn_ligand.set_weight(atom_pair_constraint,1.5)
set_constraints.apply(pose)
# 5. Create a test pose
test_pose = Pose()
# 6. Setup the DockingProtocol
docking = DockMCMProtocol()
docking.set_scorefxn(scorefxn_ligand)
# 7. Setup the PyJobDistributor
jd = PyJobDistributor(job_output, jobs, scorefxn_ligand)
counter = 0
while (not jd.job_complete):
# A. Set necessary variables for this trajectory
test_pose.assign(pose)
counter+=1
test_pose.pdb_info().name(job_output+'_'+str(counter))
# B. Perform docking
docking.apply(test_pose)
# C. Output the decoy structure.
jd.output_decoy(test_pose)
return
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