def sample_dna_interface(pdb_filename,
partners,
jobs=1,
job_output='dna_output'):
"""
Performs DNA-protein docking using Rosetta fullatom docking (DockingHighRes)
on the DNA-protein complex in <pdb_filename> using the relative chain
<partners> .
<jobs> trajectories are performed with output structures named
<job_output>_(job#).pdb.
"""
# 1. creates a pose from the desired PDB file
pose = Pose()
pose_from_file(pose, pdb_filename)
# 2. setup the docking FoldTree
# using this method, the jump number 1 is automatically set to be the
# inter-body jump
dock_jump = 1
# the exposed method setup_foldtree takes an input pose and sets its
# FoldTree to have jump 1 represent the relation between the two docking
# partners, the jump points are the residues closest to the centers of
# geometry for each partner with a cutpoint at the end of the chain,
# the second argument is a string specifying the relative chain orientation
# such as "A_B" of "LH_A", ONLY TWO BODY DOCKING is supported and the
# partners MUST have different chain IDs and be in the same pose (the
# same PDB), additional chains can be grouped with one of the partners,
# the "_" character specifies which bodies are separated
# the third argument...is currently unsupported but must be set (it is
# supposed to specify which jumps are movable, to support multibody
# docking...but Rosetta doesn't currently)
# the FoldTrees setup by this method are for TWO BODY docking ONLY!
protocols.docking.setup_foldtree(pose, partners, Vector1([dock_jump]))
# 3. create a copy of the pose for testing
test_pose = Pose()
test_pose.assign(pose)
# 4. create ScoreFunctions for centroid and fullatom docking
scorefxn = create_score_function('dna')
scorefxn.set_weight(core.scoring.fa_elec, 1) # an "electrostatic" term
#### global docking, a problem solved by the Rosetta DockingProtocol,
#### requires interface detection and refinement
#### as with other protocols, these tasks are split into centroid (interface
#### detection) and high-resolution (interface refinement) methods
#### without a centroid representation, low-resolution DNA-protein
#### prediction is not possible and as such, only the high-resolution
#### DNA-protein interface refinement is available
#### WARNING: if you add a perturbation or randomization step, the
#### high-resolution stages may fail (see Changing DNA Docking
#### Sampling below)
#### a perturbation step CAN make this a global docking algorithm however
#### the rigid-body sampling preceding refinement will require EXTENSIVE
#### sampling to produce accurate results and this algorithm spends most
#### of its effort in refinement (which may be useless for the predicted
#### interface)
# 5. setup the high resolution (fullatom) docking protocol (DockMCMProtocol)
# ...as should be obvious by now, Rosetta applications have no central
# standardization, the DockingProtocol object can be created and
# applied to perform Rosetta docking, many of its options and settings
# can be set using the DockingProtocol setter methods
# as there is currently no centroid representation of DNA in the chemical
# database, the low-resolution docking stages are not useful for
# DNA docking
# instead, create an instance of just the high-resolution docking stages
docking = protocols.docking.DockMCMProtocol()
docking.set_scorefxn(scorefxn)
# 6. setup the PyJobDistributor
jd = PyJobDistributor(job_output, jobs, scorefxn)
# 7. setup a PyMOL_Observer (optional)
# the PyMOL_Observer object owns a PyMOLMover and monitors pose objects for
# structural changes, when changes are detected the new structure is
# sent to PyMOL
# fortunately, this allows investigation of full protocols since
# intermediate changes are displayed, it also eliminates the need to
# manually apply the PyMOLMover during a custom protocol
# unfortunately, this can make the output difficult to interpret (since you
# aren't explicitly telling it when to export) and can significantly slow
# down protocols since many structures are output (PyMOL can also slow
# down if too many structures are provided and a fast machine may
# generate structures too quickly for PyMOL to read, the
# "Buffer clean up" message
# uncomment the line below to use the PyMOL_Observer
## AddPyMOLObserver(test_pose, True)
# 8. perform protein-protein docking
counter = 0 # for pretty output to PyMOL
while not jd.job_complete:
# a. set necessary variables for this trajectory
# -reset the test pose to original (centroid) structure
test_pose.assign(pose)
# -change the pose name, for pretty output to PyMOL
counter += 1
test_pose.pdb_info().name(job_output + '_' + str(counter))
# b. perform docking
docking.apply(test_pose)
# c. output the decoy structure:
# to PyMOL
test_pose.pdb_info().name(job_output + '_' + str(counter) + '_fa')
# to a PDB file
jd.output_decoy(test_pose)
def sample_refinement(pdb_filename,
kT=1.0,
smallmoves=3,
shearmoves=5,
backbone_angle_max=7,
cycles=9,
jobs=1,
job_output='refine_output'):
"""
Performs fullatom structural refinement on the input <pdb_filename> by
perturbing backbone torsion angles with a maximum perturbation of
<backbone_angle_max> for <cycles> trials of
<smallmoves> perturbations of a random residue's phi or psi and
<shearmoves> perturbations of a random residue's phi and the preceding
residue's psi followed by gradient based backbone torsion angle
minimization and sidechain packing with an acceptance criteria scaled
by <kT>. <jobs> trajectories are performed, continually exporting
structures to a PyMOL instance.
Output structures are named <job_output>_(job#).pdb.
"""
# 1. create a pose from the desired PDB file
pose = Pose()
pose_from_file(pose, pdb_filename)
# 2. create a reference copy of the pose in fullatom
starting_pose = Pose()
starting_pose.assign(pose)
# 3. create a standard ScoreFunction
#### implement the desired ScoreFunction here
scorefxn = get_fa_scorefxn() # create_score_function('standard')
#### If you wish to use the ClassRelax protocol, uncomment the following
#### line and comment-out the protocol setup below
#refinement = protocols.relax.ClassicRelax( scorefxn )
#### Setup custom high-resolution refinement protocol
#### backbone refinement protocol
# 4. create a MoveMap, all backbone torsions free
movemap = MoveMap()
movemap.set_bb(True)
# 5. create a SmallMover
# a SmallMover perturbs a random (free in the MoveMap) residue's phi or psi
# torsion angle for an input number of times and accepts of rejects this
# change based on the Metropolis Criteria using the "rama" ScoreType and
# the parameter kT
# set the maximum angle to backbone_angle_max, apply it smallmoves times
smallmover = protocols.simple_moves.SmallMover(movemap, kT, smallmoves)
# angle_max is secondary structure dependent, however secondary structure
# has not been evaulated in this protocol, thus they are all set
# to the same value0
smallmover.angle_max(backbone_angle_max) # sets all at once
#### use the overloaded version of the SmallMover.angle_max method if you
#### want to use secondary structure biased moves
#smallmover.angle_max('H', backbone_angle_max)
#smallmover.angle_max('E', backbone_angle_max)
#smallmover.angle_max('L', backbone_angle_max)
# 6. create a ShearMover
# a ShearMover is identical to a SmallMover except that the angles perturbed
# are instead a random (free in the MoveMap) residue's phi and the
# preceding residue's psi, this reduces the downstream structural change
# set the maximum angle to backbone_angle_max, apply it shearmoves times
shearmover = protocols.simple_moves.ShearMover(movemap, kT, shearmoves)
# same angle_max restictions as SmallMover
shearmover.angle_max(backbone_angle_max)
#### use the overloaded version of the SmallMover.angle_max method if you
#### want to use secondary structure biased moves
#shearmover.angle_max('H', backbone_angle_max)
#shearmover.angle_max('E', backbone_angle_max)
#shearmover.angle_max('L', backbone_angle_max)
# 7. create a MinMover, for backbone torsion minimization
minmover = protocols.minimization_packing.MinMover()
minmover.movemap(movemap)
minmover.score_function(scorefxn)
#### sidechain refinement protocol, simple packing
# 8. setup a PackRotamersMover
to_pack = standard_packer_task(starting_pose)
to_pack.restrict_to_repacking() # prevents design, packing only
to_pack.or_include_current(True) # considers the original sidechains
packmover = protocols.minimization_packing.PackRotamersMover(
scorefxn, to_pack)
#### assess the new structure
# 9. create a PyMOLMover
pymover = PyMOLMover()
# uncomment the line below to load structures into successive states
#pymover.keep_history(True)
#### the PyMOLMover slows down the protocol SIGNIFICANTLY but provides
#### very informative displays
#### the keep_history flag (when True) tells the PyMOLMover to store new
#### structures into successive states, for a single trajectory, this
#### allows you to see intermediate changes (depending on where the
#### PyMOLMover is applied), when using a JobDistributor or otherwise
#### displaying multiple trajectories with a single protocol, the output
#### can get confusing to interpret, by changing the pose's PDBInfo.name
#### the structure will load into a new PyMOL state
#### try uncommenting the lines below to see different output
#pymover.update_energy(True) # see the total score in color
# 10. export the original structure, and scores, to PyMOL
pymover.apply(pose)
scorefxn(pose)
pymover.send_energy(pose)
# 11. setup a RepeatMover on a TrialMover of a SequenceMover (wow!)
# -setup a TrialMover
# a. create a SequenceMover of the previous moves
#### add any other moves you desire
combined_mover = SequenceMover()
combined_mover.add_mover(smallmover)
combined_mover.add_mover(shearmover)
combined_mover.add_mover(minmover)
combined_mover.add_mover(packmover)
#### explore the protocol using the PyMOLMover, try viewing structures
#### before they are accepted or rejected
combined_mover.add_mover(pymover)
# b. create a MonteCarlo object to define success/failure
mc = MonteCarlo(pose, scorefxn, kT) # must reset for each trajectory!
# c. create the TrialMover
trial = TrialMover(combined_mover, mc)
#### explore the protocol using the PyMOLMover, try viewing structures
#### after acceptance/rejection, comment-out the lines below
#original_trial = TrialMover(combined_mover, mc)
#trial = SequenceMover()
#trial.add_mover(original_trial)
#trial.add_mover(pymover)
#### for each trajectory, try cycles number of applications
# -create the RepeatMover
refinement = RepeatMover(trial, cycles)
####
# 12. create a (Py)JobDistributor
jd = PyJobDistributor(job_output, jobs, scorefxn)
jd.native_pose = starting_pose
# 13. store the score evaluations for output
# printing the scores as they are produced would be difficult to read,
# Rosetta produces a lot of verbose output when running
scores = [0] * (jobs + 1)
scores[0] = scorefxn(starting_pose)
# 14. perform the refinement protocol
counter = 0 # for exporting to PyMOL
while not jd.job_complete:
# a. set necessary variables for the new trajectory
# -reload the starting pose
pose.assign(starting_pose)
# -change the pose's PDBInfo.name, for the PyMOLMover
counter += 1
pose.pdb_info().name(job_output + '_' + str(counter))
# -reset the MonteCarlo object (sets lowest_score to that of p)
mc.reset(pose)
#### if you create a custom protocol, you may have additional
#### variables to reset, such as kT
#### if you create a custom protocol, this section will most likely
#### change, many protocols exist as single Movers or can be
#### chained together in a sequence (see above) so you need
#### only apply the final Mover
# b. apply the refinement protocol
refinement.apply(pose)
####
# c. output the lowest scoring decoy structure for this trajectory
# -recover and output the decoy structure to a PDB file
mc.recover_low(pose)
jd.output_decoy(pose)
# -export the final structure to PyMOL for each trajectory
pose.pdb_info().name(job_output + '_' + str(counter) + '_final')
pymover.apply(pose)
pymover.send_energy(pose) # see the total score in color
# -store the final score for this trajectory
scores[counter] = scorefxn(pose)
# 15. output the score evaluations
print('Original Score\t:\t', scores[0])
for i in range(1, len(scores)): # print out the job scores
print(job_output + '_' + str(i) + '\t:\t', scores[i])
return scores # for other protocols
def sample_folding(sequence,
long_frag_filename,
long_frag_length,
short_frag_filename,
short_frag_length,
kT=3.0,
long_inserts=1,
short_inserts=3,
cycles=40,
jobs=1,
job_output='fold_output'):
"""
Performs
exporting structures to a PyMOL instance
Output structures are named <job_output>_(job#).pdb
"""
# 1. create a pose from the desired sequence (fullatom)
# the method pose_from_sequence produces a complete IDEALIZED
# protein conformation of the input sequence, the ResidueTypeSet (second
# argument below) may be varied, and this method supports non-proteogenic
# chemistry (though it is still a Rosetta Residue). however this syntax
# is more involved and not robust to user errors, and not presented here
# small differences in bond lengths and bond angles WILL change the results,
#### if you desire an alternate starting conformation, alter steps
#### 1. and 2. as you please
pose = pose_from_sequence(sequence, 'fa_standard')
# 2. linearize the pose by setting backbone torsions to large values
# the method make_pose_from_sequence does not create the new pose's
# PDBInfo object, so its done here, without it an error occurs later
pose.pdb_info(rosetta.core.pose.PDBInfo(pose.total_residue()))
for i in range(1, pose.total_residue() + 1):
pose.set_omega(i, 180)
pose.set_phi(i, -150) # reasonably straight
pose.set_psi(i, 150)
#### if you want to see the decoy scores, the PDBInfo needs these lines
#pose.pdb_info().chain(i, 'A') # necessary to color by score
#pose.pdb_info().number(i, i) # for PDB numbering
####
# 3. create a (fullatom) reference copy of the pose
test_pose = Pose()
test_pose.assign(pose)
test_pose.pdb_info().name('linearized pose')
# 4. create centroid <--> fullatom conversion Movers
to_centroid = SwitchResidueTypeSetMover('centroid')
# centroid Residue objects, of amino acids, have all their sidechain atoms
# replaced by a single representative "atom" to speed up calculations
to_fullatom = SwitchResidueTypeSetMover('fa_standard')
# 5. convert the poses to centroid
to_centroid.apply(pose)
to_centroid.apply(test_pose)
# 6. create the MoveMap, all backbone torsions free
movemap = MoveMap()
movemap.set_bb(True)
# minimizing the centroid chi angles (the sidechain centroid atoms) is
# almost always USELESS since this compression is performed for speed,
# not accuracy and clashes usually occur when converting to fullatom
# 7. setup the ClassicFragmentMovers
# for the long fragments file
# this "try--except" is used to catch improper fragment files
try:
fragset_long = core.fragment.ConstantLengthFragSet(
long_frag_length, long_frag_filename)
#### the ConstantLengthFragSet is overloaded, this same
#### ConstantLengthFragSet can be obtained with different syntax
# to obtain custom fragments, see Generating Fragment Files below
except:
raise IOError('Make sure long_frag_length matches the fragments in\n\
long_frag_file and that long_frag_file is valid')
long_frag_mover = protocols.simple_moves.ClassicFragmentMover(
fragset_long, movemap)
# and for the short fragments file
# this "try--except" is used to catch improper fragment files
try:
fragset_short = core.fragment.ConstantLengthFragSet(
short_frag_length, short_frag_filename)
except:
raise IOError('Make sure short_frag_length matches the fragments in\n\
short_frag_file and that short_frag_file is valid')
short_frag_mover = protocols.simple_moves.ClassicFragmentMover(
fragset_short, movemap)
# 8. setup RepeatMovers for the ClassicFragmentMovers
insert_long_frag = protocols.moves.RepeatMover(long_frag_mover,
long_inserts)
insert_short_frag = protocols.moves.RepeatMover(short_frag_mover,
short_inserts)
# 9. create a PyMOL_Observer for exporting structures to PyMOL (optional)
# the PyMOL_Observer object owns a PyMOLMover and monitors pose objects for
# structural changes, when changes are detected the new structure is
# sent to PyMOL
# fortunately, this allows investigation of full protocols since
# intermediate changes are displayed, it also eliminates the need to
# manually apply the PyMOLMover during a custom protocol
# unfortunately, this can make the output difficult to interpret (since you
# aren't explicitly telling it when to export) and can significantly slow
# down protocols since many structures are output (PyMOL can also slow
# down if too many structures are provided and a fast machine may
# generate structures too quickly for PyMOL to read, the
# "Buffer clean up" message
# uncomment the line below to use PyMOL_Observer
## AddPyMOLObserver(test_pose, True)
# 10. create ScoreFunctions
# for low-resolution, centroid, poses necessary for the TrialMover's
# MonteCarlo object (see below)
scorefxn_low = create_score_function('score3')
# for high-resolution, fullatom, poses necessary for scoring final output
# from the PyJobDistributor (see below)
scorefxn_high = get_fa_scorefxn(
) # create_score_function('standard', 'score12')
# 11. setup a RepeatMover on a TrialMover of a SequenceMover
# -setup a TrialMover
# a. create a SequenceMover of the fragment insertions
#### add any other moves you desire
folding_mover = protocols.moves.SequenceMover()
folding_mover.add_mover(insert_long_frag)
folding_mover.add_mover(insert_short_frag)
# b. create a MonteCarlo object to define success/failure
# must reset the MonteCarlo object for each trajectory!
mc = MonteCarlo(test_pose, scorefxn_low, kT)
# c. create the TrialMover
trial = TrialMover(folding_mover, mc)
#### for each trajectory, try cycles number of applications
# -create the RepeatMover
folding = protocols.moves.RepeatMover(trial, cycles)
# 12. create a (Py)JobDistributor
jd = PyJobDistributor(job_output, jobs, scorefxn_high)
# 13. store the score evaluations for output
# printing the scores as they are produced would be difficult to read,
# Rosetta produces a lot of verbose output when running
scores = [0] * (jobs + 1)
scores[0] = scorefxn_low(pose)
# 14. perform folding by
counter = 0 # for exporting to PyMOL
while not jd.job_complete:
# a. set necessary variables for the new trajectory
# -reload the starting pose
test_pose.assign(pose)
# -change the pose's PDBInfo.name, for the PyMOL_Observer
counter += 1
test_pose.pdb_info().name(job_output + '_' + str(counter))
# -reset the MonteCarlo object (sets lowest_score to that of test_pose)
mc.reset(test_pose)
#### if you create a custom protocol, you may have additional
#### variables to reset, such as kT
#### if you create a custom protocol, this section will most likely
#### change, many protocols exist as single Movers or can be
#### chained together in a sequence (see above) so you need
#### only apply the final Mover
# b. apply the refinement protocol
folding.apply(test_pose)
####
# c. export the lowest scoring decoy structure for this trajectory
# -recover the lowest scoring decoy structure
mc.recover_low(test_pose)
# -store the final score for this trajectory
scores[counter] = scorefxn_low(test_pose)
# -convert the decoy to fullatom
# the sidechain conformations will all be default,
# normally, the decoys would NOT be converted to fullatom before
# writing them to PDB (since a large number of trajectories would
# be considered and their fullatom score are unnecessary)
# here the fullatom mode is reproduced to make the output easier to
# understand and manipulate, PyRosetta can load in PDB files of
# centroid structures, however you must convert to fullatom for
# nearly any other application
to_fullatom.apply(test_pose)
# -guess what cysteines are involved in disulfide bridges
guess_disulfides(test_pose)
# -output the fullatom decoy structure into a PDB file
jd.output_decoy(test_pose)
# -export the final structure to PyMOL
test_pose.pdb_info().name(job_output + '_' + str(counter) + '_fa')
#### if you want to see the decoy scores, uncomment the line below
#scorefxn_high( test_pose )
# 15. output the score evaluations
print('===== Centroid Scores =====')
print('Original Score\t:\t', scores[0])
for i in range(1, len(scores)): # print out the job scores
# the "[:14].ljust(14)" is to force the text alignment
print( (job_output + '_' + str( i ))[:14].ljust(14) +\
'\t:\t', scores[i] )
return scores # for other protocols
def sample_single_loop_modeling(pdb_filename,
loop_begin,
loop_end,
loop_cutpoint,
frag_filename,
frag_length,
outer_cycles_low=2,
inner_cycles_low=5,
init_temp_low=2.0,
final_temp_low=0.8,
outer_cycles_high=5,
inner_cycles_high=10,
init_temp_high=2.2,
final_temp_high=0.6,
jobs=1,
job_output='loop_output'):
"""
Performs simple single loop construction on the input <pdb_filename>
with loop from <loop_begin> to <loop_end> with a
cutpoint at <loop_cutpoint> using fragments of length <frag_length>
in the file <frag_filename>. <jobs> trajectories are performed,
each using a low resolution (centroid) simulated annealing with
<outer_cycles> rounds and <inner_cycles> steps per round decrementing
"temperature" from <init_temp> to <final_temp> geometrically.
Output structures are named <job_output>_(job#).pdb.
"""
# 1. create a pose from the desired PDB file
p = Pose()
pose_from_file(p, pdb_filename)
# 2. create a reference copy of the pose in fullatom
starting_p = Pose()
starting_p.assign(p)
#### if you are constructing multiple loops simultaneously, changes will
#### occur in most of the steps below
# 3. create the Loop object
# (note: Loop objects merely specify residues, they contain no
# conformation data)
my_loop = protocols.loops.Loop(loop_begin, loop_end, loop_cutpoint)
#### if using multiple loops, add additional Loop objects
# 4. use the Loop to set the pose FoldTree
protocols.loops.set_single_loop_fold_tree(p, my_loop)
#### alternate FoldTree setup, if you uncomment the lines below,
#### comment-out the set_single_loop_foldtree line above (line 189)
#### -create an empty FoldTree
#ft = FoldTree()
#### -make it a single edge the length of pose
#ft.simple_tree(p.total_residue())
#### -insert a jump corresponding to the single loop region
#ft.add_jump(loop_begin - 2, loop_end + 2, loop_cutpoint)
#### -give the pose this FoldTree (set it to this object), this will
#### erase any previous FoldTree held by the pose
#p.fold_tree(ft)
#### there is also a fold_tree_from_loops method in exposed which sets up
#### a FoldTree but it is different from set_single_loop_foldtree in
#### that is creates jumps +/- 1 residue from their corresponding loop
#### endpoints and requires a third argument, the FoldTree to setup
# 5. sets the cut-point residues as cut-point variants
protocols.loops.add_single_cutpoint_variant(p, my_loop)
# 6. create the MoveMap, allow the loop region backbone and
# all chi torsions to be free
movemap = MoveMap()
movemap.set_bb_true_range(loop_begin, loop_end)
movemap.set_chi(True) # sets all chi torsions free
# 7. setup the fragment Mover
# this "try--except" is used to catch improper fragment files
try:
fragset = core.fragment.ConstantLengthFragSet(frag_length,
frag_filename)
#### the ConstantLengthFragSet is overloaded, this same
#### ConstantLengthFragSet can be obtained with different syntax
# to obtain custom fragments, see Generating Fragment Files below
except:
raise IOError('Make sure frag_length matches the fragments in\n\
frag_file and that frag_file is valid')
fragment_mover = protocols.simple_moves.ClassicFragmentMover(
fragset, movemap)
# 8. create a Mover for loop modeling using CCD (low resolution)
ccd_closure = protocols.loops.loop_closure.ccd.CCDLoopClosureMover(
my_loop, movemap)
# 9. create ScoreFunctions
# for centroid, use the default centroid ScoreFunction with chainbreak on
scorefxn_low = create_score_function('cen_std')
# the chainbreak ScoreType exists to penalize broken bonds
# try creating a broken pose in the interpreter and use a ScoreFunction
# with a chainbreak score to investigate its impact, the score is 0.0
# except when a bond is broken
# this penalizes failures caused by CCD failing to close the loop
scorefxn_low.set_weight(core.scoring.chainbreak, 1)
# for fullatom, used for packing and scoring final output
scorefxn_high = get_fa_scorefxn(
) # create_score_function_ws_patch('standard', 'score12')
# 10. setup sidechain packing Mover
task_pack = core.pack.task.TaskFactory.create_packer_task(starting_p)
task_pack.restrict_to_repacking() # prevents design, packing only
task_pack.or_include_current(True) # considers original sidechains
pack = protocols.minimization_packing.PackRotamersMover(
scorefxn_high, task_pack)
# 11. setup the high resolution refinement
# by creating a Loops object,
# (note: Loops is basically a list of Loop objects),
sample_loops = protocols.loops.Loops()
# giving it the loop to remodel,
sample_loops.add_loop(my_loop)
# and creating a fullatom CCD Mover (high resolution)
# this Mover is somewhat abnormal since it handles everything itself, it:
# -creates its own MoveMap for the loop regions
# -creates its own ScoreFunction (default to get_fa_scorefxn())
# -creates its own FoldTree for the pose based on the loops
# -creates its own MonteCarlo object for monitoring the pose
# -performs "simulated annealing" with 3 outer cycles and 90 inner
# cycles, very similar to the protocol outlined ere
# -creates its own backbone Movers (SmallMover, ShearMover)
# -creates its own PackRotamersMover, it does NOT restrict repacking
# to the loop regions and can alter all sidechain conformations
loop_refine = LoopMover_Refine_CCD(sample_loops)
# some of these parameters or objects can be set but the protocol
# executed by this Mover is effectively untouchable
#loop_refine.set_score_function(scorefxn_high) # in beta v2 and above
loop_refine.temp_initial(init_temp_high)
loop_refine.temp_final(init_temp_high)
loop_refine.outer_cycles(outer_cycles_high)
loop_refine.max_inner_cycles(inner_cycles_high)
# 12. create centroid <--> fullatom conversion Movers
to_centroid = SwitchResidueTypeSetMover('centroid')
to_fullatom = SwitchResidueTypeSetMover('fa_standard')
# and a Mover to recover sidechain conformations
# when a protocol samples backbone torsion space in centroid,
# the sidechain conformations are neglected, when it is transferred
# to fullatom, we typically set the sidechain conformations to their
# "original" values and perform sidechain packing,
# a ReturnSidechainMover saves a pose's sidechains (in this case
# staring_pose) and when applied, inserts these conformations
# into the input pose
recover_sidechains = protocols.simple_moves.ReturnSidechainMover(
starting_p)
# 13. create a reference copy of the pose in centroid
# the first stage of each trajectory is in centroid
# so a centroid reference is needed and the pose must start in centroid
to_centroid.apply(p)
starting_p_centroid = Pose()
starting_p_centroid.assign(p)
# 14. create the geometric "temperature" increment for simulated annealing
gamma = pow((final_temp_low / init_temp_low),
(1.0 / (outer_cycles_low * inner_cycles_low)))
# 15. create a PyMOLMover for exporting structures to PyMOL
pymov = PyMOLMover()
# uncomment the line below to load structures into successive states
#pymov.keep_history(True)
scorefxn_high(starting_p) # for exporting the scores
pymov.apply(starting_p)
pymov.send_energy(starting_p)
# 16. create a (Py)JobDistributor
# a PyJobDistributor uses the job_output argument to name all output files
# and performs the specified number (int) of jobs
# a ScoreFunction is required since the PyJobDistributor output .fasc file
# contains scoring information about each output PDB
jd = PyJobDistributor(job_output, jobs, scorefxn_high)
jd.native_pose = starting_p
# 17. perform the loop modeling protocol
counter = 0 # for exporting to PyMOL
while not jd.job_complete:
# a. set necessary variables for the new trajectory
# -reload the starting pose (centroid)
p.assign(starting_p_centroid)
# -change the pose's PDBInfo.name, for exporting to PyMOL
counter += 1
p.pdb_info().name(job_output + '_' + str(counter) + '_cen')
# -reset the starting "temperature" (to init_temp)
kT = init_temp_low
# -create a MonteCarlo object for this trajectory
# a MonteCarlo object assesses pass/fail by the Metropolis Criteria
# and also records information on the lowest scoring pose
mc = MonteCarlo(p, scorefxn_low, kT)
# b. "randomize" the loop
#### this section may change if you intend to use multiple loops or
#### alter the sampling method to "randomize" the loop
# -by breaking it open,
for i in range(loop_begin, loop_end + 1):
p.set_phi(i, -180)
p.set_psi(i, 180)
pymov.apply(p)
# -and then inserting fragments
# the number of insertions performed is somewhat arbitrary
for i in range(loop_begin, loop_end + 1):
fragment_mover.apply(p)
pymov.apply(p)
####
# low resolution loop modeling:
# c. simulated annealing incrementing kT geometrically
# from init_temp to final_temp
#### this section may change if you intend to use multiple loops or
#### alter the sampling method for low resolution modeling
for i in range(1, outer_cycles_low + 1):
# -start with the lowest scoring pose
mc.recover_low(p) # loads mc's lowest scoring pose into p
# -take several steps of in the simulated annealing by
for j in range(1, inner_cycles_low + 1):
# >increasing the "temperature"
kT = kT * gamma
mc.set_temperature(kT)
# >inserting a fragment,
fragment_mover.apply(p)
pymov.apply(p)
# >performing CCD,
ccd_closure.apply(p)
pymov.apply(p)
# >and assessing the Metropolis Criteria
mc.boltzmann(p)
####
# the LoopMover_Refine_CCD makes A LOT of moves, DO NOT expect to
# see useful results if you use the PyMOLMover keep_history option, the large
# number of intermediates will slow processing to a halt
# d. convert the best structure (lowest scoring) into fullatom by:
# -recovering the best (centroid) structure (lowest scoring),
mc.recover_low(p) # loads mc's lowest scoring pose into p
# -switching the ResidueTypeSet to fullatom (from centroid),
to_fullatom.apply(p)
# -recovering the original sidechain conformations,
recover_sidechains.apply(p)
# -and packing the result (since the backbone conformation has changed)
pack.apply(p)
pymov.apply(p)
p.pdb_info().name(job_output + '_' + str(counter) + '_fa')
# high-resolution refinement:
#### this section may change if you intend to use multiple loops or
#### alter the sampling method for high resolution refinement
# e. apply the LoopMover_Refine_CCD
loop_refine.apply(p)
# f. output the decoy (pose result from this trajectory)
# include the loop RMSD (Lrsmd)
# -output a PDB file using the PyJobDistributor
lrms = protocols.loops.loop_rmsd(p, starting_p, sample_loops, True)
jd.additional_decoy_info = ' Lrmsd: ' + str(lrms)
jd.output_decoy(p)
# -export the structure to PyMOL
pymov.apply(p)
pymov.send_energy(p)
sf,
2, # Fc-FcR interface JUMP_NUM
jd.current_num,
GlycanModelProtocol.metrics_dump_dir,
input_args.utility_dir,
MC_acceptance_rate = GlycanModelProtocol.mc_acceptance,
native_constraint_file = GlycanModelProtocol.constraint_file )
except:
metrics = ''
pass
# add the metric data to the .fasc file
jd.additional_decoy_info = metrics
# dump the decoy
jd.output_decoy( working_pose )
# zip up the decoy pose, if desired
if input_args.zip_decoy:
os.popen( "gzip %s" %working_pose.pdb_info().name() )
# increment the decoy number counter
cur_decoy_num += 1
#for res_num in native_Fc_glycan_nums_except_core_GlcNAc:
# print "Res num", res_num, "Reset phi:", GlycanModelProtocol.reset_pose.phi( res_num ), "end phi:", working_pose.phi( res_num )
# print "Res num", res_num, "Reset psi:", GlycanModelProtocol.reset_pose.psi( res_num ), "end psi:", working_pose.psi( res_num )
# print "Res num", res_num, "Reset omega:", GlycanModelProtocol.reset_pose.omega( res_num ), "end omega:", working_pose.omega( res_num )
# print
# move the lowest E pack and minimized native structure into the lowest_E_structs dir
fasc_filename = decoy_name + ".fasc"
def sample_docking(pdb_filename, partners,
translation = 3.0, rotation = 8.0,
jobs = 1, job_output = 'dock_output'):
"""
Performs protein-protein docking using the Rosetta standard DockingProtocol
on the proteins in <pdb_filename> using the relative chain
<partners> with an initial perturbation using <translation>
Angstroms and <rotation> degrees. <jobs> trajectories are performed
with output structures named <job_output>_(job#).pdb.
structures are exported to a PyMOL instance.
"""
# 1. creates a pose from the desired PDB file
pose = Pose()
pose_from_file(pose, pdb_filename)
# 2. setup the docking FoldTree
# using this method, the jump number 1 is automatically set to be the
# inter-body jump
dock_jump = 1
# the exposed method setup_foldtree takes an input pose and sets its
# FoldTree to have jump 1 represent the relation between the two docking
# partners, the jump points are the residues closest to the centers of
# geometry for each partner with a cutpoint at the end of the chain,
# the second argument is a string specifying the relative chain partners
# such as "A_B" of "LH_A", ONLY TWO BODY DOCKING is supported and the
# partners MUST have different chain IDs and be in the same pose (the
# same PDB), additional chains can be grouped with one of the partners,
# the "_" character specifies which bodies are separated
# the third argument...is currently unsupported but must be set (it is
# supposed to specify which jumps are movable, to support multibody
# docking...but Rosetta doesn't currently)
# the FoldTrees setup by this method are for TWO BODY docking ONLY!
protocols.docking.setup_foldtree(pose, partners, Vector1([dock_jump]))
# 3. create centroid <--> fullatom conversion Movers
to_centroid = SwitchResidueTypeSetMover('centroid')
to_fullatom = SwitchResidueTypeSetMover('fa_standard')
# and a Mover to recover sidechain conformations
# when a protocol samples backbone torsion space in centroid,
# the sidechain conformations are neglected, when it is transferred
# to fullatom, we typically set the sidechain conformations to their
# "original" values and perform sidechain packing,
# a ReturnSidechainMover saves a pose's sidechains (in this case
# staring_pose) and when applied, inserts these conformations
# into the input pose
recover_sidechains = protocols.simple_moves.ReturnSidechainMover(pose)
# 4. convert to centroid
to_centroid.apply(pose)
# 5. create a (centroid) test pose
test_pose = Pose()
test_pose.assign(pose)
# 6. create ScoreFunctions for centroid and fullatom docking
scorefxn_low = create_score_function('interchain_cen')
scorefxn_high = create_score_function('docking')
# PyRosetta3: scorefxn_high_min = create_score_function_ws_patch('docking', 'docking_min')
scorefxn_high_min = create_score_function('docking', 'docking_min')
# 7. create Movers for producing an initial perturbation of the structure
# the DockingProtocol (see below) can do this but several Movers are
# used to demonstrate their syntax
# these Movers randomize the orientation (rotation) of each docking partner
randomize_upstream = RigidBodyRandomizeMover(pose, dock_jump,
partner_upstream)
randomize_downstream = RigidBodyRandomizeMover(pose, dock_jump,
partner_downstream)
# this Mover translates one docking partner away from the other in a random
# direction a distance specified by the second argument (in Angstroms)
# and rotates this partner randomly by the third argument (in degrees)
dock_pert = RigidBodyPerturbMover(dock_jump, translation, rotation)
# this Mover randomizes a pose's partners (rotation)
spin = RigidBodySpinMover(dock_jump)
# this Mover uses the axis defined by the inter-body jump (jump 1) to move
# the docking partners close together
slide_into_contact = protocols.docking.DockingSlideIntoContact(dock_jump)
# 8. setup the MinMover
# the MoveMap can set jumps (by jump number) as degrees of freedom
movemap = MoveMap()
movemap.set_jump(dock_jump, True)
# the MinMover can minimize score based on a jump degree of freedom, this
# will find the distance between the docking partners which minimizes
# the score
minmover = protocols.minimization_packing.MinMover()
minmover.movemap(movemap)
minmover.score_function(scorefxn_high_min)
# 9. create a SequenceMover for the perturbation step
perturb = protocols.moves.SequenceMover()
perturb.add_mover(randomize_upstream)
perturb.add_mover(randomize_downstream)
perturb.add_mover(dock_pert)
perturb.add_mover(spin)
perturb.add_mover(slide_into_contact)
perturb.add_mover(to_fullatom)
perturb.add_mover(recover_sidechains)
perturb.add_mover(minmover)
# 10. setup the DockingProtocol
# ...as should be obvious by now, Rosetta applications have no central
# standardization, the DockingProtocol object can be created and
# applied to perform Rosetta docking, many of its options and settings
# can be set using the DockingProtocol setter methods
# here, on instance is created with all default values and the movable jump
# is manually set to jump 1 (just to be certain), the centroid docking
# ScoreFunction is set and the fullatom docking ScoreFunction is set
dock_prot = protocols.docking.DockingProtocol() # contains many docking functions
dock_prot.set_movable_jumps(Vector1([1])) # set the jump to jump 1
dock_prot.set_lowres_scorefxn(scorefxn_low)
dock_prot.set_highres_scorefxn(scorefxn_high_min)
#### you can alternatively access the low and high resolution sections of
#### the DockingProtocol, both are applied by the DockingProtocol but
#### a novel protocol may only require centroid (DockingLowRes) or
#### fullatom (DockingHighRes), uncomment the lines below and their
#### application below
#docking_low = DockingLowRes()
#docking_low.set_movable_jumps(Vector1([1]))
#docking_low.set_scorefxn(scorefxn_low)
#docking_high = DockingHighRes()
#docking_high.set_movable_jumps(Vector1([1]))
#docking_high.set_scorefxn(scorefxn_high)
# 11. setup the PyJobDistributor
jd = PyJobDistributor(job_output, jobs, scorefxn_high)
temp_pose = Pose() # a temporary pose to export to PyMOL
temp_pose.assign(pose)
to_fullatom.apply(temp_pose) # the original pose was fullatom
recover_sidechains.apply(temp_pose) # with these sidechains
jd.native_pose = temp_pose # for RMSD comparison
# 12. setup a PyMOL_Observer (optional)
# the PyMOL_Observer object owns a PyMOLMover and monitors pose objects for
# structural changes, when changes are detected the new structure is
# sent to PyMOL
# fortunately, this allows investigation of full protocols since
# intermediate changes are displayed, it also eliminates the need to
# manually apply the PyMOLMover during a custom protocol
# unfortunately, this can make the output difficult to interpret (since you
# aren't explicitly telling it when to export) and can significantly slow
# down protocols since many structures are output (PyMOL can also slow
# down if too many structures are provided and a fast machine may
# generate structures too quickly for PyMOL to read, the
# "Buffer clean up" message
# uncomment the line below to use the PyMOL_Observer
## AddPyMOLObserver(test_pose, True)
# 13. perform protein-protein docking
counter = 0 # for pretty output to PyMOL
while not jd.job_complete:
# a. set necessary variables for this trajectory
# -reset the test pose to original (centroid) structure
test_pose.assign(pose)
# -change the pose name, for pretty output to PyMOL
counter += 1
test_pose.pdb_info().name(job_output + '_' + str(counter))
# b. perturb the structure for this trajectory
perturb.apply(test_pose)
# c. perform docking
dock_prot.apply(test_pose)
#### alternate application of the DockingProtocol pieces
#docking_low.apply(test_pose)
#docking_high.apply(test_pose)
# d. output the decoy structure
to_fullatom.apply(test_pose) # ensure the output is fullatom
# to PyMOL
test_pose.pdb_info().name(job_output + '_' + str( counter ) + '_fa')
# to a PDB file
jd.output_decoy(test_pose)
print( CA_rmsd(pose, pose_low) )
print( calc_Lrmsd(pose, pose_low, Vector1([1])) )
# Job Distributor
import tempfile
output = tempfile.mkstemp()[1]
jd = PyJobDistributor(output, 10, scorefxn_low)
native_pose = pose_from_file("../test/data/workshops/complex.high.pdb")
jd.native_pose = native_pose
starting_pose = Pose()
starting_pose.assign(pose_low)
while (jd.job_complete == False):
pose_low.assign(starting_pose)
dock_lowres.apply(pose_low)
jd.output_decoy(pose_low)
# High-Resolution Docking
scorefxn_high = create_score_function("ref2015.wts", "docking")
dock_hires = protocols.docking.DockMCMProtocol()
dock_hires.set_scorefxn(scorefxn_high)
dock_hires.set_partners("A_B")
recover_sidechains = protocols.simple_moves.ReturnSidechainMover(pose_high)
recover_sidechains.apply(pose)
print( "done" )