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_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)
def interface_ddG(pose,
mutant_position,
mutant_aa,
movable_jumps,
scorefxn='',
cutoff=8.0,
out_filename=''):
# 1. create a reference copy of the pose
wt = Pose() # the "wild-type"
wt.assign(pose)
# 2. setup a specific default ScoreFunction
if not scorefxn:
# this is a modified version of the scoring function discussed in
# PNAS 2002 (22)14116-21, without environment dependent hbonding
scorefxn = ScoreFunction()
scorefxn.set_weight(fa_atr, 0.44)
scorefxn.set_weight(fa_rep, 0.07)
scorefxn.set_weight(fa_sol, 1.0)
scorefxn.set_weight(hbond_bb_sc, 0.5)
scorefxn.set_weight(hbond_sc, 1.0)
# 3. create a copy of the pose for mutation
mutant = Pose()
mutant.assign(pose)
# 4. mutate the desired residue
# the pack_radius argument of mutate_residue (see below) is redundant
# for this application since the area around the mutation is already
# repacked
mutant = mutate_residue(mutant, mutant_position, mutant_aa, 0.0, scorefxn)
# 5. calculate the "interaction energy"
# the method calc_interaction_energy is exposed in PyRosetta however it
# does not alter the protein conformation after translation and may miss
# significant interactions
# an alternate method for manually separating and scoring is provided called
# calc_binding_energy (see Interaction Energy vs. Binding Energy below)
wt_score = calc_binding_energy(wt, scorefxn, mutant_position, cutoff)
mut_score = calc_binding_energy(mutant, scorefxn, mutant_position, cutoff)
#### the method calc_interaction_energy separates an input pose by
#### 500 Angstroms along the jump defined in a Vector1 of jump numbers
#### for movable jumps, a ScoreFunction must also be provided
#### if setup_foldtree has not been applied, calc_interaction_energy may be
#### wrong (since the jumps may be wrong)
#wt_score = calc_interaction_energy(wt, scorefxn, movable_jumps)
#mut_score = calc_interaction_energy(mutant, scorefxn, movable_jumps)
ddg = mut_score - wt_score
# 6. output data (optional)
# -export the mutant structure to PyMOL (optional)
mutant.pdb_info().name(pose.sequence()[mutant_position - 1] +
str(pose.pdb_info().number(mutant_position)) +
mutant.sequence()[mutant_position - 1])
pymover = PyMOLMover()
scorefxn(mutant)
pymover.apply(mutant)
pymover.send_energy(mutant)
# -write the mutant structure to a PDB file
if out_filename:
mutant.dump_pdb(out_filename)
return ddg
def scanning(pdb_filename,
partners,
mutant_aa='A',
interface_cutoff=8.0,
output=False,
trials=1,
trial_output=''):
"""
Performs "scanning" at an interface within <pdb_filename> between
<partners> by mutating relevant residues to <mutant_aa> and repacking
residues within <pack_radius> Angstroms, further repacking all
residues within <interface_cutoff> of the interface residue, scoring
the complex and subtracting the score of a pose with the partners
separated by 500 Angstroms.
<trials> scans are performed (to average results) with summaries written
to <trial_output>_(trial#).txt.
Structures are exported to a PyMOL instance.
"""
# 1. create a pose from the desired PDB file
pose = Pose()
pose_from_file(pose, pdb_filename)
# 2. setup the docking FoldTree and other related parameters
dock_jump = 1
movable_jumps = Vector1([dock_jump])
protocols.docking.setup_foldtree(pose, partners, movable_jumps)
# 3. create ScoreFuncions for the Interface and "ddG" calculations
# the pose's Energies objects MUST be updated for the Interface object to
# work normally
scorefxn = get_fa_scorefxn() # create_score_function('standard')
scorefxn(pose) # needed for proper Interface calculation
# setup a "ddG" ScoreFunction, custom weights
ddG_scorefxn = ScoreFunction()
ddG_scorefxn.set_weight(core.scoring.fa_atr, 0.44)
ddG_scorefxn.set_weight(core.scoring.fa_rep, 0.07)
ddG_scorefxn.set_weight(core.scoring.fa_sol, 1.0)
ddG_scorefxn.set_weight(core.scoring.hbond_bb_sc, 0.5)
ddG_scorefxn.set_weight(core.scoring.hbond_sc, 1.0)
# 4. create an Interface object for the pose
interface = Interface(dock_jump)
interface.distance(interface_cutoff)
interface.calculate(pose)
# 5. create a PyMOLMover for sending output to PyMOL (optional)
pymover = PyMOLMover()
pymover.keep_history(True) # for multiple trajectories
pymover.apply(pose)
pymover.send_energy(pose)
# 6. perform scanning trials
# the large number of packing operations introduces a lot of variability,
# for best results, perform several trials and average the results,
# these score changes are useful to QUALITATIVELY defining "hotspot"
# residues
# this script does not use a PyJobDistributor since no PDB files are output
for trial in range(trials):
# store the ddG values in a dictionary
ddG_mutants = {}
for i in range(1, pose.total_residue() + 1):
# for residues at the interface
if interface.is_interface(i) == True:
# this way you can TURN OFF output by providing False arguments
# (such as '', the default)
filename = ''
if output:
filename = pose.pdb_info().name()[:-4] + '_' +\
pose.sequence()[i-1] +\
str(pose.pdb_info().number(i)) + '->' + mutant_aa
# determine the interace score change upon mutation
ddG_mutants[i] = interface_ddG(pose, i, mutant_aa,
movable_jumps, ddG_scorefxn,
interface_cutoff, filename)
# output results
print('=' * 80)
print('Trial', str(trial + 1))
print(
'Mutants (PDB numbered)\t\"ddG\" (interaction dependent score change)'
)
residues = list(ddG_mutants.keys()
) # list(...) conversion is for python3 compatbility
residues.sort() # easier to read
display = [
pose.sequence()[i - 1] + str(pose.pdb_info().number(i)) +
mutant_aa + '\t' + str(ddG_mutants[i]) + '\n' for i in residues
]
print(''.join(display)[:-1])
print('=' * 80)
# write to file
f = open(trial_output + '_' + str(trial + 1) + '.txt', 'w')
f.writelines(display)
f.close()
#### alternate output using scanning_analysis (see below), only display
#### mutations with "deviant" score changes
print('Likely Hotspot Residues')
for hotspot in scanning_analysis(trial_output):
print(hotspot)
print('=' * 80)
def pose_scoring(pose, display_residues = []):
"""
Extracts and displays various score evaluations on the input <pose>
and its <display_residues> including:
total score
score term evaluations
per-residue score
radius of gyration (approximate)
"""
# this object is contained in PyRosetta v2.0 and above
# create a PyMOLMover for exporting structures directly to PyMOL
pymover = PyMOLMover()
# 1. score the pose using the default full-atom (fa) ScoreFunction
# a ScoreFunction is essentially a list of weights indicating which
# ScoreTypes are "on" and how they are scaled when summed together
# ScoreTypes define what scoring methods are performed on the pose
# some ScoreTypes are centroid or fullatom specific, others are not
# there are (at least) 5 ways to obtain the general fullatom ScoreFunction
# the 1st (a) and 2nd (b) methods are only present since PyRosetta v2.0
#
# a. this method returns the hard-coded set of weights for the standard
# fullatom ScoreFunction, which is currently called "ref2015"
fa_scorefxn = get_fa_scorefxn()
# b. this method returns a ScoreFunction with its weights set based on
# files stored in the database/scoring/weights (.wts files)
full_scorefxn = create_score_function('ref2015')
# c. this method sets the weights based on 'ref2015.wts' and then
# corrects them based on 'docking.wts_patch'
ws_patch_scorefxn = create_score_function('ref2015', 'docking') # create_score_function_ws_patch('ref2015', 'docking')
# d. this method returns a ScoreFunction with its weights set by loading
# weights from 'ref2015' followed by an adjustment by setting
# weights from 'docking.wts_patch'
patch_scorefxn = create_score_function('ref2015')
patch_scorefxn.apply_patch_from_file('docking')
# e. here an empty ScoreFunction is created and the weights are set manually
scorefxn = ScoreFunction()
scorefxn.set_weight(core.scoring.fa_atr, 0.800) # full-atom attractive score
scorefxn.set_weight(core.scoring.fa_rep, 0.440) # full-atom repulsive score
scorefxn.set_weight(core.scoring.fa_sol, 0.750) # full-atom solvation score
scorefxn.set_weight(core.scoring.fa_intra_rep, 0.004) # f.a. intraresidue rep. score
scorefxn.set_weight(core.scoring.fa_elec, 0.700) # full-atom electronic score
scorefxn.set_weight(core.scoring.pro_close, 1.000) # proline closure
scorefxn.set_weight(core.scoring.hbond_sr_bb, 1.170) # short-range hbonding
scorefxn.set_weight(core.scoring.hbond_lr_bb, 1.170) # long-range hbonding
scorefxn.set_weight(core.scoring.hbond_bb_sc, 1.170) # backbone-sidechain hbonding
scorefxn.set_weight(core.scoring.hbond_sc, 1.100) # sidechain-sidechain hbonding
scorefxn.set_weight(core.scoring.dslf_fa13, 1.000) # disulfide full-atom score
scorefxn.set_weight(core.scoring.rama, 0.200) # ramachandran score
scorefxn.set_weight(core.scoring.omega, 0.500) # omega torsion score
scorefxn.set_weight(core.scoring.fa_dun, 0.560) # fullatom Dunbrack rotamer score
scorefxn.set_weight(core.scoring.p_aa_pp, 0.320)
scorefxn.set_weight(core.scoring.ref, 1.000) # reference identity score
# ScoreFunction a, b, and e above have the same weights and thus return
# the same score for an input pose. Likewise, c and d should return the
# same scores.
# 2. output the ScoreFunction evaluations
#ws_patch_scorefxn(pose) # to prevent verbose output on the next line
print( '='*80 )
print( 'ScoreFunction a:', fa_scorefxn(pose) )
print( 'ScoreFunction b:', full_scorefxn(pose) )
print( 'ScoreFunction c:', ws_patch_scorefxn(pose) )
print( 'ScoreFunction d:', patch_scorefxn(pose) )
print( 'ScoreFunction e:', scorefxn(pose) )
pose_score = scorefxn(pose)
# 3. obtain the pose Energies object and all the residue total scores
energies = pose.energies()
residue_energies = [energies.residue_total_energy(i)
for i in range(1, pose.total_residue() + 1)]
# 4. obtain the non-zero weights of the ScoreFunction, active ScoreTypes
weights = [core.scoring.ScoreType(s)
for s in range(1, int(core.scoring.end_of_score_type_enumeration) + 1)
if scorefxn.weights()[core.scoring.ScoreType(s)]]
# 5. obtain all the pose energies using the weights list
# Energies.residue_total_energies returns an EMapVector of the unweighted
# score values, here they are multiplied by their weights
# remember when performing individual investigation, these are the raw
# unweighted score!
residue_weighted_energies_matrix = [
[energies.residue_total_energies(i)[w] * scorefxn.weights()[w]
for i in range(1, pose.total_residue() + 1)]
for w in weights]
# Unfortunately, hydrogen bonding scores are NOT stored in the structure
# returned by Energies.residue_total_energies
# 6. hydrogen bonding information must be extracted separately
pose_hbonds = core.scoring.hbonds.HBondSet()
core.scoring.hbonds.fill_hbond_set( pose , False , pose_hbonds )
# 7. create a dictionary with the pose residue numbers as keys and the
# residue hydrogen bonding information as values
# hydrogen bonding information is stored as test in the form:
# (donor residue) (donor atom) => (acceptor residue) (accecptor atom) |score
hbond_dictionary = {}
for residue in range(1, pose.total_residue() + 1):
hbond_text = ''
for hbond in range(1, pose_hbonds.nhbonds() + 1):
hbond = pose_hbonds.hbond(hbond)
acceptor_residue = hbond.acc_res()
donor_residue = hbond.don_res()
if residue == acceptor_residue or residue == donor_residue:
hbond_text += str(donor_residue).ljust(4) + ' ' + \
str(pose.residue(donor_residue).atom_name(\
hbond.don_hatm() )).strip().ljust(4) + \
' => ' + str(acceptor_residue).ljust(4) + ' ' + \
str(pose.residue(acceptor_residue).atom_name(\
hbond.acc_atm() )).strip().ljust(4) + \
' |score: ' + str(hbond.energy()) + '\n'
hbond_dictionary[residue] = hbond_text
# 8. approximate the radius of gyration
# there is an rg ScoreType in PyRosetta for performing this computation so
# a ScoreFunction can be made as an Rg calculator, likewise you can
# bias a structure towards more or less compact structures using this
# NOTE: this is NOT the true radius of gyration for a protein, it uses
# the Residue.nbr_atom coordinates to save time, this nbr_atom is the
# Residue atom closest to the Residue's center of geometry
RadG = ScoreFunction()
RadG.set_weight(core.scoring.rg , 1)
pose_radg = RadG(pose)
# 9. output the pose information
# the information is not expressed sequentially as it is produced because
# several PyRosetta objects and methods output intermediate information
# to screen, this would produce and unattractive output
print( '='*80 )
print( 'Loaded from' , pose.pdb_info().name() )
print( pose.total_residue() , 'residues' )
print( 'Radius of Gyration ~' , pose_radg )
print( 'Total Rosetta Score:' , pose_score )
scorefxn.show(pose)
# this object is contained in PyRosetta v2.0 and above
pymover.apply(pose)
pymover.send_energy(pose)
# 10. output information on the requested residues
for i in display_residues:
print( '='*80 )
print( 'Pose numbered Residue' , i )
print( 'Total Residue Score:' , residue_energies[i-1] )
print( 'Score Breakdown:\n' + '-'*45 )
# loop over the weights, extract the scores from the matrix
for w in range(len(weights)):
print( '\t' + core.scoring.name_from_score_type(weights[w]).ljust(20) + ':\t' ,\
residue_weighted_energies_matrix[w][i-1] )
print( '-'*45 )
# print the hydrogen bond information
print( 'Hydrogen bonds involving Residue ' + str(i) + ':' )
print( hbond_dictionary[i][:-1] )
print( '='*80 )
hset = ras.get_hbonds()
hset.show(ras)
hset.show(ras, 24)
pose = pose_from_file("../test/data/workshops/1YY9.clean.pdb")
rsd1_num = pose.pdb_info().pdb2pose('D', 102)
rsd2_num = pose.pdb_info().pdb2pose('A', 408)
print(rsd1_num)
print(rsd2_num)
rsd1 = pose.residue(rsd1_num)
rsd2 = pose.residue(rsd2_num)
emap = EMapVector()
scorefxn.eval_ci_2b(rsd1, rsd2, pose, emap)
print(emap[fa_atr])
print(emap[fa_rep])
print(emap[fa_sol])
pymol = PyMOLMover()
ras.pdb_info().name("ras")
pymol.send_energy(ras)
pymol.send_energy(ras, "fa_atr")
pymol.send_energy(ras, "fa_sol")
pymol.update_energy(True)
pymol.energy_type(fa_atr)
pymol.apply(ras)
# no longer supported: pymol.label_energy(ras, "fa_rep")
# no longer supported: pymol.send_hbonds(ras)
