if deltaE > 0:
P = math.exp(-deltaE / kT) # probability of accepting move diminishes
# exponetially with increasing energy
roll = random.uniform(0.0, 1.0)
if roll >= P:
p.assign(last_pose) # reject pose and reassign previous
continue
# if new pose is accepted, store lowest score and associated pose
if new_score < low_score:
low_score = new_score
low_pose.assign(p)
# output files
p.dump_pdb("poly-A_final.pdb")
low_pose.dump_pdb("poly-A_low.pdb")
# Low-Resolution (Centroid) Scoring
ras = pose_from_file("../test/data/workshops/6Q21.clean.pdb")
score2 = get_score_function()
print(score2(ras))
print(ras.residue(5))
switch = SwitchResidueTypeSetMover("centroid")
switch.apply(ras)
print(ras.residue(5))
score3 = create_score_function("score3")
print(score3(ras))
switch2 = SwitchResidueTypeSetMover("fa_standard")
def movemap(pose, PDB_out=False):
"""
Demonstrates the syntax necessary for basic usage of the MoveMap object
performs these changes with a demonstrative backbone minimization
using <pose> and writes structures to PDB files if <PDB_out> is True
"""
#########
# MoveMap
# a MoveMap encodes what data is allowed to change in a Pose, referred to as
# its degrees of freedom
# a MoveMap is separate from a Pose and is usually required by a Mover so
# that the correct degrees of freedom are manipulated, in this way,
# MoveMap and Pose objects often work in parallel
# several MoveMap's can correspond to the same Pose
# a MoveMap stores information on a per-residue basis about the
# backbone ({phi, psi, omega}) and chi ({chi_i}) torsion angle sets
# the MoveMap can only set these sets of torsions to True or False, it
# cannot set freedom for the individual angles (such as phi free and psi
# fixed)
# the MoveMap has no upper-limit on its residue information, it defaults to
# all residues (up to residue 99999999) backbone and chi False
# you can view the MoveMap per-residue torsion settings by using the
# MoveMap.show( Pose.total_residue() ) method (the input argument is the
# highest residue to output, it does not support viewing a range)
pose_move_map = MoveMap()
# change all backbone torsion angles
pose_move_map.set_bb(True)
# change all chi angle torsion angles (False by default)
pose_move_map.set_chi(False)
# change a single backbone torsion angles
#pose_move_map.set_bb(1, True) # example syntax
# change a single residue's chi torsion angles
#pose_move_map.set_chi(1, True) # example syntax
pose_move_map.show(pose.total_residue())
# perform gradient based minimization on the "median" residues, this
# method (MinMover) determines the gradient of an input pose using a
# ScoreFunction for evaluation and a MoveMap to define the degrees of
# freedom
# create a standard ScoreFunction
scorefxn = get_fa_scorefxn(
) # create_score_function_ws_patch('standard', 'score12')
# redefine the MoveMap to include the median half of the residues
# turn "off" all backbone torsion angles
pose_move_map.set_bb(False) # reset to backbone False
# turn "on" a range of residue backbone torsion angles
pose_move_map.set_bb_true_range(int(pose.total_residue() / 4),
int(pose.total_residue() * 3 / 4))
# create the MinMover
minmover = protocols.minimization_packing.MinMover()
minmover.score_function(scorefxn)
minmover.movemap(pose_move_map)
# create a copy of the pose
test_pose = Pose()
test_pose.assign(pose)
# apply minimization
scorefxn(test_pose) # to prevent verbose output on the next line
pymover = PyMOLMover()
#### uncomment the line below and "comment-out" the two lines below to
#### export the structures into different PyMOL states of the same object
#pymover.keep_history = True # enables viewing across states
#### comment-out the line below, changing PDBInfo names tells the
#### PyMOLMover to produce new objects
test_pose.pdb_info().name('original')
pymover.apply(test_pose)
print('\nPre minimization score:', scorefxn(test_pose))
minmover.apply(test_pose)
if PDB_out:
test_pose.dump_pdb('minimized.pdb')
print('Post minimization score:', scorefxn(test_pose))
#### comment-out the line below
test_pose.pdb_info().name('minimized')
pymover.apply(test_pose)
def packer_task(pose, PDB_out=False):
"""
Demonstrates the syntax necessary for basic usage of the PackerTask object
performs demonstrative sidechain packing and selected design
using <pose> and writes structures to PDB files if <PDB_out>
is True
"""
# create a copy of the pose
test_pose = Pose()
test_pose.assign(pose)
# this object is contained in PyRosetta v2.0 and above
pymover = PyMOLMover()
# create a standard ScoreFunction
scorefxn = get_fa_scorefxn(
) # create_score_function_ws_patch('standard', 'score12')
############
# PackerTask
# a PackerTask encodes preferences and options for sidechain packing, an
# effective Rosetta methodology for changing sidechain conformations, and
# design (mutation)
# a PackerTask stores information on a per-residue basis
# each residue may be packed or designed
# PackerTasks are handled slightly differently in PyRosetta
####pose_packer = PackerTask() # this line will not work properly
pose_packer = standard_packer_task(test_pose)
# the pose argument tells the PackerTask how large it should be
# sidechain packing "optimizes" a pose's sidechain conformations by cycling
# through (Dunbrack) rotamers (sets of chi angles) at a specific residue
# and selecting the rotamer which achieves the lowest score,
# enumerating all possibilities for all sidechains simultaneously is
# impractically expensive so the residues to be packed are individually
# optimized in a "random" order
# packing options include:
# -"freezing" the residue, preventing it from changing conformation
# -including the original sidechain conformation when determining the
# lowest scoring conformation
pose_packer.restrict_to_repacking() # turns off design
pose_packer.or_include_current(True) # considers original conformation
print(pose_packer)
# packing and design can be performed by a PackRotamersMover, it requires
# a ScoreFunction, for optimizing the sidechains and a PackerTask,
# setting the packing and design options
packmover = protocols.minimization_packing.PackRotamersMover(
scorefxn, pose_packer)
scorefxn(pose) # to prevent verbose output on the next line
print('\nPre packing score:', scorefxn(test_pose))
test_pose.pdb_info().name('original') # for PyMOLMover
pymover.apply(test_pose)
packmover.apply(test_pose)
print('Post packing score:', scorefxn(test_pose))
test_pose.pdb_info().name('packed') # for PyMOLMover
pymover.apply(test_pose)
if PDB_out:
test_pose.dump_pdb('packed.pdb')
# since the PackerTask specifies how the sidechains change, it has been
# extended to include sidechain constitutional changes allowing
# protein design, this method of design is very similar to sidechain
# packing; all rotamers of the possible mutants at a single residue
# are considered and the lowest scoring conformation is selected
# design options include:
# -allow all amino acids
# -allow all amino acids except cysteine
# -allow specific amino acids
# -prevent specific amino acids
# -allow polar amino acids only
# -prevent polar amino acids
# -allow only the native amino acid
# the myriad of packing and design options can be set manually or, more
# commonly, using a specific file format known as a resfile
# resfile syntax is explained at:
# http://www.rosettacommons.org/manuals/archive/rosetta3.1_user_guide/file_resfiles.html
# manually setting deign options is tedious, the methods below are handy
# for creating resfiles
# mutate the "middle" residues
center = test_pose.total_residue() // 2
specific_design = {}
for i in range(center - 2, center + 3):
specific_design[i] = 'ALLAA'
# write a resfile to perform these mutations
generate_resfile_from_pose(test_pose,
'sample_resfile',
False,
specific=specific_design)
# setup the design PackerTask, use the generated resfile
pose_design = standard_packer_task(test_pose)
rosetta.core.pack.task.parse_resfile(test_pose, pose_design,
'sample_resfile')
print(pose_design)
# prepare a new structure
test_pose.assign(pose)
# perform design
designmover = protocols.minimization_packing.PackRotamersMover(
scorefxn, pose_design)
print(
'\nDesign with all proteogenic amino acids at (pose numbered)\
residues', center - 2, 'to', center + 2)
print('Pre-design score:', scorefxn(test_pose))
print( 'Pre-design sequence: ...' + \
test_pose.sequence()[center - 5:center + 4] + '...' )
designmover.apply(test_pose) # perform design
print('\nPost-design score:', scorefxn(test_pose))
print( 'Post-design sequence: ...' + \
test_pose.sequence()[center - 5:center + 4] + '...' )
test_pose.pdb_info().name('designed') # for PyMOLMover
pymover.apply(test_pose)
if PDB_out:
test_pose.dump_pdb('designed.pdb')
chi=designable.extend(repackable))
if test_run:
fd.rounds = 1
print(fd.movemap)
print(fd.task_factory)
fd.apply()
# Create new pose from input file for comparison
input_pose = pose_from_file(pdbpath)
#input_pose = pose_from_file(workspace.input_pdb_path)
# Calculate several different types of RMSD
ca_rmsd = CA_rmsd(fd.pose, input_pose)
all_atom_rmsd = all_atom_rmsd(fd.pose, input_pose)
filters = workspace.get_filters(fd.pose,
task_id=job_info['task_id'], score_fragments=False,
test_run=test_run)
filters.run_filters()
# Add RMSDs as extra metrics, which will be printed at the end of
# the PDB file.
setPoseExtraScore(fd.pose, 'EXTRA_METRIC_CA_RMSD', ca_rmsd)
setPoseExtraScore(fd.pose, 'EXTRA_METRIC_AllAtom_RMSD', all_atom_rmsd)
# Save final pose as a pdb file.
input_name = os.path.basename(pdbpath).split(".")[0]
out = workspace.output_prefix(job_info) + input_name + workspace.output_suffix(job_info) + '.pdb.gz'
pose.dump_pdb(out)
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
