def pep_run(decoy_name, n_decoys, pose, sf='docking', pymol_ip_addr=None):
"""
Tested score function is 'docking' not sure if this is the best. Is there a way to run this in a
distributed fashion? Each decoy takes ~3 minutes on my machine.
"""
if pymol_ip_addr:
pmm = PyMOLMover(pymol_ip_addr,
65000) #enter the IP that pymol displays on startup
pmm.keep_history(True)
# Score function and starting PDB
sf = create_score_function(sf) # no idea what this sf is...
#pose = pose_from_pdb(pdb)
# Creating FlexPepDock protocol using init options
fpdock = FlexPepDockingProtocol()
jd = PyJobDistributor(decoy_name, n_decoys, sf)
while not jd.job_complete:
pp = Pose()
pp.assign(pose)
fpdock.apply(pp)
if pymol_ip_addr:
pmm.apply(pp)
jd.output_decoy(
pp) # this will output a PDB file, which is not really necessary.
def pose_structure(pose, display_residues=[]):
"""
Extracts and displays various structural properties of the input <pose>
and its <display_residues> including:
-PDB numbering
-chain identification
-sequence
-secondary structure
"""
# store the pose's number of residues, example Python syntax
nres = pose.total_residue()
# 1. obtain the pose's sequence
sequence = pose.sequence()
# 2. obtain a list of PDB numbering and icode as a single string
pdb_info = pose.pdb_info()
PDB_nums = [(str(pdb_info.number(i)) + pdb_info.icode(i)).strip()
for i in range(1, nres + 1)]
# 3. obtains a list of the chains organized by residue
chains = [pdb_info.chain(i) for i in range(1, nres + 1)]
# 4. extracts a list of the unique chain IDs
unique_chains = []
for c in chains:
if c not in unique_chains:
unique_chains.append(c)
# start outputting information to screen
print('\n' + '=' * 80)
print('Loaded from', pdb_info.name())
print(nres, 'residues')
print(len(unique_chains), 'chain(s) (' + str(unique_chains)[1:-1] + ')')
print('Sequence:\n' + sequence)
# this object is contained in PyRosetta v2.0 and above
# 5. obtain the pose's secondary structure as predicted by PyRosetta's
# built-in DSSP algorithm
DSSP = protocols.moves.DsspMover()
DSSP.apply(pose) # populates the pose's Pose.secstruct
ss = pose.secstruct()
print('Secondary Structure:\n' + ss)
print('\t' + str(100. * ss.count('H') / len(ss))[:4] + '% Helical')
print('\t' + str(100. * ss.count('E') / len(ss))[:4] + '% Sheet')
print('\t' + str(100. * ss.count('L') / len(ss))[:4] + '% Loop')
# 6. obtain the phi, psi, and omega torsion angles
phis = [pose.phi(i) for i in range(1, nres + 1)]
psis = [pose.psi(i) for i in range(1, nres + 1)]
omegas = [pose.omega(i) for i in range(1, nres + 1)]
# this object is contained in PyRosetta v2.0 and above
# create a PyMOLMover for exporting structures directly to PyMOL
pymover = PyMOLMover()
pymover.apply(pose) # export the structure to PyMOL (optional)
# 7. output information on the requested residues
# use a simple dictionary to make output nicer
ss_dict = {'L': 'Loop', 'H': 'Helix', 'E': 'Strand'}
for i in display_residues:
print('=' * 80)
print('Pose numbered Residue', i)
print('PDB numbered Residue', PDB_nums[i - 1])
print('Single Letter:', sequence[i - 1])
print('Chain:', chains[i - 1])
print('Secondary Structure:', ss_dict[ss[i - 1]])
print('Phi:', phis[i - 1])
print('Psi:', psis[i - 1])
print('Omega:', omegas[i - 1])
# extract the chis
chis = [pose.chi(j + 1, i) for j in range(pose.residue(i).nchi())]
for chi_no in range(len(chis)):
print('Chi ' + str(chi_no + 1) + ':', chis[chi_no])
print('=' * 80)
##################################################################
# make score function
scorefxn = get_fa_scorefxn()
# mutate protein
print(fa_working.pdb_info().pdb2pose('H', 12))
mutater = pyrosetta.rosetta.protocols.simple_moves.MutateResidue()
mutater.set_target(386)
mutater.set_res_name('PRO')
mutater.apply(fa_working)
# see in PyMOL
pymol = PyMOLMover()
pymol.pymol_name('mutated_superantigen')
pymol.apply(fa_working)
##################################################################
################### RELAX LOCALLY ################################
##################################################################
# relax pose, store lowest energy
# (i.e. import fast relax method from lecture7)
# task factory, what to design, where and how...
# (dock mcm protocol, get interface residues)
# interface energy optimization
from fast_relax_function import *
fast_relax(fa_working, fast_relax_rounds=1)
##################################################################
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')
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 )
tol = 0.001
min_type = "linmin"
linmin_mover = protocols.minimization_packing.MinMover(mm, scorefxn_low,
min_type, tol, True)
# save starting pose
starting_p_centroid = Pose()
starting_p_centroid.assign(p)
print(
"building random loop conformation with ideal bond lengths, bond angles,")
print("and omega angles using KIC", end='')
success = False
kic_mover.set_idealize_loop_first(True)
kic_mover.set_pivots(loop_begin, loop_cut, loop_end)
pymol.apply(p)
for i in range(MAX_KIC_BUILD_ATTEMPTS):
print("\n attempt %d..." % i, end='')
kic_mover.apply(p)
pymol.apply(p)
if kic_mover.last_move_succeeded():
success = True
kic_mover.set_idealize_loop_first(False)
print("succeeded.")
break
if not success:
print( "Could not complete initial KIC loop building in %d attempts. Exiting" \
%MAX_KIC_BUILD_ATTEMPTS )
exit()
scorefxn_low(p)
linmin_mover.apply(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
###################################################################
## Loop through and store lowest energy docking pose
lowest_energy_pose = Pose()
lowest_energy = float('inf')
for i in range(3):
# rotate and translate superantigen (8 degrees rot, 3 ang trans)
pert_mover = rigid_moves.RigidBodyPerturbMover(jump_num, 8, 3)
pert_mover.apply(fa_working)
# minimize the energy
min_mover.apply(fa_working)
# score pose
print('working pose energy: ')
curr_score = scorefxn(fa_working)
print(curr_score)
# store values
if curr_score < lowest_energy:
lowest_energy = curr_score
###################################################################
# see in PyMOL
pymol = PyMOLMover()
pymol.keep_history(True)
pymol.apply(fa_starting)
pymol.apply(fa_working)
pm = PyMOLMover()
# Linear Oligosaccharides & IUPAC Sequences
from rosetta.core.pose import pose_from_saccharide_sequence
glucose = pose_from_saccharide_sequence('alpha-D-Glcp')
galactose = pose_from_saccharide_sequence('Galp')
mannose = pose_from_saccharide_sequence('->3)-a-D-Manp')
maltotriose = pose_from_saccharide_sequence('a-D-Glcp-' * 3)
isomaltose = pose_from_saccharide_sequence('->6)-Glcp-' * 2)
lactose = pose_from_saccharide_sequence('b-D-Galp-(1->4)-a-D-Glcp')
pm.apply(isomaltose)
pm.apply(glucose)
pm.apply(galactose)
print( maltotriose )
print( isomaltose )
print( lactose )
print( maltotriose.chain_sequence(1) )
print( isomaltose.chain_sequence(1) )
print( lactose.chain_sequence(1) )
for res in lactose: print( res.seqpos(), res.name() )
for res in maltotriose: print( res.seqpos(), res.name() )
print( glucose.residue(1) )
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 fold_tree(PDB_out=False):
"""
Demonstrates the syntax necessary for basic usage of the FoldTree object
performs these changes with a demonstrative pose example and writes
structures to PDB files if <PDB_out> is True
"""
##########
# FoldTree
# a FoldTree encodes the internal coordinate dependencies of a Pose
# a Pose object MUST have a FoldTree
# the FoldTree allows regions of a pose to become independent,
# it is used in many important applications, particularly:
# loop modeling: where changes to the conformation of a loop region
# should NOT alter the conformation of the entire protein
# rigid-body docking: where changes in the position of one docking
# partner should not alter the position of the other docking partner
# a FoldTree is effectively a list of Edge objects, you can view the Edges
# by printing the FoldTree ("print FoldTree")
# the length of a FoldTree (FoldTree.size) MUST match the length of its
# corresponding Pose (Pose.total_residue)
# it is possible to create an improper FoldTree, the method
# FoldTree.check_fold_tree returns True if the FoldTree is complete and
# usable and False otherwise
# some Edge objects are Jumps, indicating a "jump" in the internal
# coordinate dependency
# when a FoldTree is created, it can accept an optional integer argument
# setting the FoldTree to contain a single Edge with a length equal to
# the input integer value, the same result is attained by creating an
# empty FoldTree (no input arguments) and using the method
# FoldTree.simple_tree with an input integer equal to the size of the
# FoldTree
# 1. create the example pose
test_pose = pose_from_sequence('ACDEFGHIKLMNPQRSTVWY' * 3)
# 2. setup the jump points, where a jump is anchored, and the cutpoint
cutpoint = int(test_pose.total_residue() /
2) # integer division, no decimal
low_jump_point = cutpoint - 10
high_jump_point = cutpoint + 10
# the easiest way to create a new complete FoldTree is to use the method
# FoldTree.simple_tree to create and empty FoldTree and assign jumps to
# it using the method FoldTree.new_jump
# the FoldTree constructor is overloaded to accept an input integer
# indicating how large to make the FoldTree
# 3. create a simple, one jump FoldTree for the pose
# a. using FoldTree.new_jump
#pose_fold_tree = FoldTree(test_pose.total_residue())
#### these two lines produce the same FoldTree as the one above
pose_fold_tree = FoldTree()
pose_fold_tree.simple_tree(test_pose.total_residue())
pose_fold_tree.new_jump(low_jump_point, high_jump_point, cutpoint)
print('\nThe first FoldTree is proper:', pose_fold_tree.check_fold_tree())
# b. using FoldTree.add_edge
# a more difficult method for creating a FoldTree is simply to create it
# empty and use the method FoldTree.add_edge to fill the FoldTree with
# new Edge data
pose_fold_tree = FoldTree()
pose_fold_tree.add_edge(1, low_jump_point, -1)
pose_fold_tree.add_edge(low_jump_point, cutpoint, -1)
pose_fold_tree.add_edge(low_jump_point, high_jump_point, 1)
pose_fold_tree.add_edge(high_jump_point, test_pose.total_residue(), -1)
pose_fold_tree.add_edge(high_jump_point, cutpoint + 1, -1)
print('The second FoldTree is proper:', pose_fold_tree.check_fold_tree())
# demonstrate FoldTree's effect on structure
# 4. linearize it
for i in range(1, test_pose.total_residue() + 1):
test_pose.set_phi(i, -180)
test_pose.set_psi(i, 180)
test_pose.set_omega(i, 180)
# the Pose.fold_tree method is an overloaded getter/setter,
# providing it with no input returns the Pose's FoldTree object
# providing a FoldTree object as input overwrites the Pose's current
# FoldTree with the new one
# the FoldTree is set here to prevent problems when "linearizing"
test_pose.fold_tree(pose_fold_tree)
# this object is contained in PyRosetta v2.0 and above (optional)
pymover = PyMOLMover()
# 5. change and display the new structures
# a. export "linearized" structure
test_pose.pdb_info().name('linearized') # for PyMOLMover
pymover.apply(test_pose)
if PDB_out:
test_pose.dump_pdb('linearized.pdb')
print('\nlinearized structure output')
# b. make an early change
test_pose.set_phi(low_jump_point - 10, 50)
test_pose.pdb_info().name('pre_jump') # for PyMOLMover
pymover.apply(test_pose) # all downstream residues move
if PDB_out:
test_pose.dump_pdb('pre_jump.pdb')
print('pre jump perturbed structure output')
# c. make a change in the first edge created by the jump
test_pose.set_phi(low_jump_point + 5, 50)
test_pose.pdb_info().name('early_in_jump') # for PyMOLMover
pymover.apply(test_pose) # residues up to the cutpoint change
if PDB_out:
test_pose.dump_pdb('early_in_jump.pdb')
print('first internal jump edge perturbed structure output')
# d. make a change in the second edge created by the jump
test_pose.set_phi(high_jump_point - 5, 50)
test_pose.pdb_info().name('late_in_jump') # for PyMOLMover
pymover.apply(test_pose) # residues down to the cutpoint change
if PDB_out:
test_pose.dump_pdb('late_in_jump.pdb')
print('second internal jump edge perturbed structure output')
# e. make a late change
test_pose.set_phi(high_jump_point + 10, 50)
test_pose.pdb_info().name('post_jump') # for PyMOLMover
pymover.apply(test_pose) # all residues downstream move
if PDB_out:
test_pose.dump_pdb('post_jump.pdb')
print('post jump perturbed structure output')
print(pose.sequence())
print("Protein has", pose.total_residue(), "residues.")
print(pose.residue(500).name())
print(pose.pdb_info().chain(500))
print(pose.pdb_info().number(500))
print(pose.phi(5))
print(pose.psi(5))
print(pose.chi(1, 5))
R5N = AtomID(1, 5)
R5CA = AtomID(2, 5)
R5C = AtomID(3, 5)
print(pose.conformation().bond_length(R5N, R5CA))
print(pose.conformation().bond_length(R5CA, R5C))
from pyrosetta import PyMOLMover
pymol = PyMOLMover()
pymol.apply(pose)
# Calculating energy score
print("Calculating energy score of 6Q21 protein")
ras = pose_from_pdb("6q21.pdb")
print(ras)
print(ras.sequence())
from pyrosetta.teaching import *
scorefxn = get_fa_scorefxn()
print("Score function of 6Q21 protein is :")
print(scorefxn)
scorefxn2 = ScoreFunction()
scorefxn2.set_weight(fa_atr, 1.0)
scorefxn2.set_weight(fa_rep, 1.0)
print(scorefxn(ras))
scorefxn.show(ras)
from pyrosetta import init, pose_from_file, create_score_function, Pose, MoveMap, PyMOLMover
from pyrosetta.rosetta import core, protocols
from pyrosetta.teaching import MinMover, SmallMover, ShearMover, TrialMover, MonteCarlo, RepeatMover
init(extra_options = "-constant_seed") # WARNING: option '-constant_seed' is for testing only! MAKE SURE TO REMOVE IT IN PRODUCTION RUNS!!!!!
import os; os.chdir('.test.output')
start = pose_from_file("../test/data/workshops/1YY8.clean.pdb")
test = Pose()
test.assign(start)
start.pdb_info().name("start")
test.pdb_info().name("test")
pmm = PyMOLMover()
pmm.apply(start)
pmm.apply(test)
pmm.keep_history(True)
print( pmm )
# Small and Shear Moves
kT = 1.0
n_moves = 1
movemap = MoveMap()
movemap.set_bb(True)
small_mover = SmallMover(movemap, kT, n_moves)
shear_mover = ShearMover(movemap, kT, n_moves)
small_mover.angle_max("H", 5)
small_mover.angle_max("E", 5)
small_mover.angle_max("L", 5)
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)
ft.add_edge(13, 19, -1)
ft.add_edge(13, 26, 1)
ft.add_edge(26, 20, -1)
ft.add_edge(26, 116, -1)
print(ft)
ft.check_fold_tree()
pose.fold_tree(ft)
for res in (10, 13, 16, 23, 26, 30):
pose.set_phi(res, 180)
pose.dump_pdb("loop" + str(res) + ".pdb")
pmm = PyMOLMover()
pmm.apply(pose)
# no longer supported: pmm.send_foldtree(pose)
# no longer supported: pmm.view_foldtree_diagram(pose, ft)
ft.clear()
ft.simple_tree(116)
ft.new_jump(76, 85, 80)
# Cyclic Coordination Descent (CCD) Loop Closure
movemap = MoveMap()
movemap.set_bb(True)
movemap.set_chi(True)
loop1 = protocols.loops.Loop(15, 24, 19)
add_single_cutpoint_variant(pose, loop1)
ccd = protocols.loops.loop_closure.ccd.CCDLoopClosureMover(loop1, movemap)
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)
a = random.gauss(torsion, 25)
pose.set_psi(res, a)
# initialize pose objects
last_pose = Pose()
low_pose = Pose()
# create poly-A chain and set all peptide bonds to trans
p = pose_from_sequence("AAAAAAAAAA", "fa_standard")
for res in range(1, p.total_residue() + 1):
p.set_omega(res, 180)
# use the PyMOLMover to echo this structure to PyMOL
pmm = PyMOLMover()
pmm.apply(p)
# set score function to include Van der Wals and H-bonds only
score = ScoreFunction()
score.set_weight(fa_atr, 0.8)
score.set_weight(fa_rep, 0.44)
score.set_weight(hbond_sr_bb, 1.17)
# initialize low score objects
low_pose.assign(p)
low_score = score(p)
for i in range(100):
last_score = score(p)
last_pose.assign(p)
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)
