def run(file_name,
d_min=0.8,
maxnum_residues_in_cluster=3,
filter_non_protein_and_hd=True):
prefix = os.path.basename(file_name)[3:7]
print file_name, prefix
pdb_inp = iotbx.pdb.input(file_name=file_name)
resolution = pdb_inp.resolution()
data_type = pdb_inp.get_experiment_type()
if (resolution is not None and resolution <= d_min):
if (data_type in ["X-RAY DIFFRACTION", "NEUTRON DIFFRACTION"]):
pdb_hierarchy = pdb_inp.construct_hierarchy()
xray_structure = pdb_hierarchy.extract_xray_structure()
xray_structure.convert_to_isotropic()
pdb_hierarchy.adopt_xray_structure(xray_structure)
if (filter_non_protein_and_hd):
pdb_hierarchy = keep_protein_only(pdb_hierarchy=pdb_hierarchy)
if (pdb_hierarchy is not None):
if (have_conformers(pdb_hierarchy=pdb_hierarchy)):
pdb_hierarchy.remove_alt_confs(
always_keep_one_conformer=True)
pdb_hierarchy = add_hydrogens_using_ReadySet(
pdb_hierarchy=pdb_hierarchy)
box = box_pdb(pdb_hierarchy=pdb_hierarchy)
fq = fragments(
pdb_hierarchy=box.pdb_hierarchy,
maxnum_residues_in_cluster=maxnum_residues_in_cluster,
debug=True,
crystal_symmetry=box.cs)
print "clusters:", fq.clusters
os.mkdir(prefix)
cntr = 0
for fname in os.listdir(os.getcwd()):
if fname.endswith("_cluster.pdb"):
if check_missing_atom(pdb_filename=fname):
os.remove(fname)
else:
cl_fn = "%s_%d.pdb" % (prefix, cntr)
fo = open(cl_fn, "w")
ph_i = iotbx.pdb.input(
file_name=fname).construct_hierarchy()
ph_i = completion.run(pdb_hierarchy=ph_i,
crystal_symmetry=box.cs,
model_completion=False)
pdb_str = ph_i.as_pdb_string(
crystal_symmetry=box.cs, append_end=True)
pdb_str = validate(pdb_str=pdb_str)
if (pdb_str is None): continue
charge = charges_class(
raw_records=pdb_str).get_total_charge()
fo.write("REMARK charge= %s \n" % str(charge))
fo.write(pdb_str)
fo.close()
cntr += 1
libtbx.easy_run.fully_buffered("mv %s %s" %
(cl_fn, prefix))
libtbx.easy_run.fully_buffered("rm -rf *.pdb ase/")
if not os.listdir(prefix):
os.rmdir(prefix)
print "Empty dir"
def run(maxnum_residues_in_cluster):
result = []
for clustering in [True, False]:
if 0: print(" clustering", clustering, "-"*30)
model = get_model()
fq = from_cctbx(restraints_manager = model.get_restraints_manager())
if(clustering):
fm = fragments(
working_folder = os.path.split("./ase/tmp_ase.pdb")[0]+ "/",
clustering_method = betweenness_centrality_clustering,
maxnum_residues_in_cluster = maxnum_residues_in_cluster,
altloc_method = "subtract",
charge_embedding = False,
two_buffers = False,
clustering = clustering,
pdb_hierarchy = model.get_hierarchy().deep_copy(),
qm_engine_name = "mopac",
fast_interaction = True,
crystal_symmetry = model.crystal_symmetry())
else:
fc = fq
fc = from_cluster(
restraints_manager = fq,
fragment_manager = fm,
parallel_params = get_master_phil().extract())
energy, gradients = fc.target_and_gradients(sites_cart=model.get_sites_cart())
gradients = gradients.as_double()
result.append(gradients.deep_copy())
diff = flex.abs(result[0] - result[1])
max_diff = flex.max(diff)
#print " max(diff_grad):", max_diff
assert max_diff < 1.e-9
def run(pdb_file, log, maxnum_residues_in_cluster=15):
print >> log, "max number of residues in each cluster:\n", maxnum_residues_in_cluster
pdb_inp = iotbx.pdb.input(pdb_file)
ph = pdb_inp.construct_hierarchy()
cs = pdb_inp.crystal_symmetry()
fq = fragments(
pdb_hierarchy=ph,
crystal_symmetry=cs,
maxnum_residues_in_cluster=int(maxnum_residues_in_cluster),
qm_run=False) # not run qm_calculation, just the clustering result
print >> log, "Residue indices for each cluster:\n", fq.clusters
def run(file_name,
d_min=0.9,
maxnum_residues_in_cluster=5,
filter_non_protein=True):
pdb_inp = iotbx.pdb.input(file_name=file_name)
pdb_hierarchy = pdb_inp.construct_hierarchy()
fq = fragments(pdb_hierarchy=pdb_hierarchy,
maxnum_residues_in_cluster=maxnum_residues_in_cluster,
debug=True,
crystal_symmetry=pdb_inp.crystal_symmetry())
print "clusters:", fq.clusters
def run(file_name,
d_min=0.9,
maxnum_residues_in_cluster=5,
filter_non_protein=True):
pdb_inp = iotbx.pdb.input(file_name=file_name)
pdb_hierarchy = pdb_inp.construct_hierarchy()
pdb_hierarchy = keep_protein_only(pdb_hierarchy=pdb_hierarchy)
pdb_hierarchy.remove_alt_confs(always_keep_one_conformer=True)
fq = fragments(pdb_hierarchy=pdb_hierarchy,
maxnum_residues_in_cluster=maxnum_residues_in_cluster,
debug=True,
crystal_symmetry=pdb_inp.crystal_symmetry())
print "clusters:", fq.clusters
def run(prefix):
"""
Exercise combined energy and gradients from cluster qm.
"""
for restraints in ["cctbx", "qm"]:
if 0:
print("Using restraints:", restraints)
result = []
for clustering in [True, False]:
if 0:
print(" clustering", clustering, "-" * 30)
model = get_model()
if (restraints == "qm"):
fq = from_qm(pdb_hierarchy=model.get_hierarchy(),
qm_engine_name="mopac",
method="PM3",
crystal_symmetry=model.crystal_symmetry(),
clustering=clustering)
else:
fq = from_cctbx(
restraints_manager=model.get_restraints_manager())
if (clustering):
fm = fragments(
working_folder=os.path.split("./ase/tmp_ase.pdb")[0] + "/",
clustering_method=betweenness_centrality_clustering,
maxnum_residues_in_cluster=8,
charge_embedding=False,
two_buffers=False,
fast_interaction=True,
pdb_hierarchy=model.get_hierarchy().deep_copy(
), # deep copy just in case
qm_engine_name="mopac",
crystal_symmetry=model.crystal_symmetry())
fc = from_cluster(restraints_manager=fq,
fragment_manager=fm,
parallel_params=get_master_phil().extract())
else:
fc = fq
energy, gradients = fc.target_and_gradients(
sites_cart=model.get_sites_cart())
if (restraints == "qm"):
energy = energy * (kcal / mol) * (kcal / mol) / Hartree
gradients = gradients * (kcal / mol) * (kcal / mol) * (Bohr /
Hartree)
gradients = gradients.as_double()
result.append(gradients.deep_copy())
#
diff = flex.abs(result[0] - result[1])
max_diff = flex.max(diff)
def run(pdb_file, log):
pdb_inp = iotbx.pdb.input(pdb_file)
ph = pdb_inp.construct_hierarchy()
cs = pdb_inp.crystal_symmetry()
fq = fragments(pdb_hierarchy=ph,
crystal_symmetry=cs,
charge_embedding=True,
debug=True,
qm_engine_name="terachem")
print >> log, "Residue indices for each cluster:\n", fq.clusters
fq_ext = fragment_extracts(fq)
for i in xrange(len(fq.clusters)):
# add capping for the cluster and buffer
print >> log, "capping frag:", i
get_qm_file_name_and_pdb_hierarchy(fragment_extracts=fq_ext, index=i)
print >> log, "point charge file:", i
#write mm point charge file
write_mm_charge_file(fragment_extracts=fq_ext, index=i)
def generate_restraints(cs, ph, clustering=False):
fq = from_qm(pdb_hierarchy=ph,
qm_engine_name="mopac",
crystal_symmetry=cs,
clustering=clustering)
if (clustering):
fm = fragments(working_folder=os.path.split("./ase/tmp_ase.pdb")[0] +
"/",
clustering_method=betweenness_centrality_clustering,
maxnum_residues_in_cluster=8,
charge_embedding=False,
two_buffers=True,
pdb_hierarchy=ph,
qm_engine_name="mopac",
fast_interaction=True,
crystal_symmetry=cs)
restraints = from_cluster(restraints_manager=fq,
fragment_manager=fm,
parallel_params=get_master_phil().extract())
else:
restraints = fq
return restraints
def run(pdb_file, log):
pdb_inp = iotbx.pdb.input(pdb_file)
ph = pdb_inp.construct_hierarchy()
cs = pdb_inp.crystal_symmetry()
print >> log, '\n\tfragmenting "%s"' % pdb_file
t0 = time.time()
#
# use a pickle file of fragments so testing of parallel is snappy
#
fq_fn = '%s_fq.pickle' % pdb_file.replace('.pdb', '')
if not os.path.exists(fq_fn):
fq = fragments(pdb_hierarchy=ph,
crystal_symmetry=cs,
charge_embedding=True,
debug=False,
qm_engine_name="orca")
print >> log, '\n\tfragmenting took %0.1f\n' % (time.time() - t0)
print >> log, "Residue indices for each cluster:\n", fq.clusters
fq_ext = fragment_extracts(fq)
f = file(fq_fn, 'wb')
pickle.dump(fq_ext, f)
f.close()
else:
f = file(fq_fn, 'rb')
fq_ext = pickle.load(f)
f.close()
#
# get QM engine
#
qm_engine = Orca()
if 0:
#
# use parallel_map
#
#indices = range(len(fq.clusters))
indices = range(len(fq_ext.fragment_selections))
rc = run_all(
qm_engine,
fq_ext,
indices,
processes=6,
)
print rc
elif 0:
#
# use multi_core_run
#
from libtbx import easy_mp
nproc = 6
argss = []
results = []
#for i in xrange(len(fq.clusters)):
for i in range(len(fq_ext.fragment_selections)):
argss.append([qm_engine, fq_ext, i])
for args, res, err_str in easy_mp.multi_core_run(
get_qm_energy,
argss,
nproc,
):
print '%sTotal time: %6.2f (s)' % (' ' * 7, time.time() - t0)
print args[-1], res
if err_str:
print 'Error output from %s' % args
print err_str
print '_' * 80
results.append([args, res, err_str])
print '-' * 80
for args, res, err_str in results:
print args[-1], res
else:
#
# serial
#
#for i in xrange(len(fq.clusters)):
for i in range(len(fq_ext.fragment_selections)):
# add capping for the cluster and buffer
print >> log, "capping frag:", i
energy = get_qm_energy(qm_engine, fq_ext, i)
print >> log, " frag:", i, " energy:", energy, ' time:', print_time(
time.time() - t0)
time.sleep(10)
def run(prefix):
"""
Exercise combined energy and gradients from cluster qm.
"""
#e = -4770.51146839
e = -0.0188567309879
g = [
-0.08456615549999999, 0.10127334349999997, -0.10242523249999999,
0.005342293499999999, -0.11995874419999995, -0.07123394619999997,
-0.0181705993, 0.10454728429999999, -0.04413921959999999,
0.009679603399999999, -0.009121438199999998, 0.017765005099999998,
-0.10292599449999999, -0.021032337599999992, 0.19227364749999998,
0.20080026099999998, 0.07756343689999999, 0.04037623699999999,
0.022457268599999993, -0.19447525149999997, -0.08313646499999999,
0.08618981049999999, 0.11223422999999998, -0.03263333429999998,
-0.12023754289999997, 0.04570431589999999, 0.07184392599999999,
-0.09959662779999998, 0.05599450589999998, -0.18692389119999994,
0.14166337219999994, 0.10928884749999995, -0.00010803089999999996,
-0.09286083449999998, 0.0070887217999999995, -0.042819663499999994,
0.011747478199999996, 0.0020709142999999998, 0.0155718881,
0.09978134159999996, -0.16828699659999996, 0.20112003719999993,
-0.13691679659999995, 0.04693286069999999, -0.020098479499999995,
0.006421124799999999, -0.14228682679999996, 0.03074080909999999,
-0.06345386689999997, 0.0540718306, -0.2192631818999999,
-0.09424992719999997, 0.0876742437, 0.08769038159999999,
-0.017843138899999996, -0.020051329499999992, -0.10296862299999995,
0.0036765932, -0.0011355988999999997, 0.0336712045,
0.14390991449999999, -0.058120583499999996, 0.23378543549999994,
-0.04526928529999999, -0.09279928339999997, -0.10211093629999997,
0.13913907279999996, -0.021608406899999995, 0.03163317669999999,
-0.046078754099999994, 0.08021273509999997, -0.2133580607,
-0.09506438349999997, -0.11225534059999999, 0.002175395599999999,
0.02810503409999999, 0.034700818599999995, -0.04577220239999999,
-0.0031643896, 0.006103702599999998, -0.013420845099999999,
0.04734791989999999, -0.04087237219999999, 0.27310615299999996,
0.09222950209999997, 0.030048884899999996, -0.10796715279999998,
0.024249821599999992, 0.08609433989999997, 0.11100423779999997,
-0.0908682388, 0.11657135879999994, -0.18054697989999993, 0.1363704825,
-0.0612450326, -0.06956100439999999, -0.05383942109999999,
0.07699810309999998, -0.03252626399999999, 0.014023944299999999,
-0.007865846099999999, 0.013593557999999999, 0.03773802329999999,
-0.1629163042, 0.22817627809999996, -0.038193966499999996,
0.13891371279999998, -0.010524218799999997, -0.08517528979999998,
-0.07180391979999999, 0.08937603319999998, -0.08876329529999998,
0.06411278599999999, -0.20456718929999995, 0.06752903599999999,
0.14215725829999998, 0.047365303699999986, -0.07095322129999998,
-0.015525017399999997, -0.0555983734, 0.010615422799999996,
0.0017179213999999993, 0.0119133477, 0.13235639709999997,
-0.13802391619999999, 0.21295888619999995, -0.13386174689999994,
-0.012668172499999998, -0.05137007749999999, 0.0643506372,
-0.12679474069999994, 0.013655725699999996, -0.042072087299999984,
0.0728243264, -0.22654012409999996, -0.14573698399999999,
0.025316557999999992, 0.06546370409999999, 0.03143893629999999,
-0.0021188467, -0.08296277749999997, -0.008833872099999996,
-0.004236358899999999, 0.019423341599999998, 0.10096460029999998,
-0.04916252659999999, 0.2626485158, 0.02541927949999999,
-0.06881671389999998, -0.12597456279999997, 0.12323139989999995,
0.02877757349999999, 0.06864828369999999, -0.013625003799999995,
0.11928656539999999, -0.21394597359999995, -0.007209919599999998,
-0.15212524499999996, -0.051973657499999985, 0.021747500799999993,
0.04431474299999998, -0.023677993699999997, -0.002670129099999999,
0.0019006115999999997, -0.007955863099999998, -0.024645625899999992,
-0.09768972109999997, 0.26912261289999995, 0.08742696429999998,
0.10077918939999997, -0.05481312519999999, -0.06490659249999998,
0.045055832199999986, 0.11830992419999996, -0.04679164979999999,
0.10801289749999997, -0.20996703789999993, 0.1619887609, 0.0189571961,
-0.014714428099999996, -0.0779905827, 0.052030067499999985,
-0.04978193019999999, 0.013969698299999996, -0.008418040199999999,
0.022545170099999994, 0.027116685899999996, -0.16116820019999997,
0.23463616229999995, -0.09975993879999998, 0.10186490829999997,
-0.0291957566, -0.060009407699999996, -0.12147408619999997,
0.04644019639999999, -0.03950867969999999, 0.06864468099999999,
-0.21645581219999993, -0.04352159619999999, 0.13685605209999996,
0.06394913949999997, -0.029060687099999993, -0.035780113999999995,
-0.07880487589999997, 0.0012057329999999998, -0.003457790599999999,
0.008208808999999997, 0.10429058969999998, -0.10613049529999999,
0.24350930369999996, -0.0803707538, -0.0717863952,
-0.09746923109999997, 0.11602792729999999, -0.07391092899999997,
0.037259427599999985, -0.0015554781999999997, 0.08293186139999999,
-0.20200613059999997, -0.14833666199999998, -0.05584847209999998,
-0.0069063241, 0.04999217049999999, 0.011683344699999997,
-0.07303952619999997, 0.004491002799999999, 0.0008760120999999998,
0.010574099399999997, 0.02846439539999999, -0.040399862999999994,
0.2780464814, 0.09680116969999997, -0.017292296299999996,
-0.10586559739999998, 0.05384248459999999, 0.0748885907,
0.10969722139999996, -0.03653972, 0.11935011549999996,
-0.19070836209999995, 0.08581903909999998, -0.12018963119999997,
-0.04881325499999999, -0.017609414599999998, 0.06085935429999999,
-0.06904357269999999, 0.002511758599999999, 0.007746915999999999,
0.0158303274, -0.025517339699999995, -0.13106877169999998,
0.25135694819999993, 0.030080269599999994, 0.13709668719999998,
-0.033244503599999996, -0.09850133139999998, -0.007615988199999999,
0.10309588899999998, -0.08454689749999997, 0.08028340959999998,
-0.17584256699999995, 0.11473156629999999, 0.0690920467,
0.0035207403999999998, 0.1530698161, 0.06053849989999999,
0.004663570399999998, -0.05215193699999999, -0.03866576739999999,
-0.050896319599999996, -0.07301203669999999, -0.0208231669,
0.017359334199999993, 0.09495519929999997, -0.15792162779999994,
0.21719830599999995, -0.12020762609999999, 0.06283927129999999,
-0.044325136599999995, -0.02876324339999999, -0.12972709869999993,
0.04722357629999999
]
pdb_inp = iotbx.pdb.input(
os.path.join(qr_unit_tests, "data_files", "helix.pdb"))
ph = pdb_inp.construct_hierarchy()
cs = pdb_inp.crystal_symmetry()
sites_cart = ph.atoms().extract_xyz()
fq = from_qm(
pdb_hierarchy=ph,
qm_engine_name="mopac",
crystal_symmetry=cs,
clustering=True,
)
fm = fragments(working_folder=os.path.split("./ase/tmp_ase.pdb")[0] + "/",
clustering_method=betweenness_centrality_clustering,
maxnum_residues_in_cluster=8,
charge_embedding=False,
pdb_hierarchy=ph,
qm_engine_name="mopac",
crystal_symmetry=cs)
fc = from_cluster(
restraints_manager=fq,
fragment_manager=fm,
parallel_params=get_master_phil().extract(),
)
energy, gradients = fc.target_and_gradients(sites_cart=sites_cart)
energy = energy * (kcal / mol) * (kcal / mol) / Hartree
gradients = gradients * (kcal / mol) * (kcal / mol) * (Bohr / Hartree)
gradients = list(gradients.as_double())
os.system("rm -rf ase")
assert approx_equal(energy, e, 1.0E-4)
