def process(args):
Sfn = args['Sfn']
# Open matrix file in parallel mode
Sf = h5py.File(Sfn, 'r')
model_list = args['pdb_list']
atypes_t = Sf['atypes'][:][0]
atypes_, rtypes = gu.choose_artypes(atypes_t)
atypes_ = Sf.keys()
try:
atypes_.remove('origin')
atypes_.remove('step')
atypes_.remove('atypes')
atypes_.remove('H') # we do not need hydrogens!
except:
pass
atypes_ = set(atypes_)
atypes = atypes_
excl = args['excl']
incl = args['incl']
if excl:
excl = set(excl)
atypes = atypes - excl
if incl:
incl = set(incl)
atypes.intersection_update(incl)
atypes = atypes
if excl and incl:
raise ('You can not use include and exclude options simultaneiously!')
print(excl, incl, atypes)
extractor = er.PepExtractor(**args)
lM = len(model_list)
# Init storage for matrices
# Get file name
# tSfn = 'tmp.' + Sfn
tSfn = args['output']
tSf = h5py.File(tSfn, 'w')
# fix of pos number // zlo
pivot_pept_ = extractor.plist[0]
pivot_pept = extractor.extract_result(pivot_pept_)
num_poses = pivot_pept.numCoordsets()
score = tSf.create_dataset('score', (lM, ), dtype=np.float)
GminXYZ = Sf['origin'][:]
step = Sf['step'][0]
NUCS = set(Sf.keys())
protected = set(['origin', 'step'])
NUCS -= protected
gNUCS = dict()
for i in NUCS:
gNUCS[i] = Sf[i][:]
for cm in tqdm.trange(lM):
m_ = model_list[cm]
S = prody.parsePDB(m_)
mscore = 0.0
ac = 0
for a in S.iterAtoms():
try:
atype = rtypes[(a.getResname(), a.getName())]
except:
continue
if atype not in atypes:
continue
C = a.getCoords()
adj = (C - GminXYZ)
adj = (adj / step).astype(np.int)
x, y, z = adj
try:
tscore = gNUCS[atype][x, y, z]
except (IndexError, ValueError):
continue
mscore += tscore
ac += 1
if ac > 0:
mscore /= float(ac)
score[cm] = mscore
tscore = np.zeros((lM, ), dtype=[('name', 'S128'), ('score', 'f8')])
for n in range(lM):
tscore['name'][n] = model_list[n]
tscore['score'] = score[:]
tscore.sort(order='score')
tscore['score'] /= tscore['score'][-1]
np.savetxt('a_score_table.csv', tscore[::-1], fmt="%s\t%.2f")
tSf.close()
Sf.close()
def process(args):
step = args['step']
padding = max(gu.avdw.values()) * 2 * \
args['pad'] # Largest VdW radii - 1.7A
box_config = dp.ConfigLedock(args['config'])
GminXYZ = box_config.get_min_xyz()
GminXYZ = gu.adjust_grid(GminXYZ, step, padding)
box = box_config.get_box_size()
L = box + padding
N = np.ceil((L / step)).astype(np.int)
GmaxXYZ = GminXYZ + N * step
print('GMIN', GminXYZ, N)
print('BOX', L)
with open('box_coords.txt', 'w') as f:
f.write('BOX: ' + ' '.join(GminXYZ.astype(np.str)) + '\n')
f.write('STEP: %.2f\n' % step)
f.write('NSTEPS: ' + ';'.join(N.astype(np.str)) + '\n')
Sfn = args['Sfn']
# atypes_, rtypes = gu.choose_artypes(args['atypes'])
atypes_ = set(gu.three_let)
try:
atypes_.remove('H') # we do not need hydrogens!
except KeyError:
pass
atypes = tuple(atypes_)
extractor = er.PepExtractor(**args)
NUCS = gu.three_let
iNUCS = dict(map(lambda x: (
x[1],
x[0],
), enumerate(NUCS)))
lnucs = len(NUCS)
fNUCS = np.zeros((lnucs, ), dtype=np.int)
# Init storage for matrices
# Get file name
tSf = dict()
for i in NUCS:
tSf[i] = np.zeros(N, dtype=np.float32)
lM = len(extractor.plist)
t0 = time.time()
for cm in range(lM):
m = extractor.plist[cm]
t1 = time.time()
dt = t1 - t0
t0 = t1
print('STEP: %d PERCENT: %.2f%% TIME: %s' %
(cm, float(cm) / lM * 100, dt))
try:
S = extractor.extract_result(m)
except:
print('ERROR: BAD PEPTIDE: %s' % m)
continue
lS = S.numCoordsets()
for S_ in range(lS):
for a in S.iterResidues():
a.setACSIndex(S_)
try:
atype = a.getResname().upper()
except:
continue
if atype not in atypes:
continue
AminXYZ, Ashape = gu.get_bounding(a.getCoords(), padding, step)
adj = (AminXYZ - GminXYZ)
adj = (adj / step).astype(np.int)
if (adj < 0).any():
continue
if ((GmaxXYZ - (AminXYZ + Ashape * step)) < 0).any():
continue
x, y, z = adj
Agrid, AminXYZ = gu.process_residue(a, padding, step)
try:
tSf[atype][x:x + Agrid.shape[0], y:y + Agrid.shape[1],
z:z + Agrid.shape[2]] += Agrid
fNUCS[iNUCS[atype]] += 1
except:
print(m, a)
pass
print('fNUCS ', fNUCS)
nNUCS = np.zeros((lnucs, ), dtype=np.float32)
ln = len(NUCS)
for i in range(ln):
mult = fNUCS[i]
if mult > 0:
ttSf = tSf[NUCS[i]]
nmax = None
nmax = np.max(ttSf)
med = np.median(ttSf)
ttSf[ttSf < (med)] = 0
ttSf /= mult
nNUCS[i] = nmax / mult
print(i, NUCS[i], nNUCS[i], nmax)
nmax = np.max(nNUCS)
# Open matrix file in parallel mode
Sf = h5py.File(Sfn, 'w')
for i in range(ln):
if mult > 0:
ttSf = tSf[NUCS[i]]
ttSf /= nmax
ttSf *= 100.0
tG = np.ceil(ttSf).astype(np.int8)
Sf.create_dataset(NUCS[i], data=tG)
else:
print('Array is empty for: ', NUCS[i])
Gstep = np.array([step, step, step], dtype=np.float32)
Sf.create_dataset('step', data=Gstep)
Sf.create_dataset('origin', data=GminXYZ)
# Sf.create_dataset('atypes', data=np.array([args['atypes'], ], dtype='S20'))
print('Totgood: ', np.sum(fNUCS))
Sf.close()
def process(self, args=None):
if not args:
args = self.args
NUCS = self.atypes
iNUCS = dict(map(lambda x: (
x[1],
x[0],
), enumerate(NUCS)))
iNUCS = self.mpi.comm.bcast(iNUCS)
lnucs = len(NUCS)
fNUCS = np.zeros((lnucs, ), dtype=np.float)
# Init storage for matrices
# Get file name
tSf = dict()
for i in NUCS:
tSf[i] = np.zeros(self.N, dtype=np.float)
args['mpi'] = self.mpi
extractor = er.PepExtractor(**args)
lM = len(self.aplist)
self.mpi.comm.Barrier()
# if self.mpi.rank == 0:
# pbar = tqdm(total=lM)
tota_ = 0
totba_ = 0
for cm in range(lM):
m = self.aplist[cm]
print('Rank %d model %d of %d' % (self.mpi.rank, cm, lM))
# if self.mpi.rank == 0:
# pbar.update(cm)
try:
S = extractor.extract_result(m)
except:
print('ERROR: BAD PEPTIDE: %s' % m)
continue
lS = S.numCoordsets()
tlS = range(lS)
if self.cluster is True:
resc = S.select('not element H').getCoordsets()
cl = 'NOSUP_SERIAL_CALCULATOR'
mHandler = MatrixHandler()
matrix = mHandler.createMatrix(resc, cl)
mat = scipy.spatial.distance.squareform(matrix.get_data())
smatrix = (mat**2) * (-1)
aff = AffinityPropagation(affinity='precomputed')
aff_cluster = aff.fit(smatrix)
tlS = aff_cluster.cluster_centers_indices_
if tlS is None:
continue
for S_ in tlS:
S.setACSIndex(S_)
for a in S.iterAtoms():
# skip hydrogens
if a.getElement() == 'H':
continue
try:
atype = self.rtypes[(a.getResname(), a.getName())]
except:
print('ATYPE not found', a.getResname(), a.getName())
if atype not in self.atypes:
continue
Agrid, AminXYZ = gu.process_atom(a, self.step)
adj = (AminXYZ - self.GminXYZ)
adj = (adj / self.step).astype(np.int)
x, y, z = adj
try:
tSf[atype][x:x + Agrid.shape[0], y:y + Agrid.shape[1],
z:z + Agrid.shape[2]] += Agrid
fNUCS[iNUCS[atype]] += 1
tota_ += 1
except:
# print(m, a)
totba_ += 1
pass
# if self.mpi.rank == 0:
# pbar.close()
self.mpi.comm.Barrier()
if self.mpi.rank == 0:
print('Collecting grids')
fNUCS_ = self.mpi.comm.allreduce(fNUCS)
nNUCS = np.zeros((lnucs, ), dtype=np.float)
tota = self.mpi.comm.reduce(tota_)
totba = self.mpi.comm.reduce(totba_)
for i in range(lnucs):
NUC_ = NUCS[i]
if self.mpi.rank != 0:
self.mpi.comm.Send(tSf[NUC_], dest=0, tag=i)
elif self.mpi.rank == 0:
for j in range(1, self.mpi.NPROCS):
tG = np.empty(tSf[NUC_].shape, dtype=np.float)
self.mpi.comm.Recv(tG, source=j, tag=i)
tSf[NUC_] += tG
nNUCS[i] = np.max(tSf[NUC_])
nNUCS_ = self.mpi.comm.bcast(nNUCS)
self.mpi.comm.Barrier()
# Allocate results file
Sfn = args['Sfn']
if self.mpi.rank == 0:
print('Saving data')
# Sf.atomic = True
nmax = bn.nanmax(np.divide(nNUCS_, fNUCS_))
Sf = h5py.File(Sfn, 'w')
for i in range(lnucs):
NUC_ = NUCS[i]
iNUC_ = iNUCS[NUC_]
mult = fNUCS_[iNUC_]
if mult > 0.0:
tG = tSf[NUC_]
med = np.median(tG)
tG[tG < (med)] = 0
tG /= float(mult)
tG /= float(nmax)
tG *= 100.0
tSf[NUC_] = tG
else:
print('Array is empty for: ', NUC_)
Sf.create_dataset(NUC_, data=tSf[NUC_])
Gstep = np.array([self.step, self.step, self.step], dtype=np.float)
Sf.create_dataset('step', data=Gstep)
Sf.create_dataset('origin', data=self.GminXYZ)
Sf.create_dataset('atypes',
data=np.array([
args['atypes'],
], dtype='S20'))
print('Total bad atoms %d of %d' % (totba, tota))
Sf.close()
self.mpi.comm.Barrier()
# Open matrix file in parallel mode
self.database.close()
def process(args):
Sfn = args['Sfn']
# Open matrix file in parallel mode
Sf = h5py.File(Sfn, 'r')
padding = max(gu.avdw.values()) * 2 * \
args['pad'] # Largest VdW radii - 1.7A
atypes_ = Sf.keys()
try:
atypes_.remove('origin')
atypes_.remove('step')
atypes_.remove('atypes')
atypes_.remove('H') # we do not need hydrogens!
except:
pass
atypes_ = set(atypes_)
atypes = atypes_
excl = args['excl']
incl = args['incl']
if excl:
excl = set(excl)
atypes = atypes - excl
if incl:
incl = set(incl)
atypes.intersection_update(incl)
atypes = atypes
if excl and incl:
raise ('You can not use include and exclude options simultaneiously!')
print(excl, incl, atypes)
extractor = er.PepExtractor(**args)
lM = len(extractor.plist)
# Init storage for matrices
# Get file name
# tSfn = 'tmp.' + Sfn
tSfn = args['output']
tSf = h5py.File(tSfn, 'w')
score = tSf.create_dataset('score', (lM * 20, ), dtype=np.float)
GminXYZ = Sf['origin'][:]
step = Sf['step'][0]
NUCS = set(Sf.keys())
protected = set(['origin', 'step'])
NUCS -= protected
gNUCS = dict()
for i in NUCS:
gNUCS[i] = Sf[i][:]
rk = gNUCS.keys()[0]
GmaxXYZ = GminXYZ + np.array(gNUCS[rk].shape[:]) * step
t0 = time.time()
for cm in range(lM):
m = extractor.plist[cm]
t1 = time.time()
dt = t1 - t0
t0 = t1
print('STEP: %d PERCENT: %.2f%% TIME: %s' %
(cm, float(cm) / lM * 100, dt))
try:
S = extractor.extract_result(m)
except:
print('ERROR: BAD PEPTIDE: %s' % m)
continue
lS = S.numCoordsets()
for S_ in range(lS):
mscore = 0.0
ac = 0
for a in S.iterResidues():
a.setACSIndex(S_)
try:
atype = a.getResname().upper()
except:
continue
if atype not in atypes:
continue
AminXYZ, Ashape = gu.get_bounding(a.getCoords(), padding, step)
adj = (AminXYZ - GminXYZ)
adj = (adj / step).astype(np.int)
x, y, z = adj
if (adj < 0).any():
continue
if ((GmaxXYZ - (AminXYZ + Ashape * step)) < 0).any():
continue
try:
tscore = gNUCS[atype][x:x + Ashape[0], y:y + Ashape[1],
z:z + Ashape[2]]
except (IndexError, ValueError):
continue
tscore = np.sum(tscore)
mscore += tscore
ac += 1
if ac > 0:
mscore /= float(ac)
mscore /= float(len(a))
score[cm * lS + S_] = mscore
tscore = np.zeros((lM * lS, ), dtype=[('name', 'S128'), ('score', 'f8')])
for n in range(lM):
for i in range(lS):
tscore['name'][n * lS +
i] = '%s_%02d.pdb' % (extractor.plist[n], i + 1)
tscore['score'] = score[:]
tscore.sort(order='score')
tscore['score'] /= tscore['score'][-1]
np.savetxt('r_score_table.csv', tscore[::-1], fmt="%s\t%.7f")
tSf.close()
Sf.close()
def process(self, args=None):
if not args:
args = self.args
# Sfn = args['Sfn']
# Open matrix file in parallel mode
# Sf = h5py.File(Sfn, 'r')
args['mpi'] = self.mpi
extractor = er.PepExtractor(**args)
lM = len(self.aplist)
dtype = np.dtype([
('name', 'S10'),
('pnum', '<i4'),
('rnum', '<i4'),
('dist', '<f8'),
('hdist1', '<f8'),
('hdist2', '<f8'),
('BD', '<f8'),
('FL', '<f8'),
('AT', '<f8'),
('dir', 'S3'),
('aln', 'S20')
])
if self.mpi.rank == 0:
m = self.aplist[0]
lm = len(m)
S = extractor.extract_result(m)
lS = S.numCoordsets()
Sf = h5py.File(args['out'], 'w')
out = Sf.create_dataset(
'out', (len(self.plist) * lS * lm, ), dtype=dtype)
Sf.close()
self.mpi.comm.Barrier()
Sf = h5py.File(args['out'], 'r+', driver='mpio', comm=self.mpi.comm)
out = Sf['out']
# Init storage for matrices
# Get file name
# tSfn = 'tmp.' + Sfn
# tSfn = args['output']
# stubs = {
# 'ACE': 'X',
# 'NME': 'Z',
# }
a = prody.parsePDB(args['receptor'])
OG = a.select('resnum 151 name SG')
Ob = a.select('resnum 168 and name O')
ND1 = a.select('resnum 46 and name NE2')
OXH = a.select("name N resnum 149 150 151")
t0 = time.time()
for cm in range(lM):
m = self.aplist[cm]
lm = len(m)
t1 = time.time()
dt = t1 - t0
t0 = t1
print('STEP: %d PERCENT: %.2f%% TIME: %s' % (
cm, float(cm) / lM * 100, dt))
try:
S = extractor.extract_result(m)
except:
print('ERROR: BAD PEPTIDE: %s' % m)
continue
lS = S.numCoordsets()
for S_ in range(lS):
S.setACSIndex(S_)
tC = S.select('name C')
dist = np.inf
rnum = None
for C in tC.iterAtoms():
rnum = C.getResnum()
if rnum == 1:
continue
O = S.select('resnum %i and name O' % rnum)
Nl = S.select('resnum %i and name N' % (rnum))
N = S.select('resnum %i and name N' % (rnum + 1))
C_ = C.getCoords()
O_ = O.getCoords()[0]
N_ = N.getCoords()[0]
dist = prody.calcDistance(C, OG)[0]
hdist1 = np.min(prody.calcDistance(OXH, O))
hdist2 = np.min(prody.calcDistance(Ob, Nl))
nC_ = np.cross((C_ - N_), (O_ - C_))
nC_ /= np.linalg.norm(nC_)
nC_ = nC_ + C_
nC_ = nC_.reshape(1, 3)
nC = C.copy()
nC.setCoords(nC_)
BD = prody.calcAngle(OG, C, O)
FL = prody.calcDihedral(OG, nC, C, O)
AT = prody.calcAngle(ND1, OG, C)
angle_ = prody.calcDihedral(ND1, OG, C, N)
# angle_ = prody.calcDistance(Od, N)[0] \
# - prody.calcDistance(Od, C)[0]
if angle_ < 0:
DIR = 'F'
else:
DIR = 'R'
s = 'X' + m + 'Z'
pref = ''
if DIR == 'F':
seq = s
pref = 6 - rnum
else:
seq = s[::-1]
pref = rnum
suf = 12 - (pref + len(seq))
seq = '-' * pref + seq + '-' * suf
# outfmt = "%-10s\t%d\t%6.2f\t%6.2f%6.2f\t%6.2f\t" \
# "%6.2f\t%6.2f\t%3s\t%12s\n"
# outstr = outfmt % (
# ('%s_%02d' % (m, S_ + 1),
# rnum, dist, hdist1, hdist2, BD, FL, AT,
# DIR, seq)
# )
outdata = (m, S_ + 1,
rnum, dist, hdist1, hdist2, BD, FL, AT,
DIR, seq)
ind = (self.tb + cm) * lS * lm + S_ * lm + rnum - 2
out[ind] = outdata
self.database.close()
Sf.close()
def process(args):
# Sfn = args['Sfn']
# Open matrix file in parallel mode
# Sf = h5py.File(Sfn, 'r')
extractor = er.PepExtractor(**args)
lM = len(extractor.plist)
# Init storage for matrices
# Get file name
# tSfn = 'tmp.' + Sfn
# tSfn = args['output']
stubs = {
'ACE': 'X',
'NME': 'Z',
}
a = prody.parsePDB(args['receptor'])
SG = a.select('resnum 241 name OG')
O = a.select('resnum 262 and name O')
ND1 = a.select('resnum 79 and name NE2')
tHN_ = a.select("name N resnum 239 240 241")
tHN = np.average(tHN_.getCoords(), axis=0)
t0 = time.time()
out = open(args['out'], 'w')
for cm in range(lM):
m = extractor.plist[cm]
t1 = time.time()
dt = t1 - t0
t0 = t1
print('STEP: %d PERCENT: %.2f%% TIME: %s' % (
cm, float(cm) / lM * 100, dt))
try:
S = extractor.extract_result(m)
except:
print('ERROR: BAD PEPTIDE: %s' % m)
continue
lS = S.numCoordsets()
for S_ in range(lS):
S.setACSIndex(S_)
tC = S.select('name C')
dist = np.inf
for c in tC.iterAtoms():
dist_ = prody.calcDistance(c, SG)[0]
if dist_ < dist:
dist = dist_
rnum = c.getResnum()
C = S.select('resnum %i and name C' % rnum)
CO = S.select('resnum %i and name O' % rnum)
# N = S.select('resnum %i and name N' % rnum_)
N_ = S.select('resnum %i and name N' % (rnum + 1))
angleAttack = prody.calcAngle(ND1, SG, C)
angle_ = prody.calcDistance(O, N_)[0] - prody.calcDistance(O, C)[0]
if angle_ > 0:
DIR = 'F'
else:
DIR = 'R'
s = 'X' + m + 'Z'
pref = ''
if DIR == 'F':
seq = s
pref = 5 - rnum
else:
seq = s[::-1]
pref = rnum
suf = 10 - (pref + len(seq))
seq = '-' * pref + seq + '-' * suf
# hangle = prody.calcAngle(tHN, C, N)
hdist = np.linalg.norm(tHN - CO.getCoords())
# HN = b.select('resnum %i and name H' % rnum)
# N_B = b.select('resnum %i and name N' % (rnum + 1))
# HN_B = b.select('resnum %i and name H' % (rnum + 1))
# distN = prody.calcDistance(O, N)[0]
# angleHN = prody.calcAngle(O, HN, N)
# distN_B = prody.calcDistance(O, N_B)[0]
# angleHN_B = prody.calcAngle(O, HN_B, N_B)
outstr = "%-10s\t%6.2f\t%6.2f\t%6.2f\t%3s\t%12s\t%d\n" % (
('%s_%02d' % (m, S_ + 1),
dist, angleAttack, hdist,
# distN, angleHN,
# distN_B, angleHN_B,
DIR, seq, rnum)
)
out.write(outstr)
# tSf.close()
# Sf.close()
# os.remove(tSfn)
out.close()