def calcGNMfeatures(self, chain='all', env='chain', GNM_PRS=True):
"""Computes GNM-based features.
:arg chain: chain identifier
:type chain: str
:arg env: environment model, i.e. ``'chain'``, ``'reduced'`` or
``'sliced'``
:type env: str
:arg GNM_PRS: whether or not to compute features based on Perturbation
Response Scanning analysis
:type GNM_PRS: bool
"""
assert env in ['chain', 'reduced', 'sliced']
assert type(GNM_PRS) is bool
# list of features to be computed
features = ['GNM_MSF-'+env]
if GNM_PRS:
features += ['GNM_effectiveness-'+env, 'GNM_sensitivity-'+env]
# compute features (if not precomputed)
if chain == 'all':
chain_list = self.chids
else:
chain_list = [chain, ]
for chID in chain_list:
d = self.feats[chID]
if all([f in d for f in features]):
continue
try:
gnm = self.calcGNM(chID, env=env)
except Exception as e:
if (isinstance(e, MemoryError)):
msg = 'MemoryError'
else:
msg = str(e)
for f in features:
d[f] = msg
LOGGER.warn(msg)
continue
key_msf = 'GNM_MSF-' + env
if key_msf not in d:
try:
d[key_msf] = calcSqFlucts(gnm)
except Exception as e:
msg = str(e)
d[key_msf] = msg
LOGGER.warn(msg)
key_eff = 'GNM_effectiveness-' + env
if key_eff in features and key_eff not in d:
key_sns = 'GNM_sensitivity-' + env
try:
prs_mtrx, eff, sns = calcPerturbResponse(gnm)
d[key_eff] = eff
d[key_sns] = sns
except Exception as e:
msg = str(e)
d[key_eff] = msg
d[key_sns] = msg
LOGGER.warn(msg)
return
def calcGNMfeatures(self, chain='all', env='chain', GNM_PRS=True):
assert env in ['chain', 'reduced', 'sliced']
assert type(GNM_PRS) is bool
# list of features to be computed
features = ['GNM_MSF-' + env]
if GNM_PRS:
features += ['GNM_effectiveness-' + env, 'GNM_sensitivity-' + env]
# compute features (if not precomputed)
if chain == 'all':
chain_list = self.chids
else:
chain_list = [
chain,
]
for chID in chain_list:
d = self.feats[chID]
if all([f in d for f in features]):
continue
try:
gnm = self.calcGNM(chID, env=env)
except Exception as e:
if (isinstance(e, MemoryError)):
msg = 'MemoryError'
else:
msg = str(e)
for f in features:
d[f] = msg
LOGGER.warn(msg)
continue
key_msf = 'GNM_MSF-' + env
if key_msf not in d:
try:
d[key_msf] = calcSqFlucts(gnm)
except Exception as e:
msg = str(e)
d[key_msf] = msg
LOGGER.warn(msg)
key_eff = 'GNM_effectiveness-' + env
if key_eff in features and key_eff not in d:
key_sns = 'GNM_sensitivity-' + env
try:
prs_mtrx, eff, sns = calcPerturbResponse(gnm)
d[key_eff] = eff
d[key_sns] = sns
except Exception as e:
msg = str(e)
d[key_eff] = msg
d[key_sns] = msg
LOGGER.warn(msg)
return
def get_perturbations(enm, n_modes=6):
"""
Calculates perturbation response based on an elastic network model
Parameters
----------
enm
n_modes
number of modes to consider
Returns
-------
Effectiveness - how effective each residue is in perturbing other residues
Sensitivity - how sensitive a residue is to perturbation by other residues
"""
_, effectiveness, sensitivity = pd.calcPerturbResponse(enm[:n_modes])
return effectiveness, sensitivity
def calcANMfeatures(self,
chain='all',
env='chain',
ANM_PRS=True,
stiffness=True,
MBS=False):
assert env in ['chain', 'reduced', 'sliced']
for k in ANM_PRS, stiffness, MBS:
assert type(k) is bool
# list of features to be computed
features = ['ANM_MSF-' + env]
if ANM_PRS:
features += ['ANM_effectiveness-' + env, 'ANM_sensitivity-' + env]
if MBS:
features += ['MBS-' + env]
if stiffness:
features += ['stiffness-' + env]
# compute features (if not precomputed)
if chain == 'all':
chain_list = self.chids
else:
chain_list = [
chain,
]
for chID in chain_list:
d = self.feats[chID]
if all([f in d for f in features]):
continue
try:
anm = self.calcANM(chID, env=env)
except Exception as e:
if (isinstance(e, MemoryError)):
msg = 'MemoryError'
else:
msg = str(e)
for f in features:
d[f] = msg
LOGGER.warn(msg)
continue
key_msf = 'ANM_MSF-' + env
if key_msf not in d:
try:
d[key_msf] = calcSqFlucts(anm)
except Exception as e:
msg = str(e)
d[key_msf] = msg
LOGGER.warn(msg)
key_eff = 'ANM_effectiveness-' + env
if key_eff in features and key_eff not in d:
key_sns = 'ANM_sensitivity-' + env
try:
prs_mtrx, eff, sns = calcPerturbResponse(anm)
d[key_eff] = eff
d[key_sns] = sns
except Exception as e:
msg = str(e)
d[key_eff] = msg
d[key_sns] = msg
LOGGER.warn(msg)
key_mbs = 'MBS-' + env
if key_mbs in features and key_mbs not in d:
try:
pdb = self.getPDB()
ca = pdb[chID].ca
d[key_mbs] = calcMBS(anm, ca, cutoff=15.)
except Exception as e:
msg = str(e)
d[key_mbs] = msg
LOGGER.warn(msg)
key_stf = 'stiffness-' + env
if key_stf in features and key_stf not in d:
try:
pdb = self.getPDB()
ca = pdb[chID].ca
stiff_mtrx = calcMechStiff(anm, ca)
d[key_stf] = np.mean(stiff_mtrx, axis=0)
except Exception as e:
msg = str(e)
d[key_stf] = msg
LOGGER.warn(msg)
return
def calcANMfeatures(self, chain='all', env='chain',
ANM_PRS=True, stiffness=True, MBS=False):
"""Computes ANM-based features.
:arg chain: chain identifier
:type chain: str
:arg env: environment model, i.e. ``'chain'``, ``'reduced'`` or
``'sliced'``
:type env: str
:arg ANM_PRS: whether or not to compute features based on Perturbation
Response Scanning analysis
:type ANM_PRS: bool
:arg stiffness: whether or not to compute stiffness with MechStiff
:type stiffness: bool
:arg MBS: whether or not to compute Mechanical Bridging Score
:type MBS: bool
"""
assert env in ['chain', 'reduced', 'sliced']
for k in ANM_PRS, stiffness, MBS:
assert type(k) is bool
# list of features to be computed
features = ['ANM_MSF-'+env]
if ANM_PRS:
features += ['ANM_effectiveness-'+env, 'ANM_sensitivity-'+env]
if MBS:
features += ['MBS-'+env]
if stiffness:
features += ['stiffness-'+env]
# compute features (if not precomputed)
if chain == 'all':
chain_list = self.chids
else:
chain_list = [chain, ]
for chID in chain_list:
d = self.feats[chID]
if all([f in d for f in features]):
continue
try:
anm = self.calcANM(chID, env=env)
except Exception as e:
if (isinstance(e, MemoryError)):
msg = 'MemoryError'
else:
msg = str(e)
for f in features:
d[f] = msg
LOGGER.warn(msg)
continue
key_msf = 'ANM_MSF-' + env
if key_msf not in d:
try:
d[key_msf] = calcSqFlucts(anm)
except Exception as e:
msg = str(e)
d[key_msf] = msg
LOGGER.warn(msg)
key_eff = 'ANM_effectiveness-' + env
if key_eff in features and key_eff not in d:
key_sns = 'ANM_sensitivity-' + env
try:
prs_mtrx, eff, sns = calcPerturbResponse(anm)
d[key_eff] = eff
d[key_sns] = sns
except Exception as e:
msg = str(e)
d[key_eff] = msg
d[key_sns] = msg
LOGGER.warn(msg)
key_mbs = 'MBS-' + env
if key_mbs in features and key_mbs not in d:
try:
pdb = self.getPDB()
ca = pdb[chID].ca
d[key_mbs] = calcMBS(anm, ca, cutoff=15.)
except Exception as e:
msg = str(e)
d[key_mbs] = msg
LOGGER.warn(msg)
key_stf = 'stiffness-' + env
if key_stf in features and key_stf not in d:
try:
pdb = self.getPDB()
ca = pdb[chID].ca
stiff_mtrx = calcMechStiff(anm, ca)
d[key_stf] = np.mean(stiff_mtrx, axis=0)
except Exception as e:
msg = str(e)
d[key_stf] = msg
LOGGER.warn(msg)
return