def setUpClass(self):
from htmd.simlist import simlist, simfilter
from glob import glob
from htmd.projections.metric import Metric
from moleculekit.projections.metricdistance import MetricDistance
from moleculekit.projections.metricdihedral import MetricDihedral
from moleculekit.util import tempname
from htmd.home import home
from os.path import join
sims = simlist(
glob(join(home(dataDir="adaptive"), "data", "*", "")),
glob(join(home(dataDir="adaptive"), "input", "*")),
)
fsims = simfilter(sims, tempname(), "not water")
metr = Metric(fsims)
metr.set(
MetricDistance(
"protein and resid 10 and name CA",
"resname BEN and noh",
periodic="selections",
metric="contacts",
groupsel1="residue",
threshold=4,
)
)
self.data1 = metr.project()
metr.set(MetricDihedral())
self.data2 = metr.project()
def __init__(self,
sel1,
sel2,
numshells=4,
shellwidth=3,
pbc=True,
gap=None,
truncate=None):
super().__init__()
from moleculekit.projections.metricdistance import MetricDistance
self.symmetrical = sel1 == sel2
self.metricdistance = MetricDistance(
sel1=sel1,
sel2=sel2,
groupsel1=None,
groupsel2=None,
metric="distances",
threshold=8,
pbc=pbc,
truncate=truncate,
)
self.numshells = numshells
self.shellwidth = shellwidth
self.description = None
self.shellcenters = None
def setUpClass(self):
from htmd.simlist import simlist, simfilter
from glob import glob
from htmd.projections.metric import Metric
from moleculekit.projections.metricdistance import MetricDistance
from moleculekit.projections.metricdihedral import MetricDihedral
from moleculekit.util import tempname
from htmd.home import home
from os.path import join
sims = simlist(glob(join(home(dataDir='adaptive'), 'data', '*', '')),
glob(join(home(dataDir='adaptive'), 'input', '*')))
fsims = simfilter(sims, tempname(), 'not water')
metr = Metric(fsims)
metr.set(
MetricDistance('protein and resid 10 and name CA',
'resname BEN and noh',
metric='contacts',
groupsel1='residue',
threshold=4))
self.data1 = metr.project()
metr.set(MetricDihedral())
self.data2 = metr.project()
def mygoalfunction(mol):
distance_metric = MetricDistance(
'protein and resname HID and resid 42 and name CA',
'resname MOL and name C11')
distance = distance_metric.project(mol)
distance[distance < 20.0] = 1.0
print('THE PROJECTION VALUES:', distance)
return -distance # or even 1/distance
def compute_salt_bridges(self):
salts = []
[
self.reps.remove(index)
for index, rep in reversed(list(enumerate(self.reps.replist)))
]
metr = MetricDistance('sidechain and acidic and element O',
'sidechain and basic and element N',
metric="contacts",
threshold=3.2,
pbc=False)
try:
data = metr.project(self)
mapping = metr.getMapping(self)
if len(np.shape(data)) > 1:
data = data[0].copy() # handling NMR structures
self.reps.add(sel='protein', style='NewCartoon', color=8)
if mapping[data].atomIndexes.values.any():
for salt in mapping[data].atomIndexes.values:
resid1 = self.get(
"resid", sel=f"same residue as index {salt[0]}")[0]
chain1 = self.get(
"chain", sel=f"same residue as index {salt[0]}")[0]
resid2 = self.get(
"resid", sel=f"same residue as index {salt[1]}")[0]
chain2 = self.get(
"chain", sel=f"same residue as index {salt[1]}")[0]
if [resid1, resid2] not in salts:
salts.append({
"residues": [int(resid1), int(resid2)],
"chain": [chain1, chain2]
})
self.reps.add(f"protein and resid {resid1}",
style="Licorice",
color="1")
self.reps.add(f"protein and resid {resid2}",
style="Licorice",
color="0")
except:
logger.error("Molecule has no basic or acidic residues")
raise
graph = make_graph_salts(salts)
comp, _ = label_components(graph)
if comp.a.size != 0:
salts = add_networks_salts(graph, comp)
else:
logger.warning('No salt bridges present in the structure')
return salts
def main(argv):
inputfile = ''
outputfile = ''
try:
opts, args = getopt.getopt(argv, "hi:o:", ["ifile=", "ofile="])
except getopt.GetoptError("usage:"):
print('salt_bridges.py -i <inputfile> -o <outputfile>')
sys.exit(2)
for opt, arg in opts:
if opt == '-h':
print('salt_bridges.py -i <inputfile> -o <outputfile>')
sys.exit()
elif opt in ("-i", "--ifile"):
inputfile = arg
elif opt in ("-o", "--ofile"):
outputfile = arg
#1. Load molecule
logger.info("Filtering and writing PDB")
mol = filter_mol(inputfile)
#2. Compute distances
logger.info("Computing distances among all polar residues")
metr = MetricDistance('chain A and sidechain and acidic and element O',
'chain A and sidechain and basic and element N',
metric="contacts",
threshold=3.2,
pbc=False)
try:
data = metr.project(mol)
except:
logger.error("Molecule has no basic or acidic residues")
raise
if len(np.shape(data)) > 1:
data = data[0].copy() # handling NMR structures
mapping = metr.getMapping(mol)
#3. Write txt and vmd session out
write_salt_bridges(data, mapping, mol, outputfile)
inputfile_processed = f"{inputfile[:-4]}-chainA.pdb"
postprocess_session(inputfile_processed, outputfile)
logger.info("Saving VMD session")
def compute_salt_bridges(self):
salts = []
[
self.reps.remove(index)
for index, rep in reversed(list(enumerate(self.reps.replist)))
]
metr = MetricDistance('sidechain and acidic and element O',
'sidechain and basic and element N',
metric="contacts",
threshold=3.2,
pbc=False)
try:
data = metr.project(self)
except:
logger.error("Molecule has no basic or acidic residues")
raise
if len(np.shape(data)) > 1:
data = data[0].copy() # handling NMR structures
mapping = metr.getMapping(self)
self.reps.add(sel='protein', style='NewCartoon', color=8)
if mapping[data].atomIndexes.values.any():
for bond in mapping[data].atomIndexes.values:
resid1 = self.get("resid",
sel=f"same residue as index {bond[0]}")[0]
resid2 = self.get("resid",
sel=f"same residue as index {bond[1]}")[0]
if [resid1, resid2] not in salts:
salts.append([resid1, resid2])
self.reps.add(f"protein and resid {resid1}",
style="Licorice",
color="1")
self.reps.add(f"protein and resid {resid2}",
style="Licorice",
color="0")
else:
logger.warning("No salt bridges found in this protein")
return salts
def test_adaptive(self):
from sklearn.cluster import MiniBatchKMeans
from jobqueues.localqueue import LocalCPUQueue
from moleculekit.projections.metricdistance import MetricDistance
import numpy as np
import random
np.random.seed(
0) # Needed for the clustering to always give same results
random.seed(0)
md = AdaptiveBandit()
md.app = LocalCPUQueue()
md.generatorspath = 'generators'
md.inputpath = 'input'
md.datapath = 'data'
md.coorname = 'input.coor'
md.filter = True
md.filtersel = 'all'
md.clustmethod = MiniBatchKMeans
md.projection = MetricDistance('protein resid 173 and name CA',
'resname BEN and name C1 C2 C3 C7',
periodic='selections')
md.ticadim = 2
md.nmin = 1
md.nmax = 2
md.nepochs = 9999
md.nframes = 1000000
md.reward_method = 'mean'
md.exploration = 0.01
md.actionspace = 'tica'
md.actionpool = 0
md.recluster = False
md.save = True
md.dryrun = True
md.run()
def __init__(
self,
sel1,
sel2,
periodic,
numshells=4,
shellwidth=3,
pbc=None,
gap=None,
truncate=None,
):
super().__init__()
if pbc is not None:
raise DeprecationWarning(
"The `pbc` option is deprecated please use the `periodic` option as described in MetricDistance."
)
from moleculekit.projections.metricdistance import MetricDistance
self.symmetrical = sel1 == sel2
self.metricdistance = MetricDistance(
sel1=sel1,
sel2=sel2,
periodic=periodic,
groupsel1=None,
groupsel2=None,
metric="distances",
threshold=8,
truncate=truncate,
)
self.numshells = numshells
self.shellwidth = shellwidth
self.description = None
self.shellcenters = None
if __name__ == "__main__":
import htmd.home
import os
import shutil
from htmd.util import tempname
from moleculekit.projections.metricdistance import MetricDistance
tmpdir = tempname()
shutil.copytree(htmd.home.home() + '/data/adaptive/', tmpdir)
os.chdir(tmpdir)
md = AdaptiveMD()
# md.dryrun = True
md.nmin = 1
md.nmax = 2
md.nepochs = 3
md.ticalag = 2
md.ticadim = 3
md.updateperiod = 5
md.projection = MetricDistance('protein and name CA',
'resname BEN and noh')
md.projection = [
MetricDistance('protein and name CA', 'resname BEN and noh'),
MetricDistance('protein and name CA', 'resname BEN and noh')
]
# md.generatorspath = htmd.home()+'/data/dhfr'
# md.datapath = 'input'
# md.app = AcemdLocal(inputfile='input.acemd')
# md.app = AcemdLocal(datadir='data')
# md.run() # Takes too long (2 minutes on 780).
# Calculating how many timescales are above the lag time to limit number of macrostates
from pyemma.msm import timescales_msm
timesc = timescales_msm(data.St.tolist(), lags=self.lag, nits=macronum).get_timescales()
macronum = min(self.macronum, max(np.sum(timesc > self.lag), 2))
return macronum
if __name__ == "__main__":
import htmd.home
import os
import shutil
from htmd.util import tempname
from moleculekit.projections.metricdistance import MetricDistance
tmpdir = tempname()
shutil.copytree(htmd.home.home()+'/data/adaptive/', tmpdir)
os.chdir(tmpdir)
md = AdaptiveMD()
# md.dryrun = True
md.nmin = 1
md.nmax = 2
md.nepochs = 3
md.ticalag = 2
md.ticadim = 3
md.updateperiod = 5
md.projection = MetricDistance('protein and name CA', 'resname BEN and noh', periodic='selections')
md.projection = [MetricDistance('protein and name CA', 'resname BEN and noh', periodic='selections'), MetricDistance('protein and name CA', 'resname BEN and noh', periodic='selections')]
if __name__ == '__main__':
from htmd.simlist import simlist, simfilter
from glob import glob
from htmd.projections.metric import Metric
from moleculekit.projections.metricdistance import MetricDistance
from moleculekit.projections.metricdihedral import MetricDihedral
from moleculekit.util import tempname
from htmd.home import home
from os.path import join
testfolder = home(dataDir='adaptive')
sims = simlist(glob(join(testfolder, 'data', '*', '')), glob(join(testfolder, 'input', '*', 'structure.pdb')))
fsims = simfilter(sims, tempname(), 'not water')
metr = Metric(fsims)
metr.set(MetricDistance('protein and resid 10 and name CA', 'resname BEN and noh', metric='contacts',
groupsel1='residue', threshold=4))
data1 = metr.project()
metr.set(MetricDihedral())
data2 = metr.project()
# Testing combining of metrics
data1.combine(data2)
# Testing dimensions
assert np.array_equal(data1.description.shape, (897, 3)), 'combine not working correct'
assert np.array_equal(data1.trajectories[0].projection.shape, (6, 897)), 'combine not working correct'
assert np.array_equal(np.where(data1.description.type == 'contact')[0], [0, 1, 2, 3, 4, 5, 6, 7, 8]), 'combine not working correct'
# Testing dimension dropping / keeping
datatmp = data1.copy()
data1.dropDimensions(range(9))
def rmsdgoal(proj):
return -proj # Lower RMSDs should give higher score
tmpdir = tempname()
shutil.copytree(htmd.home.home() + "/data/adaptive/", tmpdir)
os.chdir(tmpdir)
md = AdaptiveGoal()
md.dryrun = True
md.nmin = 1
md.nmax = 2
md.nepochs = 3
md.ticalag = 2
md.ticadim = 3
md.updateperiod = 5
md.projection = MetricDistance("protein and name CA",
"resname BEN and noh",
periodic="selections")
# md.goalprojection = MetricRmsd(Molecule(htmd.home() + '/data/adaptive/generators/1/structure.pdb'),
# 'protein and name CA')
md.goalfunction = rmsdgoal
# md.app = LocalGPUQueue()
# md.run()
# Some real testing now
from moleculekit.projections.metricsecondarystructure import (
MetricSecondaryStructure, )
from moleculekit.projections.metricdistance import MetricSelfDistance
os.chdir(path.join(home(), "data", "test-adaptive"))
goalProjectionDict = {