def getRMSD(traj, nativePDB, resname, symmetries, topology=None):
"""
Computes the RMSD of a trajectory, given a native and symmetries
:param traj: Trajecotry filename
:type traj: str
:param nativePDB: Native PDB object
:type native PDB: :py:class:`.PDB`
:param resname: Resname to compute its RMSD
:type resname: str
:param symmetries: Symmetries dictionary list with independent symmetry groups
:type symmetries: list of dict
:param topology: Topology for non-pdb trajectories
:type topology: list
:return: np.array -- Array with the rmsd values of the trajectory
"""
snapshots = getSnapshots(traj)
rmsds = np.zeros(len(snapshots))
RMSDCalc = RMSDCalculator.RMSDCalculator(symmetries)
for i, snapshot in enumerate(snapshots):
snapshotPDB = atomset.PDB()
snapshotPDB.initialise(snapshot, resname=resname, topology=topology)
rmsds[i] = RMSDCalc.computeRMSD(nativePDB, snapshotPDB)
return rmsds
def testPDB_RMSD(self):
# preparation
pdb_native = atomset.PDB()
pdb_native.initialise("tests/data/ain_native_fixed.pdb", resname='AIN')
pdb_traj = atomset.PDB()
pdb_traj.initialise("tests/data/ain_trajectory.pdb", resname='AIN')
RMSDCalc = RMSDCalculator.RMSDCalculator()
# function to test
RMSD = RMSDCalc.computeRMSD(pdb_native, pdb_traj)
golden_RMSD = 3.928617
self.assertAlmostEqual(RMSD, golden_RMSD, 5)
def main(lig_resname, native_path, initial_path, up_lim, low_lim, filename):
RMSDCalc = RMSDCalculator.RMSDCalculator()
nativePDB = atomset.PDB()
nativePDB.initialise(native_path, resname=lig_resname)
filtered = []
for conf in glob.glob(
os.path.join(initial_path, "{}*.pdb".format(filename))):
initialPDB = atomset.PDB()
initialPDB.initialise(conf, resname=lig_resname)
if low_lim < RMSDCalc.computeRMSD(nativePDB, initialPDB) < up_lim:
filtered.append(conf)
print(" ".join(filtered))
def testPDB_RMSD_symmetries(self):
# preparation
pdb_native = atomset.PDB()
pdb_native.initialise("tests/data/ain_native_fixed.pdb", resname='AIN')
pdb_traj = atomset.PDB()
pdb_traj.initialise("tests/data/ain_trajectory.pdb", resname='AIN')
symDict = [{"1733:O1:AIN": "1735:O2:AIN"}]
RMSDCalc = RMSDCalculator.RMSDCalculator(symDict)
# function to test
RMSD = RMSDCalc.computeRMSD(pdb_native, pdb_traj)
reverseRMSD = RMSDCalc.computeRMSD(pdb_traj, pdb_native)
golden_RMSD = 3.860743
self.assertAlmostEqual(RMSD, reverseRMSD, 5)
self.assertAlmostEqual(RMSD, golden_RMSD, 5)
def main(resname, files_glob, native):
input_files = glob.glob(files_glob)
nativePDB = atomset.PDB()
nativePDB.initialise(native, resname=resname)
RMSDCalc = RMSDCalculator.RMSDCalculator()
results = {}
for f in input_files:
p = atomset.PDB()
p.initialise(f, resname=resname)
results[f] = RMSDCalc.computeRMSD(nativePDB, p)
with open("rmsd_file.dat", "w") as fw:
fw.write("File\tRMSD(A)\n")
for f in results:
fw.write("%s\t%.4f\n" % (f, results[f]))
def __init__(self,
thresholdCalculator,
resname=None,
reportBaseFilename=None,
columnOfReportFile=None,
contactThresholdDistance=8,
symmetries=[]):
clustering.Clustering.__init__(self, resname, reportBaseFilename,
columnOfReportFile,
contactThresholdDistance)
self.type = clusteringTypes.CLUSTERING_TYPES.contacts
self.thresholdCalculator = thresholdCalculator
self.symmetries = symmetries
self.initialPDB = None
self.maxThreshold = self.thresholdCalculator.getMaxThreshold()
self.distancesList = []
self.RMSDCalculator = RMSDCalculator.RMSDCalculator(symmetries)
def testPDB_RMSD_symmetries_XTC(self):
# preparation
golden = "tests/data/ain_native_fixed.pdb"
topology = utilities.getTopologyFile(golden)
xtc_obj = mdtraj.load("tests/data/ain_native_fixed.xtc", top=golden)
xtc = atomset.PDB()
xtc.initialise(10 * xtc_obj.xyz[0], resname="AIN", topology=topology)
golden_pdb = atomset.PDB()
golden_pdb.initialise(golden, resname="AIN")
symDict = [{"1733:O1:AIN": "1735:O2:AIN"}]
RMSDCalc = RMSDCalculator.RMSDCalculator(symDict)
# function to test
RMSD = RMSDCalc.computeRMSD(xtc, golden_pdb)
reverseRMSD = RMSDCalc.computeRMSD(golden_pdb, xtc)
golden_RMSD = 0.00000
self.assertAlmostEqual(RMSD, reverseRMSD, 2)
self.assertAlmostEqual(RMSD, golden_RMSD, 2)
def testPDB_RMSD_XTC(self):
# preparation
golden = "tests/data/ain_native_fixed.pdb"
topology = utilities.getTopologyFile(golden)
xtc_obj = mdtraj.load("tests/data/ain_native_fixed.xtc", top=golden)
xtc = atomset.PDB()
xtc.initialise(10 * xtc_obj.xyz[0], resname="AIN", topology=topology)
golden_pdb = atomset.PDB()
golden_pdb.initialise(golden, resname="AIN")
# assertion
RMSDCalc = RMSDCalculator.RMSDCalculator()
# function to test
RMSD = RMSDCalc.computeRMSD(golden_pdb, xtc)
golden_RMSD = 0.0000
self.assertAlmostEqual(RMSD, golden_RMSD, 2)
def main(args):
# Parameters
clusteringObj = utilities.readClusteringObject(args.clusteringObj)
native = args.native
pathwayFilename = args.pathwayFilename
ntrajs = args.ntrajs
threshold = args.threshold
RMSDCalc = RMSDCalculator.RMSDCalculator(clusteringObj.symmetries)
# use graph algorithm to establish a path
initial_cluster = 0
final_cluster = getOptimalCluster(clusteringObj, native, RMSDCalc)
distanceMatrix = createNetworkMatrix(clusteringObj, threshold, RMSDCalc)
predecessors = obtainShortestPath(distanceMatrix)
pathway = createPathway(initial_cluster, final_cluster, predecessors)
print "Pathway clusters:"
print pathway
# write pathway into a single trajectory
writePathwayTrajectory(clusteringObj, pathway, pathwayFilename, native)
# create clustering object with only the pathway clusters
ClPath = clustering.Clustering()
ClPath.clusters.clusters = map(
lambda x: clusteringObj.clusters.clusters[x], pathway)
# spawning along the trajectory
spawningParams = spawning.SpawningParams()
densityCalculatorBuilder = densitycalculator.DensityCalculatorBuilder()
densityCalculator = densityCalculatorBuilder.build({})
spawningPathway = spawning.InverselyProportionalToPopulationCalculator(
densityCalculator)
# Set a least 1 processors from the extrems of the path
degeneracies = spawningPathway.calculate(ClPath.clusters.clusters,
ntrajs - 2, spawningParams)
degeneracies[0] += 1
degeneracies[-1] += 1
print "degeneracies over pathway:"
print degeneracies
print ""
def main(epoch_num, trajectory, snapshot_num, resname, clustering_object, topology):
calc = RMSDCalculator.RMSDCalculator()
clustering_object = utilities.readClusteringObject(clustering_object)
n_clusters = np.loadtxt(os.path.join(str(epoch_num-1), "clustering", "summary.txt")).shape[0]
if topology is not None:
topology_contents = utilities.getTopologyFile(topology)
else:
topology_contents = None
filename = glob.glob(os.path.join(str(epoch_num), "*traj*_%d.*" % trajectory))
if not filename:
raise ValueError("No file with the specified epoch and trajectory found")
try:
snapshots = utilities.getSnapshots(filename[0], topology=topology)[snapshot_num]
except IndexError:
raise IndexError("Snapshot number %d not found in trajectory %d for epoch %d, please check that the arguments provided are correct" % (snapshot_num, trajectory, epoch_num))
pdb = atomset.PDB()
pdb.initialise(snapshots, resname=resname)
for i, cluster in enumerate(clustering_object[:n_clusters]):
dist = calc.computeRMSD(pdb, cluster.pdb)
if dist < cluster.threshold:
print("Snapshot belongs to cluster", i)
return
print("Snapshot not assigned to any cluster! :(")
from __future__ import absolute_import, division, print_function, unicode_literals
from builtins import range
import networkx as nx
from AdaptivePELE.utilities import utilities
from AdaptivePELE.atomset import RMSDCalculator
def weight(pathway, confs):
w = 0
for j in range(1, len(path)):
w += confs[pathway[j - 1]][path[j]]['metric']
return w
metricCol = 4
RMSDCalc = RMSDCalculator.RMSDCalculator()
cl = utilities.readClusteringObject("ClCont.pkl")
nodeFin = cl.getOptimalMetric(column=metricCol)
conf = nx.DiGraph()
nx.read_edgelist("conformationNetwork_4DAJ.edgelist",
create_using=conf,
data=True,
nodetype=int)
for source, target in conf.edges_iter():
clusterSource = cl.getCluster(source)
clusterTarget = cl.getCluster(target)
conf[source][target]['metric'] = RMSDCalc.computeRMSD(
clusterSource.pdb, clusterTarget.pdb)
# paths = nx.all_simple_paths(conf, 0, nodeFin)
paths = nx.shortest_simple_paths(conf, 0, nodeFin, weight='metric')