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1. UGM_2014
2. Notebooks

In [1]:

from rdkit import Chem
from rdkit.Chem import Draw, AllChem
from rdkit.Chem.Draw import IPythonConsole
import gzip, numpy

In [2]:

from rdkit.Chem import PyMol
v = PyMol.MolViewer()

In [3]:

# zinc 0903464
smi_zinc = 'Cc1cc2c(c(c1)OCC(=O)N3Cc4c(c5ccccc5[nH]4)C[C@H]3C(=O)OC)c6c(c(=O)o2)CCC6'
ref1 = Chem.MolFromSmiles(smi_zinc)
ref1

Out[3]:

In [4]:

# load pdb
ref1 = Chem.MolFromSmiles('Cc1cc2c(c(c1)OCC(=O)N3Cc4c(c5ccccc5[nH]4)C[C@H]3C(=O)OC)c6c(c(=O)o2)CCC6')
ref1 = AllChem.AddHs(ref1, addCoords=True)
mol1 = Chem.MolFromPDBFile('ligand_zinc_start.pdb', removeHs=False)
mol1 = AllChem.AssignBondOrdersFromTemplate(ref1, mol1)
mol1.RemoveAllConformers()
mol1

Out[4]:

In [5]:

from rdkit.Chem import rdConformerParser
cids1 = rdConformerParser.AddConformersFromGromosTrajectory(mol1, 'ligand_zinc_gath.trc')
print len(cids1)

In [6]:

from rdkit.Chem import rdMolAlign
for i in range(1, mol1.GetNumConformers()):
    pyO3A = rdMolAlign.GetO3A(mol1, mol1, prbCid=i, refCid=0)
    pyO3A.Align()

In [7]:

v.DeleteAll()
for cid in cids1: #[:10]:
    v.ShowMol(mol1, confId=cid, name='conf_'+str(cid), showOnly=False)

Out[8]:

In [9]:

rmsd1 = []
for cid in cids1[1:]:
  rmsd1.append(AllChem.GetConformerRMS(mol1, 0, cid, prealigned=True))

In [10]:

plt.xlabel('RMSD')
plt.ylabel('count')
hist(rmsd1)
plt.show()

In [11]:

cids12 = [0]
cids12 += [cid for cid,r in zip(cids1[1:], rmsd1) if r >= 3.0]
print len(cids12)

In [12]:

v.DeleteAll()
for cid in cids12:
    v.ShowMol(mol1, confId=cid, name='conf_'+str(cid), showOnly=False)

Out[13]:

In [14]:

rmsdmat1 = AllChem.GetConformerRMSMatrix(mol1, prealigned=True)

In [15]:

from rdkit import SimDivFilters
mmp = SimDivFilters.MaxMinPicker()
dids1 = mmp.Pick(numpy.array(rmsdmat1),mol1.GetNumConformers(),10)
print [d for d in dids1]

[37, 75, 61, 81, 10, 44, 84, 59, 18, 3]

In [16]:

v.DeleteAll()
for cid in dids1:
    v.ShowMol(mol1, confId=cid, name='conf_'+str(cid), showOnly=False)

Out[17]:

In [18]:

from rdkit.ML.Cluster import Butina
clusters1 = Butina.ClusterData(rmsdmat1, mol1.GetNumConformers(), 1.0, isDistData=True, reordering=True)
print len(clusters1)

In [19]:

plt.xlabel('cluster index')
plt.ylabel('number of members')
bar(range(1,len(clusters1)+1), [len(c) for c in clusters1])
plt.show()

In [20]:

clids1 = []
for c in clusters1:
    if len(c) > 1:
        clids1.append(c[0])
print clids1

[70, 21, 93, 2, 40, 64, 59, 39, 36]

In [21]:

v.DeleteAll()
for cid in clids1:
    v.ShowMol(mol1, confId=cid, name='conf_'+str(cid), showOnly=False)

Out[22]:

In [23]:

tani1 = []
for cid in cids1[1:]:
  tani1.append(AllChem.ShapeTanimotoDist(mol1, mol1, confId1=0, confId2=cid))

In [24]:

plt.xlabel('Shape Tanimoto Distance')
plt.ylabel('count')
hist(tani1)
plt.show()

In [25]:

cids13 = [0]
cids13 += [cid for cid,t in zip(cids1[1:], tani1) if t > 0.65]
print len(cids13)

In [26]:

v.DeleteAll()
for cid in cids13:
    v.ShowMol(mol1, confId=cid, name='conf_'+str(cid), showOnly=False)

Out[27]:

In [28]:

distmat1 = []
for i in range(1, mol1.GetNumConformers()):
    tmp = []
    for j in range(i):
        tmp.append(AllChem.ShapeTanimotoDist(mol1, mol1, confId1=i, confId2=j))
    distmat1.extend(tmp)

In [29]:

mmp = SimDivFilters.MaxMinPicker()
tdids1 = mmp.Pick(numpy.array(distmat1),mol1.GetNumConformers(),10)
print [d for d in tdids1]

[37, 81, 75, 61, 10, 73, 59, 7, 11, 44]

In [30]:

tclusters1 = Butina.ClusterData(distmat1, mol1.GetNumConformers(), 0.3, isDistData=True, reordering=True)
print len(tclusters1)

In [31]:

plt.xlabel('cluster index')
plt.ylabel('number of members')
bar(range(1,len(tclusters1)+1), [len(c) for c in tclusters1])
plt.show()

In [32]:

tclids1 = []
for c in tclusters1:
    if len(c) > 1:
        tclids1.append(c[0])
print tclids1

[30, 99, 93, 15, 87, 40, 3, 57, 74, 20, 98, 80, 38, 8]

In [33]:

v.DeleteAll()
for cid in tclids1:
    v.ShowMol(mol1, confId=cid, name='conf_'+str(cid), showOnly=False)

Out[34]:

In [35]:

from rdkit.Chem.Pharm2D import Gobbi_Pharm2D, Generate
factory = Gobbi_Pharm2D.factory
pharm3D = []
for cid in cids1:
    dm = Chem.Get3DDistanceMatrix(mol1, confId=cid)
    pharm3D.append(Generate.Gen2DFingerprint(mol1, factory, dMat=dm))

In [36]:

from rdkit import DataStructs
# similarity distribution
sims1 = []
p1 = pharm3D[0] # first conformer as reference
for p in pharm3D[1:]:
    sims1.append(DataStructs.TanimotoSimilarity(p1, p))

In [37]:

plt.xlabel('Tanimoto similarity')
plt.ylabel('number of conformers')
hist(sims1)
plt.show()

In [38]:

sids1 = [0]
sids1 += [cid for cid,s in zip(cids1[1:], sims1) if s < 0.4]
print len(sids1)
print sids1

11
[0, 64, 73, 76, 79, 83, 84, 87, 88, 95, 98]

In [39]:

tanimat1 = []
for i in range(1, mol1.GetNumConformers()):
    tanimat1.extend(DataStructs.BulkTanimotoSimilarity(pharm3D[i], pharm3D[:i], returnDistance=True))

In [40]:

tclusters12 = Butina.ClusterData(tanimat1, mol1.GetNumConformers(), 0.3, isDistData=True, reordering=True)
print len(tclusters12)

In [41]:

plt.xlabel('cluster index')
plt.ylabel('number of members')
bar(range(1,len(tclusters12)+1), [len(c) for c in tclusters12])
plt.show()

In [42]:

tclids12 = []
for c in tclusters12:
    if len(c) > 1:
        tclids12.append(c[0])
print tclids12

[13, 90, 51, 87, 82, 98, 76, 56, 99, 77, 61, 11, 10]

In [43]:

v.DeleteAll()
for cid in tclids12:
    v.ShowMol(mol1, confId=cid, name='conf_'+str(cid), showOnly=False)

Out[44]:

In [45]:

smi_pdb = 'C[NH+]1CCN(CCOc2cc3ncnc(Nc4c5c(ccc4Cl)OCO5)c3c(OC3CCOCC3)c2)CC1'
mol2 = Chem.MolFromSmiles(smi_pdb)
mol2

Out[45]:

In [46]:

# PDB 2H8H
mol2 = Chem.MolFromMolFile('lig_2H8H.sdf', removeHs=False)
mol2.RemoveAllConformers()
mol2

Out[46]:

In [48]:

cids2 = rdConformerParser.AddConformersFromAmberTrajectory(mol2, 'ligand.trx')
print len(cids2)

In [49]:

for i in range(1, mol2.GetNumConformers()):
    pyO3A = rdMolAlign.GetO3A(mol2, mol2, prbCid=i, refCid=0)
    pyO3A.Align()

In [72]:

v.DeleteAll()
for cid in cids2:
    v.ShowMol(mol2, confId=cid, name='conf_'+str(cid), showOnly=False)

Out[73]:

In [57]:

rmsd2 = []
for cid in cids2[1:]:
  rmsd2.append(AllChem.GetConformerRMS(mol2, 0, cid, prealigned=True))

In [58]:

plt.xlabel('RMSD')
plt.ylabel('count')
hist(rmsd2)
plt.show()

In [59]:

cids22 = [0]
cids22 += [cid for cid,r in zip(cids2[1:], rmsd2) if r >= 1.0]
print len(cids22)

In [74]:

v.DeleteAll()
for cid in cids22:
    v.ShowMol(mol2, confId=cid, name='conf_'+str(cid), showOnly=False)

Out[75]:

In [62]:

rmsdmat2 = AllChem.GetConformerRMSMatrix(mol2, prealigned=True)

In [63]:

mmp = SimDivFilters.MaxMinPicker()
dids2 = mmp.Pick(numpy.array(rmsdmat2), mol2.GetNumConformers(), 10)
print [d for d in dids2]

[37, 23, 11, 62, 28, 77, 61, 49, 44, 33]

In [76]:

v.DeleteAll()
for cid in dids2:
    v.ShowMol(mol2, confId=cid, name='conf_'+str(cid), showOnly=False)

Out[77]:

In [66]:

clusters2 = Butina.ClusterData(rmsdmat2, mol2.GetNumConformers(), 0.5, isDistData=True, reordering=True)
print len(clusters2)

In [67]:

plt.xlabel('cluster index')
plt.ylabel('number of members')
bar(range(1,len(clusters2)+1), [len(c) for c in clusters2])
plt.show()

In [68]:

clids2 = []
for c in clusters2:
    if len(c) > 1:
        clids2.append(c[0])
print clids2

[37, 47, 14, 83, 80, 18, 69, 45, 84, 59]

In [78]:

v.DeleteAll()
for cid in clids2:
    v.ShowMol(mol2, confId=cid, name='conf_'+str(cid), showOnly=False)

Out[79]:

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