Example: superposition

# Load two HEM-containing proteins from the PDB website
# into EntityHandles. 
prot_one=io.LoadPDB('1mbo', remote=True)
prot_two=io.LoadPDB('1mbn', remote=True)

# Superpose the proteins based on the HEM ligands
mol.alg.Superpose(prot_one.Select('rname=HEM'),
                  prot_two.Select('rname=HEM'))

# Superpose based on HEM-binding residues using the within (<>)
# operator
mol.alg.Superpose(prot_one.Select('rname!=HEM and 3.0 <> [rname=HEM]', 
                                  mol.MATCH_RESIDUES),
                  prot_two.Select('rname!=HEM and 3.0 <> [rname=HEM]', 
                                  mol.MATCH_RESIDUES))





Example: ligand binding-site annotation

# load the two structures
structure_wi_lig = io.LoadPDB('data/structure-with-ligand.pdb')
structure_no_lig = io.LoadPDB('data/structure-without-ligand.pdb')

# set a generic property for all residues of the first structure in
# contact with RVP
for res in structure_wi_lig.Select('4.0 <> [rname=RVP]').residues:
  res.SetBoolProp('close_to_ligand', True)


# get the sequence for both structures
ligand_seq = seq.SequenceFromChain('ligand', structure_wi_lig.chains[0])
no_lig_seq = seq.SequenceFromChain('no lig', structure_no_lig.chains[0])

# align the two sequences using a global alignment algorithm with the 
# BLOSUM62 substitution matrix and default gap extension and opening 
# penalties. Global align returns a list of global alignments, but we
# only use the first one...
aln = seq.alg.GlobalAlign(ligand_seq, no_lig_seq, seq.alg.BLOSUM62)[0]

# print the alignment, to check that the alignment is reasonable
print aln

# the alignment essentially defines a mapping of residues in the first
# and the second structure. We will use GetMatchingBackboneViews to
# obtain two entity views which contain the corresponding residues
aln_wi_lig, aln_no_lig = aln.GetMatchingBackboneViews()

# iterate over the residue pairs and print residue names of residues
# which are part of the "predicted" binding site
print 'predicted binding site'
for lig_res, no_lig_res in zip(aln_wi_lig.residues, aln_no_lig.residues):
  if lig_res.HasProp('close_to_ligand'):
    print no_lig_res




Example: correlating backbone fragments with local electron density

# convert the candidate loop into a density and calculate the real-spatial
  # cross correlation with the actual density. The correlation 
  # coefficient is stored as the generic property  "correl".
  # which could be later used to colour the loops
  
  for index, candidate in enumerate(candidates):
    EntityToDensityRosetta(candidate.CreateFullView(), cmap, 
                           HIGH_RESOLUTION, 5.0, True)
  
    correl=img_alg.RealSpatialCrossCorrelation(dmap, cmap, dmap.GetExtent())
    candidate.SetFloatProp('correl', correl)