Python Programming for Biology: Bioinformatics and Beyond

External macromolecular structure modules

We will now look briefly at alternative ways of using macromolecular data in Python. The

first of these is the PDB module of BioPython, which provides a more capable and tested

alternative to our very simple structure data model. The second is the graphical program

PyMol, which can be imported as a Python module so that coordinate data can be rendered

as pretty pictures. (See

http://www.cambridge.org/pythonforbiology

for download links to

BioPython and PyMol.)

Structures in BioPython

The PDB sub-module of BioPython contains functionality to read, write, manipulate and

investigate  macromolecular  structure  data.  In  the  following  example,  after  loading  the

PDB  module,  we  make  a  parser  object  that  can  make  a  PDB.Structure  object  using  the

data from a file. Although this method may seem a little clunky compared to our earlier

examples of a single function that can be used to import the data, having an intermediate

object does allow the programmer to have more line-by-line control of the file reading.

from Bio import PDB

fileName = 'examples/1UST.pdb'

parser = PDB.PDBParser()

struc = parser.get_structure('Name', fileName)

Once  furnished  with  the  main  structure  object,  we  can  then  extract  the  first  set  of

coordinates  (first  conformation)  from  the  structure  and  loop  through  all  of  the  chains,

residues and atoms to get at the coordinates in a convenient manner, noting that the names

of object attributes differ from the previous data model:

---

conformation = struc[0]

for chain in conformation:

for residue in chain:

atomNames = [a.name for a in residue]

print(chain.id, residue.id[1], residue.resname, atomNames)

caAtom = residue['CA']

print(caAtom.name, caAtom.coord, caAtom.bfactor)

Writing  the  data  to  disk  requires  calling  a  function  to  define  a  special  object  (called

writer here) which can be used to save the file:

outFileName = 'test.pdb'

writer = PDB.PDBIO()

writer.set_structure(struc)

writer.save(outFileName)

Referring back to one of the early examples we gave in this chapter, to find the atoms

within a given distance of a specified point, we can write an equivalent function that uses

the BioPython PDB.Structure.Structure object, rather than the simple Structure object we

defined  in

Chapter  8

.  The  mathematics  of  the  function  are  the  same  as  in

findCloseAtoms(), but the collection of coordinates and atom objects is naturally different

because of the different data model. Note that in this example we have been a little more

rigorous  and  check,  using  the  inbuilt  isinstance()  function,  that  the  input  object  is  of  the

required type: PDB.Structure.Structure. Looping through the chains, residues and atoms is

fairly straightforward, although the data model is slightly different. For example, here we

use  a  conformation  (coordinate  model)  number  to  specify  which  coordinate  set  to  use

within  the  structure  object;  our  bespoke  model  is  simpler  and  loads  alternative

conformations as entirely separate objects.

def findCloseAtomsBioPy(structure, xyz, conf=0, limit=5.0):

if not isinstance(structure, PDB.Structure.Structure):

raise Exception('Structure must be Bio.PDB.Structure class')

closeAtoms = []

xyz = array(xyz)

limit2 = limit * limit

coords = []

atoms = []

confModel = structure[conf]

for chain in confModel:

for residue in chain:

for atom in residue:

coords.append(atom.coord)

atoms.append(atom)

deltas = coords - xyz

squares = deltas * deltas

sumSquares = squares.sum(axis=1)

boolArray = sumSquares < limit2

indices = boolArray.nonzero()[0]

---

closeAtoms = [atoms[i] for i in indices]

return closeAtoms