Struct

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This page describes Bio.Struct, a new module based on [[Bio.PDB]] that adds further structural biology features.
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This module extends Bio.PDB, providing additional features that prove useful to structural biologists using Biopython.
  
== Aims ==
 
  
Some thing here...
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=== Availability ===
 +
 
 +
This module is only available (for now) in [http://github.com/JoaoRodrigues/biopython/tree/GSOC2010 João's GSOC2010 branch in Github].
 +
 
 +
After getting the branch, it can be accessed easily by an import statement. It should suffice to access all of the functions. Importing specific parts of the module is not supposed to be necessary unless likewise specific operations are sought after.
 +
 
 +
<python>from Bio import Struct</python>
 +
 
 +
=== I/O Functions ===
 +
 
 +
Bio.Struct provides a simple I/O interface to read and write structures in PDB format. It has two methods '''read''' and '''write''' that wrap Bio.PDB.PDBParser and Bio.PDB.PDBIO respectively. It does not intend to replace these methods, merely being a simpler way of performing I/O tasks.
 +
 
 +
==== read() ====
 +
 
 +
<python>s = Struct.read('protein_A.pdb')</python>
 +
 
 +
The name of the resulting Structure object is based on the filename.
 +
 
 +
It has an additional ''id'' argument that allows specification of the Structure object name, much like the first argument in ''PDBParser.get_structure()''.
 +
 
 +
==== write() ====
 +
 
 +
<python>Struct.write(s)</python>
 +
 
 +
The name of the resulting output is based on the Structure object id.
 +
 
 +
It has an additional ''name'' argument that allows naming the output file (much like the main argument in ''PDBIO.save()''). If a file already exists by that name, the write() function automatically renames the output adding ''_0'' (or _1, etc).
 +
 
 +
=== Protein Class ===
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 +
We created a Structure-based class named Protein to confer protein specific methods to a given SMCRA object. A method was also added to Bio.PDB.Structure that allows easy interconversion between the two classes: '''as_protein()'''. We will discuss each method in detail below.
 +
 
 +
==== as_protein() ====
 +
 
 +
Converts a Structure object to the Protein class. The conversion also filters all residues and excludes all those that are not aminoacids (HETATMs are also excluded). This filtering can be disabled by setting the optional ''filter_residues'' argument to False.
 +
 
 +
<python>s = Struct.read('protein_A.pdb')
 +
 
 +
dir(s)
 +
# Edited for shortening purposes
 +
['__doc__', ... , 'renumber_residues', 'set_parent', 'xtra']
 +
 
 +
p = s.as_protein()
 +
 
 +
dir(p)
 +
 
 +
['__doc__', ... , 'renumber_residues', 'search_ss_bonds', 'set_parent', 'xtra']</python>
 +
 
 +
==== search_ss_bonds() ====
 +
 
 +
The function returns an iterator with tuples of pairs of Cysteine residues in close enough proximity to be forming a SS bond. The threshold for a S-S contact to be defined as a SS bond is 3.0A, but it can be manually specified through the ''threshold'' argument.
 +
 
 +
<python>for bond in p.search_ss_bonds():
 +
  print bond
 +
 
 +
(<Residue CYS het=  resseq=5 icode= >, <Residue CYS het=  resseq=55 icode= >)
 +
(<Residue CYS het=  resseq=14 icode= >, <Residue CYS het=  resseq=38 icode= >)
 +
(<Residue CYS het=  resseq=30 icode= >, <Residue CYS het=  resseq=51 icode= >)</python>
 +
 
 +
==== coarse_grain() ====
 +
 
 +
Despite coarse-graining being general to all molecules, the current implemented methods concern proteins only. To ease the introduction of new CG-models, a CG_models.py class is present that defines how each residue should be coarse grained. As of now, three models are supported.
 +
 
 +
===== Example Usage: MARTINI =====
 +
 
 +
<python>cg_martini = p.coarse_grain('MARTINI')
 +
 
 +
for residue in cg_martini.get_residues():
 +
  print residue.resname, residue.child_list
 +
 
 +
ARG [<Atom BB>, <Atom S1>, <Atom S2>]
 +
PRO [<Atom BB>, <Atom S1>]
 +
ASP [<Atom BB>, <Atom S1>]
 +
PHE [<Atom BB>, <Atom S1>, <Atom S2>, <Atom S3>]
 +
......
 +
GLY [<Atom BB>]
 +
ALA [<Atom BB>]</python>
 +
 
 +
===== Supported Models =====
 +
 
 +
====== CA Trace ======
 +
 
 +
Protein residues are reduced to their alpha carbon atom. This is the default method.
 +
 
 +
====== ENCAD 3pt Model ======
 +
 
 +
ENCAD is the Energy Calculation and Dynamics program, developed by Michal Levitt since the 80s. Its 3pt coarse grained model reduces each protein residue to 3 points (some exceptions are reduced to 4 points): Ca, O, and a side chain bead centered on a particular pre-defined atom.
 +
 
 +
====== MARTINI Model ======
 +
 
 +
MARTINI is a well known CG model that, as ENCAD, reduces protein residues to 3/4 beads: Ca, O, and a side chain bead in a particular position.
 +
 
 +
==== check_missing_atoms() ====
 +
 
 +
Compares the residues in the Protein object with a pre-defined topology (derived from AMBER) to check for missing atoms. Outputs a dictionary of tuples for each incomplete residue. Automatically ignores Hydrogen atoms (''ha_only'' argument can be set to False to override this) and allows the usage of a particular template through the ''template'' argument (default None). Templates are dictionaries with residue names as keys and lists with atom names as values.
 +
 
 +
<python>p.check_missing_atoms()</python>
 +
 
 +
==== find_seq_homologues() ====
 +
 
 +
Bridges Bio.Blast.NCBIWWW qblast() function. It allows a direct sequence homology search through that function using the Protein object's sequence. Auto-adjusts parameters for short sequences. For more complex homology searches, use the Bio.Blast.NCBIWWW module directly as this is supposed to be just a convenience function.
 +
 
 +
It returns a list ranked by Expectation Value with some informational values (e-value, identities, positives, gaps), the PDB code of the match, and the alignment.
 +
 
 +
Allows an argument, ''raw_output'', that replaces the default parsed results with raw XML output from the BLAST search.
 +
 
 +
<python>seq_homologues = p.find_seq_homologues()
 +
 
 +
for homologues in seq_homologues:
 +
    print homologues[0], homologues[1]
 +
    print homologues[-1]
 +
    print
 +
 
 +
2BUO 1.82482e-31
 +
DIRQGPKEPFRDYVDRFYKTLRAEQASQEVKNW-TETLLVQNANPDCKTILKALGPGATLEE--TACQG
 +
DIRQGPKEPFRDYVDRFYKTLRAEQASQEVKNW TETLLVQNANPDCKTILKALGPGATLEE  TACQG
 +
DIRQGPKEPFRDYVDRFYKTLRAEQASQEVKNWMTETLLVQNANPDCKTILKALGPGATLEEMMTACQG
 +
 
 +
.....</python>

Latest revision as of 23:41, 13 August 2010

This module extends Bio.PDB, providing additional features that prove useful to structural biologists using Biopython.


Contents

Availability

This module is only available (for now) in João's GSOC2010 branch in Github.

After getting the branch, it can be accessed easily by an import statement. It should suffice to access all of the functions. Importing specific parts of the module is not supposed to be necessary unless likewise specific operations are sought after.

from Bio import Struct

I/O Functions

Bio.Struct provides a simple I/O interface to read and write structures in PDB format. It has two methods read and write that wrap Bio.PDB.PDBParser and Bio.PDB.PDBIO respectively. It does not intend to replace these methods, merely being a simpler way of performing I/O tasks.

read()

s = Struct.read('protein_A.pdb')

The name of the resulting Structure object is based on the filename.

It has an additional id argument that allows specification of the Structure object name, much like the first argument in PDBParser.get_structure().

write()

Struct.write(s)

The name of the resulting output is based on the Structure object id.

It has an additional name argument that allows naming the output file (much like the main argument in PDBIO.save()). If a file already exists by that name, the write() function automatically renames the output adding _0 (or _1, etc).

Protein Class

We created a Structure-based class named Protein to confer protein specific methods to a given SMCRA object. A method was also added to Bio.PDB.Structure that allows easy interconversion between the two classes: as_protein(). We will discuss each method in detail below.

as_protein()

Converts a Structure object to the Protein class. The conversion also filters all residues and excludes all those that are not aminoacids (HETATMs are also excluded). This filtering can be disabled by setting the optional filter_residues argument to False.

s = Struct.read('protein_A.pdb')
 
dir(s)
# Edited for shortening purposes
['__doc__', ... , 'renumber_residues', 'set_parent', 'xtra']
 
p = s.as_protein()
 
dir(p)
 
['__doc__', ... , 'renumber_residues', 'search_ss_bonds', 'set_parent', 'xtra']

search_ss_bonds()

The function returns an iterator with tuples of pairs of Cysteine residues in close enough proximity to be forming a SS bond. The threshold for a S-S contact to be defined as a SS bond is 3.0A, but it can be manually specified through the threshold argument.

for bond in p.search_ss_bonds():
  print bond
 
(<Residue CYS het=  resseq=5 icode= >, <Residue CYS het=  resseq=55 icode= >)
(<Residue CYS het=  resseq=14 icode= >, <Residue CYS het=  resseq=38 icode= >)
(<Residue CYS het=  resseq=30 icode= >, <Residue CYS het=  resseq=51 icode= >)

coarse_grain()

Despite coarse-graining being general to all molecules, the current implemented methods concern proteins only. To ease the introduction of new CG-models, a CG_models.py class is present that defines how each residue should be coarse grained. As of now, three models are supported.

Example Usage: MARTINI
cg_martini = p.coarse_grain('MARTINI')
 
for residue in cg_martini.get_residues():
  print residue.resname, residue.child_list
 
ARG [<Atom BB>, <Atom S1>, <Atom S2>]
PRO [<Atom BB>, <Atom S1>]
ASP [<Atom BB>, <Atom S1>]
PHE [<Atom BB>, <Atom S1>, <Atom S2>, <Atom S3>]
......
GLY [<Atom BB>]
ALA [<Atom BB>]
Supported Models
CA Trace

Protein residues are reduced to their alpha carbon atom. This is the default method.

ENCAD 3pt Model

ENCAD is the Energy Calculation and Dynamics program, developed by Michal Levitt since the 80s. Its 3pt coarse grained model reduces each protein residue to 3 points (some exceptions are reduced to 4 points): Ca, O, and a side chain bead centered on a particular pre-defined atom.

MARTINI Model

MARTINI is a well known CG model that, as ENCAD, reduces protein residues to 3/4 beads: Ca, O, and a side chain bead in a particular position.

check_missing_atoms()

Compares the residues in the Protein object with a pre-defined topology (derived from AMBER) to check for missing atoms. Outputs a dictionary of tuples for each incomplete residue. Automatically ignores Hydrogen atoms (ha_only argument can be set to False to override this) and allows the usage of a particular template through the template argument (default None). Templates are dictionaries with residue names as keys and lists with atom names as values.

p.check_missing_atoms()

find_seq_homologues()

Bridges Bio.Blast.NCBIWWW qblast() function. It allows a direct sequence homology search through that function using the Protein object's sequence. Auto-adjusts parameters for short sequences. For more complex homology searches, use the Bio.Blast.NCBIWWW module directly as this is supposed to be just a convenience function.

It returns a list ranked by Expectation Value with some informational values (e-value, identities, positives, gaps), the PDB code of the match, and the alignment.

Allows an argument, raw_output, that replaces the default parsed results with raw XML output from the BLAST search.

seq_homologues = p.find_seq_homologues()
 
for homologues in seq_homologues:
    print homologues[0], homologues[1]
    print homologues[-1]
    print
 
2BUO 1.82482e-31
DIRQGPKEPFRDYVDRFYKTLRAEQASQEVKNW-TETLLVQNANPDCKTILKALGPGATLEE--TACQG
DIRQGPKEPFRDYVDRFYKTLRAEQASQEVKNW TETLLVQNANPDCKTILKALGPGATLEE  TACQG
DIRQGPKEPFRDYVDRFYKTLRAEQASQEVKNWMTETLLVQNANPDCKTILKALGPGATLEEMMTACQG
 
.....
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