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.. currentmodule:: freesasa
Introduction
============
This package provides Python bindings for the `FreeSASA C Library
<https://github.com/mittinatten/freesasa>`_.
It can be installed using
.. code::
pip install freesasa
Binaries are available for Python 2.7, 3.5, 3.6 and 3.7 for Mac OS X
and Windows, in addition to the source distribution.
Basic calculations
------------------
Using defaults everywhere a simple calculation can be carried out as
follows (assuming the file ``1ubq.pdb`` is available)
.. code:: python
import freesasa
structure = freesasa.Structure("1ubq.pdb")
result = freesasa.calc(structure)
area_classes = freesasa.classifyResults(result, structure)
print "Total : %.2f A2" % result.totalArea()
for key in area_classes:
print key, ": %.2f A2" % area_classes[key]
Which would give the following output
.. code::
Total : 4804.06 A2
Polar : 2504.22 A2
Apolar : 2299.84 A2
The following does a high precision L&R calculation
.. code:: python
result = freesasa.calc(structure,
freesasa.Parameters({'algorithm' : freesasa.LeeRichards,
'n-slices' : 100}))
Using the results from a calculation we can also integrate SASA over a selection of
atoms, using a subset of the Pymol `selection syntax`_:
.. _selection syntax: http://freesasa.github.io/doxygen/Selection.html
.. code:: python
selections = freesasa.selectArea(('alanine, resn ala', 'r1_10, resi 1-10'),
structure, result)
for key in selections:
print key, ": %.2f A2" % selections[key]
which gives the output
.. code::
alanine : 120.08 A2
r1_10 : 634.31 A2
Customizing atom classification
-------------------------------
This uses the NACCESS parameters (the file ``naccess.config`` is
available in the ``share/`` directory of the repository).
.. code:: python
classifier = freesasa.Classifier("naccess.config")
structure = freesasa.Structure("1ubq.pdb", classifier)
result = freesasa.calc(structure)
area_classes = freesasa.classifyResults(result, structure, classifier)
Classification can be customized also by extending the :py:class:`.Classifier`
interface. The code below is an illustration of a classifier that
classes nitrogens separately, and assigns radii based on element only
(and crudely).
.. code:: python
import freesasa
import re
class DerivedClassifier(freesasa.Classifier):
# this must be set explicitly in all derived classifiers
purePython = True
def classify(self, residueName, atomName):
if re.match('\s*N', atomName):
return 'Nitrogen'
return 'Not-nitrogen'
def radius(self, residueName, atomName):
if re.match('\s*N',atomName): # Nitrogen
return 1.6
if re.match('\s*C',atomName): # Carbon
return 1.7
if re.match('\s*O',atomName): # Oxygen
return 1.4
if re.match('\s*S',atomName): # Sulfur
return 1.8
return 0; # everything else (Hydrogen, etc)
classifier = DerivedClassifier()
# use the DerivedClassifier to calculate atom radii
structure = freesasa.Structure("1ubq.pdb", classifier)
result = freesasa.calc(structure)
# use the DerivedClassifier to classify atoms
area_classes = freesasa.classifyResults(result,structure,classifier)
Of course, this example is somewhat contrived, if we only want the
integrated area of nitrogen atoms, the simpler choice would be
.. code:: python
selection = freesasa.selectArea('nitrogen, symbol n', structure, result)
However, extending :py:class:`.Classifier`, as illustrated above, allows
classification to arbitrary complexity and also lets us redefine the
radii used in the calculation.
Bio.PDB
-------
FreeSASA can also calculate the SASA of a ``Bio.PDB`` structure from BioPython
.. code:: python
from Bio.PDB import PDBParser
parser = PDBParser()
structure = parser.get_structure("Ubiquitin", "1ubq.pdb")
result, sasa_classes = freesasa.calcBioPDB(structure)
If one needs more control over the analysis the structure can be
converted to a :py:class:`.Structure` using :py:func:`.structureFromBioPDB()`
and the calculation can be performed the normal way using this
structure.
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