def rhapsody(input_obj,
classifier,
aux_classifier=None,
input_type='SAVs',
custom_PDB=None,
force_env=None,
log=True):
"""'input_obj' can be:
* a filename, a list/tuple of strings or a single string, containing SAV
coordinates, with the format "P17516 135 G E" (input_type='SAVs', default)
* a filename of the output from PolyPhen-2, usually named "pph2-full.txt"
(input_type='PP2')
* a string of Uniprot coordinates with unspecified variant, for performing
simulated mutagenesis experiment (input_type='scanning'). Possible formats
are: 'P17516 135' for a single site scanning, and 'P17516' for a complete
sequence scanning.
'custom_PDB' can be a PDBID, a filename or an Atomic instance
"""
assert input_type in ('SAVs', 'scanning', 'PP2')
if log: LOGGER.start('rhapsody-log.txt')
# initialize object that will contain all results and predictions
r = Rhapsody()
# import classifier and feature set from pickle
r.importClassifier(classifier, force_env=force_env)
# import custom PDB structure
if custom_PDB is not None:
r.setCustomPDB(custom_PDB)
# obtain or import PolyPhen-2 results
if input_type == 'SAVs':
# 'input_obj' is a filename, list, tuple or string
# containing SAV coordinates
r.queryPolyPhen2(input_obj)
elif input_type == 'scanning':
# 'input_obj' is a Uniprot accession number identifying a sequence,
# with or without a specified position
r.queryPolyPhen2(input_obj, scanning=True)
elif input_type == 'PP2':
# 'input_obj' is a filename containing PolyPhen-2's output
r.importPolyPhen2output(input_obj)
# compute needed features
r.calcFeatures()
# compute predictions
r.calcPredictions()
if aux_classifier is not None:
# compute additional predictions from a subset of features
try:
r.calcAuxPredictions(aux_classifier, force_env=force_env)
r.printPredictions(format="both",
filename='rhapsody-predictions-full.txt')
except Exception as e:
LOGGER.warn(f'Unable to compute auxiliary predictions: {e}')
# print final predictions
r.printPredictions(filename='rhapsody-predictions.txt')
# save pickle
r.savePickle()
if log: LOGGER.close('rhapsody-log.txt')
return r
def rhapsody(query, query_type='SAVs',
main_classifier=None, aux_classifier=None,
custom_PDB=None, force_env=None,
refresh=False, log=True, **kwargs):
"""Obtain Rhapsody pathogenicity predictions on a list of human missense
variants ([ref]_)
:arg query: Single Amino Acid Variants (SAVs) in Uniprot coordinates
- if *query_type* = ``'SAVs'`` (default), it should be a filename, a
string or a list/tuple of strings, containing Uniprot SAV coordinates,
with the format ``'P17516 135 G E'``. The string could also be just
a single Uniprot sequence identifier (e.g. ``'P17516'``), or the
coordinate of a specific site in a sequence (e.g. ``'P17516 135'``), in
which case all possible 19 amino acid substitutions at the specified
positions will be analyzed.
- if *query_type* = ``'PolyPhen2'``, it should be a filename containing
the output from PolyPhen-2, usually named :file:`pph2-full.txt`
:type query: str, list
:arg query_type: ``'SAVs'`` or ``'PolyPhen2'``
:type query_type: str
:arg main_classifier: main classifier's filename. If **None**, the default
*full* Rhapsody classifier will be used
:type main_classifier: str
:arg aux_classifier: auxiliary classifier's filename. If both
*main_classifier* and *aux_classifier* are **None**, the default
*reduced* Rhapsody classifier will be used
:type aux_classifier: str
:arg custom_PDB: a PDBID, a filename or an :class:`Atomic` to be used
for computing structural and dynamical features, instead of the PDB
structure automatically selected by the program
:type custom_PDB: str, :class:`AtomGroup`
:arg force_env: force a specific environment model for GNM/ANM
calculations, among ``'chain'``, ``'reduced'`` and ``'sliced'``.
If **None** (default), the model of individual dynamical features will
match that found in the classifier's feature set
:type force_env: str
:arg refresh: if **True**, precomputed features and PDB mappings found in
the working directory will be ignored and computed again
:type refresh: str
:arg log: if **True**, log messages will be saved in
:file:`rhapsody-log.txt`
:type log: str
.. [ref] Ponzoni L, Bahar I. Structural dynamics is a determinant of
the functional significance of missense variants. *PNAS* **2018**
115 (16) 4164-4169.
"""
assert query_type in ['SAVs', 'PolyPhen2'], 'Invalid query type.'
if log:
LOGGER.start('rhapsody-log.txt')
# select classifiers
if main_classifier is None:
main_classifier = getDefaultClassifiers()['full']
if aux_classifier is None:
aux_classifier = getDefaultClassifiers()['reduced']
# initialize object that will contain all results and predictions
r = Rhapsody(**kwargs)
# import classifiers and feature set from pickle
r.importClassifiers(main_classifier, aux_classifier, force_env=force_env)
# import custom PDB structure
if custom_PDB is not None:
r.setCustomPDB(custom_PDB)
# obtain or import PolyPhen-2 results
if query_type == 'SAVs':
r.queryPolyPhen2(query)
elif query_type == 'PolyPhen2':
r.importPolyPhen2output(query)
# compute predictions
r.getPredictions(refresh=refresh)
# print predictions to file
r.printPredictions()
if aux_classifier is not None:
# print both 'full' and 'reduced' predictions in a more detailed format
r.printPredictions(
classifier="both", PolyPhen2=False, EVmutation=False,
filename='rhapsody-predictions-full_vs_reduced.txt')
# save pickle
r.savePickle()
if log:
LOGGER.close('rhapsody-log.txt')
return r
