Source code for finches.frontend.calvados_frontend
from finches.frontend.frontend_base import FinchesFrontend
# for model construction
from finches.forcefields.calvados import calvados_model
from finches import epsilon_calculation
# needed so we preserve docstrings after decorator is applied...
from functools import wraps
##
## This is a decorator that checks for RNA in the input sequences and throws an
## exception if it is found. NOTE the @wraps(func) decorator is needed to preserve
## the docstring of the function being decorated.
##
def RNA_check(func):
@wraps(func)
def wrapper(*args, **kwargs):
# args[1] = seq1
# args[2] = seq2
if 'U' in args[1] or 'U' in args[2]:
raise ValueError("CALVADOS2 cannot handle RNA ('U')")
return func(*args, **kwargs)
return wrapper
[docs]
class CALVADOS_frontend(FinchesFrontend):
# ....................................................................................
#
#
def __init__(self, salt=0.150, pH=7.4, temp=288):
# call superclass constructor
super().__init__()
# initialize the CALVADOS forcefield opbject
self.model = calvados_model('CALVADOS2', salt=salt, pH=pH, temp=temp)
# build an interaction matrix constructor object
self.IMC_object = epsilon_calculation.InteractionMatrixConstructor(self.model)
# decorator checks for RNA in CALVADOS input
# ....................................................................................
#
#
[docs]
@RNA_check
def intermolecular_idr_matrix(self,
seq1,
seq2,
window_size=31,
use_cython=True,
use_aliphatic_weighting=True,
use_charge_weighting=True,
disorder_1=True,
disorder_2=True,
null_shuffle=False):
"""
Returns the interaction matrix for the two sequences. Specifically this involves
decomposing the two sequences into window_size fragments and calculating the inter-fragment
epsilon values using a sliding window approach.
Note that we don't pad the sequence here, so the edges of the matrix start
and end at indices that depend on the window size. To avoid confusion, the
function also returns the indices for sequence1 and sequence2.
Parameters
--------------
seq1 : str
Input sequence 1
seq2 : str
Input sequence 2
window_size : int
The window size to use for the interaction matrix calculation.
Default is 31.
use_cython : bool
Whether to use the cython implementation of the interaction matrix calculation.
Default is True.
use_aliphatic_weighting : bool
Whether to use the aliphatic weighting scheme for the interaction matrix
calculation. This weights local aliphatic residues based on the number of
aliphatic residues adjacent to them. Default is True.
use_charge_weighting : bool
Whether to use the charge weighting scheme for the interaction matrix. This
weights local charged residues based on the number of charged residues adjacent
to them. Default is True.
disorder_1 : bool
Whether to generate the disorder profile for sequence 1. Default is True. If False,
a uniform disorder profile is used (all values=1).
disorder_2 : bool
Whether to generate the disorder profile for sequence 2. Default is True. If False,
a uniform disorder profile is used (all values=1).
null_shuffle : bool
Whether to shuffle the sequence before calculating the interaction matrix. Default
is False. If set to a number defines the number of shuffles used for each sequence;
recommended to use 100 shuffles.
Returns
--------------
tuple
A tuple containing the interaction matrix, disorder profile for sequence 1, and disorder
profile for sequence 2.
[0] : This is interaction matrix, and is itself a tuple of 3 elements. The first
is the matrix of sliding epsilon values, and the second and 3rd are the indices that map
sequence position from sequence1 and sequence2 to the matrix
[1] disorder profile for sequence 1. Will be all 1s if disorder_1 is False
[2] disorder profile for sequence 2. Will be all 1s if disorder_2 is False
"""
# call the superclass function
return super().intermolecular_idr_matrix(seq1,
seq2,
window_size=window_size,
use_cython=use_cython,
use_aliphatic_weighting=use_aliphatic_weighting,
use_charge_weighting=use_charge_weighting,
disorder_1=disorder_1,
disorder_2=disorder_2,
null_shuffle=null_shuffle)
# ....................................................................................
#
#
[docs]
@RNA_check
def epsilon(self,
seq1,
seq2,
use_aliphatic_weighting=True,
use_charge_weighting=True):
"""
Returns the epilson value associated with the two sequences. Note that CALVADOS
does not currently support RNA.
Parameters
--------------
seq1 : str
Input sequence 1
seq2 : str
Input sequence 2
use_aliphatic_weighting : bool
Whether to use the aliphatic weighting scheme for the interaction matrix
calculation. This weights local aliphatic residues based on the number of
aliphatic residues adjacent to them. Default is True.
use_charge_weighting : bool
Whether to use the charge weighting scheme for the interaction matrix. This
weights local charged residues based on the number of charged residues adjacent
to them. Default is True.
Returns
--------------
float
The epsilon value for the two sequences.
"""
return self.IMC_object.calculate_epsilon_value(seq1,
seq2,
use_aliphatic_weighting=use_aliphatic_weighting,
use_charge_weighting=use_charge_weighting)
# ....................................................................................
#
#
[docs]
@RNA_check
def interaction_figure(self,
seq1,
seq2,
window_size=31,
use_cython=True,
use_aliphatic_weighting=True,
use_charge_weighting=True,
tic_frequency=100,
seq1_domains=[],
seq2_domains=[],
seq1_lines=[],
seq2_lines=[],
vmin=-7.5,
vmax=7.5,
cmap='PRGn',
fname=None,
zero_folded=True,
no_disorder=False,
null_shuffle=False,
plot_rectangles=None):
"""
Function to generate an interaction matrix figure between two sequences. This does
all the calculation on the backend and formats a figure with parallel disorder tracks
alongside the interaction matrix.
Parameters
--------------
seq1 : str
Input sequence 1
seq2 : str
Input sequence 2
window_size : int
Size of the window to use for the interaction matrix calculation. Note
this must be an odd number and will be converted to an odd number if it
is not. Default is 31.
use_cython : bool
Whether to use the cython implementation of the interaction matrix (always
use this if you can). Default is True.
use_aliphatic_weighting : bool
Whether to use the aliphatic weighting scheme for the interaction matrix
calculation. This weights local aliphatic residues based on the number of
aliphatic residues adjacent to them. Default is True.
use_charge_weighting : bool
Whether to use the charge weighting scheme for the interaction matrix. This
weights local charged residues based on the number of charged residues adjacent
to them. Default is True.
tic_frequency : int
Frequency of the TICs on the plot. Default is 100.
seq1_domains : list
List of tuples/lists containing the start and end positions of domains in
sequence 1. This means these can be easily highlighted in the plot.
seq2_domains : list
List of tuples/lists containing the start and end positions of domains in
sequence 2. This means these can be easily highlighted in the plot.
seq1_lines : list
List of values that will draw lines onto the plot along sequence 1.
seq2_lines : list
List of values that will draw lines onto the plot along sequence 1.
vmin : float
Minimum value for the interaction matrix color scale. Default is -0.75.
vmax : float
Maximum value for the interaction matrix color scale. Default is 0.75.
cmap : str
Colormap to use for the interaction matrix. Default is 'PRGn'.
fname : str
Filename to save the figure to. If None, the figure will be displayed
disorder_1 : bool
Whether to include the disorder profile for sequence 1. Default is True.
disorder_2 : bool
Whether to include the disorder profile for sequence 2. Default is True.
no_disorder : bool
Whether to include the disorder profiles. Default is False. If True, the disorder
profiles will not be included.
null_shuffle : bool
Whether to shuffle the sequence before calculating the interaction matrix. Default
is False. If set to a number defines the number of shuffles used for each sequence;
recommended to use 100 shuffles.
plot_rectangles : list
If a list is provided it should be a list of lists, where each sublist has the
folowing information [seq1_start, seq1_end, seq2_start, seq2_end, color, alpha, kwargs].
Based on this information, rectangles will be drawn on the plot to highlight
specific regions. Default is None.
Returns
--------------
A tuple containing the figure and the axes objects for the main plot, the top
disorder plot, the right disorder plot and the colorbar.
fig : matplotlib.figure.Figure (from plt.figure()
im : matplotlib.image.AxesImage (from plt.imshow())
ax_main : matplotlib.axes.Axes (from plt.subplot2grid()
ax_top : matplotlib.axes.Axes (from plt.subplot2grid()
ax_right : matplotlib.axes.Axes (from plt.subplot2grid()
ax_colorbar : matplotlib.axes.Axes (from plt.subplot2grid()
"""
# call the superclass function
return super().interaction_figure(seq1,
seq2,
window_size=window_size,
use_cython=use_cython,
use_aliphatic_weighting=use_aliphatic_weighting,
use_charge_weighting=use_charge_weighting,
tic_frequency=tic_frequency,
seq1_domains=seq1_domains,
seq2_domains=seq2_domains,
seq1_lines=seq1_lines,
seq2_lines=seq2_lines,
vmin=vmin,
vmax=vmax,
cmap=cmap,
fname=fname,
zero_folded=zero_folded,
disorder_1=True,
disorder_2=True,
no_disorder=no_disorder,
null_shuffle=null_shuffle,
plot_rectangles=plot_rectangles)
# ....................................................................................
#
#
[docs]
def protein_nucleic_vector(seq, fragsize=31, smoothing_window=30, poly_order=3):
"""
Stub function to calculate the protein-nucleic acid interaction vector. CALVADOS
does not currently support RNA.
"""
raise Exception('CALVADOS cannot currently handle RNA')