conkit.core.distogram module¶

Distogram container used throughout ConKit

class Distogram(id)[source]¶

Bases: conkit.core.contactmap.ContactMap

A distogram object to store all associated information. This class inherits methods and attributes from ContactMap

Examples

>>> import numpy as np
>>> from conkit.core import Distance
>>> from conkit.core import Distogram
>>> distogram = Distogram("example")
>>> distogram.add(Distance(1, 25, (0.25, 0.45, 0.25, 0.05), ((0, 4), (4, 6), (6, 8), (8, np.inf))))
>>> distogram.add(Distance(7, 19, (0.15, 0.15, 0.60, 0.1), ((0, 4), (4, 6), (6, 8), (8, np.inf))))
>>> print(distogram)
Distogram(id="example" ndistances=2)

id¶

A unique identifier

Type:	str

original_file_format¶

The original file format used to create the Distogram instance

Type:	str

ndistances¶

The number of Distance instances in the Distogram

Type:	int

as_array(seq_len=None, get_weigths=False)[source]¶

Transform the Distogram instance into a :obj:numpy.array instance with shape: (seq_len, seq_len) where each element represents the predicted distance between residues

Parameters:	seq_len (int, optional) – Sequence length. If not provided, it will be pulled from `sequence` [default: None] get_weigths (bool, optional) – If True the resulting array contains the confidence for each predicted distance rather than actual distances [default: False]
Returns:	`numpy.array` instance that represents the distogram. Note: change of residue indexing, now starts in 0
Return type:	`numpy.array`
Raises:	`ValueError` – No seq_len was provided and `sequence` is not defined

as_contactmap(distance_cutoff=8)[source]¶

Create a ContactMap instance with the contacts present in this Distogram instance.

Parameters:	distance_cutoff (int, float) – The distance cutoff used to consider a residue pair within contact of each other
Returns:	A contactmap with the contacts present in this distogram instance.
Return type:	`ContactMap`

static calculate_rmsd(prediction, model, seq_len=None, calculate_wrmsd=False)[source]¶

Calculate the RMSD between two Distogram instances.

Parameters:	prediction (`Distogram`) – A ConKit `Distogram` used as the prediction for the RMSD model (`Distogram`) – A ConKit `Distogram` used as the model to calculate the RMSD seq_len (int, optional) – Sequence length. If not provided, it will be pulled from `sequence` [default: None] calculate_wrmsd (bool) – If set to True wRMSD is calculated using distance confidence scores [default: False]
Returns:	A list of floats with the RMSD values along the sequence
Return type:	list
Raises:	`ValueError` – other is not a `Distogram` instance.

find_residues_within(resnum, distance_cutoff)[source]¶

Find all residues within a given distance of a given residue

Parameters:	resnum (int) – The residue number of the residue of interest distance_cutoff (int, float) – The distance cutoff used to find residues
Returns:	A set with the residue numbers of residues within the given distance
Return type:	set

get_absent_residues(seq_len=None)[source]¶

Get residues not represented by any Distance instance

Parameters:	seq_len (int, optional) – Sequence length. If not provided, it will be pulled from `sequence` [default: None]
Returns:	A list of absent residues
Return type:	list
Raises:	`ValueError` – No seq_len was provided and `sequence` is not defined

get_unique_distances(inplace=False)[source]¶

Filter the Distance instances so that each residue pairs is present only once

Parameters:	inplace (bool, optional) – Replace stored distances with the unique distances in the current instance [default: False]
Returns:	`ContactMap` instance, regardless of inplace
Return type:	`ContactMap`

static merge_arrays(distogram_1, distogram_2)[source]¶

Take two Distogram instances and merge them together into the same numpy.array instance. Each half square in this array will correspond with the predicted distances at each hierarchy

Parameters:	distogram_1 (`Distogram`) – First `Distogram` instance, used to populate top half square of the array distogram_2 (`Distogram`) – Second `Distogram` instance, used to populate lower half square of the array
Returns:	`numpy.array` instance that represents the combined distograms.
Return type:	`numpy.array`
Raises:	`ValueError` – No sequence has been registered for one of the `Distogram` instances `ValueError` – The sequence length associated to the `Distogram` instances is incompatible

ndistances

The number of Distance instances

Returns:	The number of distance pairs in the `Distogram`
Return type:	int

original_file_format: The original file format used to create the Distogram instance

reshape_bins(new_bins)[source]¶

Reshape the predicted distance bins for all Distance instances. This will update distance_scores and distance_bins to fit the new bins.

Parameters:	new_bins (tuple) – A tuple of tuples, where each element corresponds with the upper and lower edges of the intervals for the new distance bins
Raises:	`ValueError` – The new distance bins are not valid