Core entities for hierarchy construction
Contact(res1_seq, res2_seq, raw_score, distance_bound=(0, 8))[source]¶Bases: conkit.core._Entity
A contact pair template to store all associated information
Examples
>>> from conkit.core import Contact
>>> contact = Contact(1, 25, 1.0)
>>> print(contact)
Contact(id="(1, 25)" res1="A" res1_seq=1 res2="A" res2_seq=25 raw_score=1.0)
Attributes
distance_bound |
The lower and upper distance boundary values of a contact pair in Ångstrom [Default: 0-8Å]. |
id |
The ID of the selected entity |
is_match |
A boolean status for the contact |
is_mismatch |
A boolean status for the contact |
is_unknown |
A boolean status for the contact |
lower_bound |
The lower distance boundary value |
raw_score |
The prediction score for the contact pair |
res1 |
The amino acid of residue 1 [default: X] |
res2 |
The amino acid of residue 2 [default: X] |
res1_chain |
The chain for residue 1 |
res2_chain |
The chain for residue 2 |
res1_seq |
The residue sequence number of residue 1 |
res2_seq |
The residue sequence number of residue 2 |
res1_altseq |
The alternative residue sequence number of residue 1 |
res2_altseq |
The alternative residue sequence number of residue 2 |
scalar_score |
The raw_score scaled according to the average raw_score |
status |
An indication of the residue status, i.e true positive, false positive, or unknown |
upper_bound |
The upper distance boundary value |
weight |
A separate internal weight factor for the contact pair |
Methods
add(entity) |
Add a child to the Entity |
copy() |
Create a shallow copy of Entity |
deepcopy() |
Create a deep copy of Entity |
define_match() |
Define a contact as matching contact |
define_mismatch() |
Define a contact as mismatching contact |
define_unknown() |
Define a contact with unknown status |
remove(id) |
Remove a child |
distance_bound¶The lower and upper distance boundary values of a contact pair in Ångstrom [Default: 0-8Å].
is_match¶A boolean status for the contact
is_mismatch¶A boolean status for the contact
is_unknown¶A boolean status for the contact
lower_bound¶The lower distance boundary value
raw_score¶The prediction score for the contact pair
res1¶The amino acid of residue 1 [default: X]
res1_altseq¶The alternative residue sequence number of residue 1
res1_chain¶The chain for residue 1
res1_seq¶The residue sequence number of residue 1
res2¶The amino acid of residue 2 [default: X]
res2_altseq¶The alternative residue sequence number of residue 2
res2_chain¶The chain for residue 2
res2_seq¶The residue sequence number of residue 2
status¶An indication of the residue status, i.e true positive, false positive, or unknown
upper_bound¶The upper distance boundary value
weight¶A separate internal weight factor for the contact pair
ContactFile(id)[source]¶Bases: conkit.core._Entity
A contact file object representing a single prediction file
The contact file class represents a data structure to hold all predictions with a single contact map file. It contains functions to store, manipulate and organise contact maps.
Examples
>>> from conkit.core import ContactMap, ContactFile
>>> contact_file = ContactFile("example")
>>> contact_file.add(ContactMap("foo"))
>>> contact_file.add(ContactMap("bar"))
>>> print(contact_file)
ContactFile(id="example" nseqs=2)
Attributes
author |
The author of the ContactFile |
method |
The ContactFile-specific method |
remark |
The ContactFile-specific remarks |
target |
The target name |
top_map |
The first ContactMap entry in ContactFile |
Methods
add(entity) |
Add a child to the Entity |
copy() |
Create a shallow copy of Entity |
deepcopy() |
Create a deep copy of Entity |
remove(id) |
Remove a child |
sort(kword[, reverse, inplace]) |
Sort the ContactFile |
The author of the ContactFile
method¶The ContactFile-specific method
remark¶The ContactFile-specific remarks
sort(kword, reverse=False, inplace=False)[source]¶Sort the ContactFile
| Parameters: | kword : str
reverse : bool, optional
inplace : bool, optional
|
|---|---|
| Returns: | contact_map :
|
| Raises: | ValueError
|
target¶The target name
top_map¶The first ContactMap entry in ContactFile
| Returns: | top_map :
|
|---|
ContactMap(id)[source]¶Bases: conkit.core._Entity
A contact map object representing a single prediction
The ContactMap class represents a data structure to hold a single
contact map prediction in one place. It contains functions to store,
manipulate and organise Contact instances.
Examples
>>> from conkit.core import Contact, ContactMap
>>> contact_map = ContactMap("example")
>>> contact_map.add(Contact(1, 10, 0.333))
>>> contact_map.add(Contact(5, 30, 0.667))
>>> print(contact_map)
ContactMap(id="example" ncontacts=2)
Attributes
coverage |
The sequence coverage score |
id |
The ID of the selected entity |
ncontacts |
The number of Contact instances in the ContactMap |
precision |
The precision (Positive Predictive Value) score |
repr_sequence |
The representative Sequence associated with the ContactMap |
repr_sequence_altloc |
The representative altloc Sequence associated with the ContactMap |
sequence |
The Sequence associated with the ContactMap |
top_contact |
The first Contact entry in ContactMap |
Methods
add(entity) |
Add a child to the Entity |
assign_sequence_register([altloc]) |
Assign the amino acids from Sequence to all Contact instances |
calculate_jaccard_index(other) |
Calculate the Jaccard index between two ContactMap instances |
calculate_kernel_density([bw_method]) |
Calculate the contact density in the contact map using Gaussian kernels |
calculate_scalar_score() |
Calculate a scaled score for the ContactMap |
copy() |
Create a shallow copy of Entity |
deepcopy() |
Create a deep copy of Entity |
find(indexes[, altloc]) |
Find all contacts associated with index |
match(other[, remove_unmatched, renumber, ...]) |
Modify both hierarchies so residue numbers match one another. |
remove(id) |
Remove a child |
remove_neighbors([min_distance, inplace]) |
Remove contacts between neighboring residues |
rescale([inplace]) |
Rescale the raw scores in ContactMap |
sort(kword[, reverse, inplace]) |
Sort the ContactMap |
assign_sequence_register(altloc=False)[source]¶Assign the amino acids from Sequence to all Contact instances
| Parameters: | altloc : bool
|
|---|
calculate_jaccard_index(other)[source]¶Calculate the Jaccard index between two ContactMap instances
This score analyzes the difference of the predicted contacts from two maps,
where \(x\) and \(y\) are the sets of predicted contacts from two different predictors, \(\left|x \cap y\right|\) is the number of elements in the intersection of \(x\) and \(y\), and the \(\left|x \cup y\right|\) represents the number of elements in the union of \(x\) and \(y\).
The J-score has values in the range of \([0, 1]\), with a value of \(1\) corresponding to identical contact maps and \(0\) to dissimilar ones.
| Parameters: | other :
|
|---|---|
| Returns: | float
|
Warning
The Jaccard distance ranges from \([0, 1]\), where \(1\) means the maps contain identical contacts pairs.
Notes
The Jaccard index is different from the Jaccard distance mentioned in [1]. The Jaccard distance corresponds to \(1-Jaccard_{index}\).
| [1] | Q. Wuyun, W. Zheng, Z. Peng, J. Yang (2016). A large-scale comparative assessment of methods for residue-residue contact prediction. Briefings in Bioinformatics, [doi: 10.1093/bib/bbw106]. |
calculate_kernel_density(bw_method='amise')[source]¶Calculate the contact density in the contact map using Gaussian kernels
Various algorithms can be used to estimate the bandwidth. To calculate the
bandwidth for an 1D data array X with n data points and d dimensions,
the listed algorithms have been implemented. Please note, in rules 2 and 3, the
value of \(\sigma\) is the smaller of the standard deviation of X or
the normalized interquartile range.
Asymptotic Mean Integrated Squared Error (AMISE)
This particular choice of bandwidth recovers all the important features whilst maintaining smoothness. It is a direct implementation of the method used by [2].
Bowman & Azzalini [3] implementation
| [2] | Sadowski, M.I. (2013). Prediction of protein domain boundaries from inverse covariances. |
| [3] | Bowman, A.W. & Azzalini, A. (1997). Applied Smoothing Techniques for Data Analysis. |
| [4] | Scott, D.W. (1992). Multivariate Density Estimation: Theory, Practice, and Visualization. |
| [5] | Silverman, B.W. (1986). Density Estimation for Statistics and Data Analysis. |
| Parameters: | bw_method : str, optional
|
|---|---|
| Returns: | list
|
| Raises: | RuntimeError
ValueError
|
calculate_scalar_score()[source]¶Calculate a scaled score for the ContactMap
This score is a scaled score for all raw scores in a contact map. It is defined by the formula
where \(x\) corresponds to the raw score of each predicted contact and \(\overline{d}\) to the mean of all raw scores.
The score is saved in a separate Contact attribute called
scalar_score
This score is described in more detail in [6].
| [6] | S. Ovchinnikov, L. Kinch, H. Park, Y. Liao, J. Pei, D.E. Kim, H. Kamisetty, N.V. Grishin, D. Baker (2015). Large-scale determination of previously unsolved protein structures using evolutionary information. Elife 4, e09248. |
coverage¶The sequence coverage score
The coverage score is calculated by analysing the number of residues covered by the predicted contact pairs.
The coverage score is calculated by dividing the number of contacts \(x_{cov}\) by the number of residues in the sequence \(L\).
| Returns: | cov : float
|
|---|
See also
find(indexes, altloc=False)[source]¶Find all contacts associated with index
| Parameters: | index : list, tuple
altloc : bool
|
|---|---|
| Returns: |
|
match(other, remove_unmatched=False, renumber=False, inplace=False)[source]¶Modify both hierarchies so residue numbers match one another.
This function is key when plotting contact maps or visualising contact maps in 3-dimensional space. In particular, when residue numbers in the structure do not start at count 0 or when peptide chain breaks are present.
| Parameters: | other :
remove_unmatched : bool, optional
renumber : bool, optional
inplace : bool, optional
|
|---|---|
| Returns: | hierarchy_mod
|
| Raises: | ValueError
|
ncontacts¶The number of Contact instances in the ContactMap
| Returns: | ncontacts : int
|
|---|
precision¶The precision (Positive Predictive Value) score
The precision value is calculated by analysing the true and false postive contacts.
The status of each contact, i.e true or false positive status, can be
determined by running the match() function providing a reference
structure.
| Returns: | ppv : float
|
|---|
See also
remove_neighbors(min_distance=5, inplace=False)[source]¶Remove contacts between neighboring residues
| Parameters: | min_distance : int, optional
inplace : bool, optional
|
|---|---|
| Returns: | contact_map :
|
repr_sequence¶The representative Sequence associated with the ContactMap
The peptide sequence constructed from the available contacts using the normal res_seq positions
| Returns: | sequence : |
|---|---|
| Raises: | TypeError
|
See also
repr_sequence_altloc¶The representative altloc Sequence associated with the ContactMap
The peptide sequence constructed from the available contacts using the altloc res_seq positions
| Returns: | sequence : |
|---|---|
| Raises: | ValueError
|
See also
rescale(inplace=False)[source]¶Rescale the raw scores in ContactMap
Rescaling of the data is done to normalize the raw scores to be in the range [0, 1]. The formula to rescale the data is:
\(x\) is the original value and \(d\) are all values to be rescaled.
| Parameters: | inplace : bool, optional
|
|---|---|
| Returns: | contact_map :
|
sequence¶The Sequence associated with the ContactMap
| Returns: | Sequence |
|---|
See also
sort(kword, reverse=False, inplace=False)[source]¶Sort the ContactMap
| Parameters: | kword : str
reverse : bool, optional
inplace : bool, optional
|
|---|---|
| Returns: | contact_map :
|
| Raises: | ValueError
|
top_contact¶The first Contact entry in ContactMap
| Returns: | top_contact :
|
|---|
Sequence(id, seq)[source]¶Bases: conkit.core._Entity
A sequence template to store all associated information
Examples
>>> from conkit.core import Sequence
>>> sequence_entry = Sequence("example", "ABCDEF")
>>> print(sequence_entry)
Sequence(id="example" seq="ABCDEF" seqlen=6)
Attributes
id |
The ID of the selected entity |
remark |
The Sequence-specific remarks |
seq |
The protein sequence as str |
seq_len |
The protein sequence length |
Methods
add(entity) |
Add a child to the Entity |
align_global(other[, id_chars, nonid_chars, ...]) |
Generate a global alignment between two Sequence instances |
align_local(other[, id_chars, nonid_chars, ...]) |
Generate a local alignment between two Sequence instances |
copy() |
Create a shallow copy of Entity |
deepcopy() |
Create a deep copy of Entity |
remove(id) |
Remove a child |
align_global(other, id_chars=2, nonid_chars=1, gap_open_pen=-0.5, gap_ext_pen=-0.1, inplace=False)[source]¶Generate a global alignment between two Sequence instances
| Parameters: | other : id_chars : int, optional nonid_chars : int, optional gap_open_pen : float, optional gap_ext_pen : float, optional inplace : bool, optional
|
|---|---|
| Returns: |
|
align_local(other, id_chars=2, nonid_chars=1, gap_open_pen=-0.5, gap_ext_pen=-0.1, inplace=False)[source]¶Generate a local alignment between two Sequence instances
| Parameters: | other : id_chars : int, optional nonid_chars : int, optional gap_open_pen : float, optional gap_ext_pen : float, optional inplace : bool, optional
|
|---|---|
| Returns: |
|
seq¶The protein sequence as str
seq_len¶The protein sequence length
SequenceFile(id)[source]¶Bases: conkit.core._Entity
A sequence file object representing a single sequence file
The SequenceFile class represents a data structure to hold
Sequence instances in a single sequence file. It contains
functions to store and analyze sequences.
Examples
>>> from conkit.core import Sequence, SequenceFile
>>> sequence_file = SequenceFile("example")
>>> sequence_file.add(Sequence("foo", "ABCDEF"))
>>> sequence_file.add(Sequence("bar", "ZYXWVU"))
>>> print(sequence_file)
SequenceFile(id="example" nseqs=2)
Attributes
id |
The ID of the selected entity |
is_alignment |
A boolean status for the alignment |
nseqs |
The number of Sequence instances |
remark |
The SequenceFile-specific remarks |
status |
An indication of the residue status, i.e true positive, false positive, or unknown |
top_sequence |
The first Sequence entry in SequenceFile |
Methods
add(entity) |
Add a child to the Entity |
calculate_freq() |
Calculate the gap frequency in each alignment column |
calculate_meff([identity]) |
Calculate the number of effective sequences |
copy() |
Create a shallow copy of Entity |
deepcopy() |
Create a deep copy of Entity |
remove(id) |
Remove a child |
sort(kword[, reverse, inplace]) |
Sort the SequenceFile |
trim(start, end[, inplace]) |
Trim the SequenceFile |
calculate_freq()[source]¶Calculate the gap frequency in each alignment column
This function calculates the frequency of gaps at each position in the Multiple Sequence Alignment.
| Returns: | list
|
|---|---|
| Raises: | MemoryError
RuntimeError
|
calculate_meff(identity=0.7)[source]¶Calculate the number of effective sequences
This function calculates the number of effective sequences (Meff) in the Multiple Sequence Alignment.
The mathematical function used to calculate Meff is
| Parameters: | identity : float, optional
|
|---|---|
| Returns: | int
|
| Raises: | MemoryError
RuntimeError
ValueError
ValueError
|
is_alignment¶A boolean status for the alignment
| Returns: | bool
|
|---|
nseqs¶The number of Sequence instances
in the SequenceFile
| Returns: | int
|
|---|
remark¶The SequenceFile-specific remarks
sort(kword, reverse=False, inplace=False)[source]¶Sort the SequenceFile
| Parameters: | kword : str
reverse : bool, optional
inplace : bool, optional
|
|---|---|
| Returns: |
|
| Raises: | ValueError
|
status¶An indication of the residue status, i.e true positive, false positive, or unknown
top_sequence¶The first Sequence entry in SequenceFile
| Returns: |
|
|---|
trim(start, end, inplace=False)[source]¶Trim the SequenceFile
| Parameters: | start : int
end : int
inplace : bool, optional
|
|---|---|
| Returns: |
|