##
## meta.py
##
## meta.py contains all the user-facing function associated with metapredict. If a new function is added it should be included
## here and added to the __all__ list
##
##Handles the primary functions
# NOTE - any new functions must be added to this list!
__all__ = ['predict_disorder', 'predict_disorder_domains', 'graph_disorder', 'predict_all', 'percent_disorder', 'predict_disorder_fasta', 'graph_disorder_fasta', 'predict_disorder_uniprot', 'graph_disorder_uniprot', 'predict_disorder_domains_uniprot', 'predict_disorder_domains_from_external_scores', 'graph_pLDDT_uniprot', 'predict_pLDDT_uniprot', 'graph_pLDDT_fasta', 'predict_pLDDT_fasta', 'graph_pLDDT', 'predict_pLDDT', 'predict_disorder_caid', 'predict_disorder_batch']
# import packages
import os
import sys
import numpy as np
from tqdm import tqdm
# note - we import packages below with a leading _ which means they are ignored in the import
#import protfasta to read .fasta files
import protfasta as _protfasta
# import getseq to get sequences from uniprot
from getSequence import getseq as _getseq
# import stuff for confidence score predictions
from metapredict.backend.network_parameters import metapredict_networks
from metapredict.parameters import DEFAULT_NETWORK, DEFAULT_NETWORK_PLDDT
# import stuff for IDR predictor from backend. Note the 'as _*' hides the imported
# module from the user
from metapredict.backend.predictor import predict as _predict
from metapredict.backend.predictor import predict_pLDDT as _predict_pLDDT
from metapredict.backend import meta_tools as _meta_tools
#import stuff for graphing from backend
from metapredict.backend.meta_graph import graph as _graph
from metapredict.backend import domain_definition as _domain_definition
# import stuff for exceptions
from metapredict.metapredict_exceptions import MetapredictError
# stuff for data structures
from metapredict.backend.data_structures import DisorderObject as _DisorderObject
# ..........................................................................................
#
[docs]
def predict_disorder(inputs, version=DEFAULT_NETWORK, device=None,
normalized=True, round_values=True, return_numpy=True, return_domains=False,
disorder_threshold=None, minimum_IDR_size=12, minimum_folded_domain=50,
gap_closure=10, override_folded_domain_minsize=False, print_performance=False,
show_progress_bar=False, force_disable_batch=False,
disable_pack_n_pad=False, silence_warnings=False,
legacy=False):
"""
The main function in metapredict. Updated to handle much more advanced
functionality while maintaining backwards compatibility with previous
versions of metapredict.
With V3, you can now input a single sequence, a list of sequences, or
a dictionary where key:value pairings are a name for a sequence
and the values are the actual sequence into this single function
into this single function.
We are keeping the 'predict_disorder_batch' function available
to avoid breaking peoples' code, but that function isn't strictly
necessary anymore because you can do batch predictions here.
You can also choose the specific device to carry out predictions
(a specific CUDA enabled GPU, MPS on Mac GPUs, or CPU).
We also made it easy for users to print performance of the predictor
on their own device to see how fast metapredict is!
An important new thing is that we now have 3 versions of metapredict.
Although setting 'legacy=True' will still work to maintain backwards
compatibilty, the recommended thing to do is set 'version' equal
to either 'v1', 'v2', or 'v3'.
Finally, this also adds the ability to return DisorderObjects if
return_domains is set to True. The DisorderObject has the following variables:
DisorderObject
.sequence : str
Amino acid sequence
.disorder : list or np.ndaarray
Hybrid disorder score
.disordered_domain_boundaries : list
List of domain boundaries for IDRs using Python indexing
.folded_domain_boundaries : list
List of domain boundaries for folded domains using Python indexing
.disordered_domains : list
List of the actual sequences for IDRs
.folded_domains : list
List of the actual sequences for folded domains
Parameters
------------
inputs : str, list, or dict
Input amino acid sequence(s) as a string, list, or dictionary.
If a list, needs to be just a list of amino acid sequences
If a dictionary, each key needs to be a sequence name and
each value needs to be the corresponding sequence.
version : str
Which version of metapredict to use. Default is DEFAULT_NETWORK which
is the latest version as defined in parameters. Alternatively, 'V1', 'V2',
or 'V3' can be specified to access a specific version of metapredict
device : int or str
Identifier for the device to be used for predictions.
Possible inputs: 'cpu', 'mps', 'cuda', or an int that corresponds to
the index of a specific cuda-enabled GPU. If 'cuda' is specified and
cuda.is_available() returns False, instead of falling back to CPU,
metapredict will raise an Exception so you know that you are not
using CUDA as you were expecting.
Default: None
When set to None, we will check if there is a cuda-enabled
GPU. If there is, we will try to use that GPU.
If you set the value to be an int, we will use cuda:int as the device
where int is the int you specify. The GPU numbering is 0 indexed, so 0
corresponds to the first GPU and so on. Only specify this if you
know which GPU you want to use.
* Note: MPS is only supported in Pytorch 2.1 or later. If I remember
right it might have been beta supported in 2.0 *. MPS is still fairly
new, so use at your own risk.
normalized : bool
Whether or not to normalize disorder values to between 0 and 1.
Default : True
round_values : bool
Whether to round the values to 4 decimal places.
Default : True
return_numpy : bool
Whether to return a numpy array or a list for single predictions.
Default : True
return_domains : bool
Flag which, if set to true, means we return DisorderDomain
objects instead of simply the disorder scores. These
domain objects include the boundaries between IDRs and
folded domains, the disorder scores, and the individual
sequences for IDRs and folded domains. This adds a small
amount of overhead to the prediction, but typically only
increase prediction time by 10-15%.
disorder_threshold : float
Used only if return_domains = True.
Default is set to None because there are different threshold
values depending on the network (V1 = 0.42, V2=0.5, V3=0.5). You can
override this value.
minimum_IDR_size : int
Used only if return_domains = True.
Defines the smallest possible IDR. This is a hard limit -
i.e. we CANNOT get IDRs smaller than this. Default = 12.
minimum_folded_domain : int
Used only if return_domains = True.
Defines where we expect the limit of small folded domains
to be. This is NOT a hard limit and functions to modulate
the removal of large gaps (i.e. gaps less than this size
are treated less strictly). Note that, in addition,
gaps < 35 are evaluated with a threshold of
0.35*disorder_threshold and gaps < 20 are evaluated with
a threshold of 0.25*disorder_threshold. These two
lengthscales were decided based on the fact that
coiled-coiled regions (which are IDRs in isolation)
often show up with reduced apparent disorder within IDRs,
and but can be as short as 20-30 residues.
The folded_domain_threshold is used based on the
idea that it allows a 'shortest reasonable' folded domain
to be identified. Default=50.
gap_closure : int
Used only if return_domains = True.
Defines the largest gap that would be 'closed'. Gaps here
refer to a scenario in which you have two groups of
disordered residues seprated by a 'gap' of un-disordered
residues. In general large gap sizes will favour larger
contigous IDRs. It's worth noting that gap_closure becomes
relevant only when minimum_region_size becomes very small
(i.e. < 5) because really gaps emerge when the smoothed
disorder fit is "noisy", but when smoothed gaps
are increasingly rare. Default=10.
override_folded_domain_minsize : bool
By default this function includes a fail-safe check that
assumes folded domains really shouldn't be less than
35 or 20 residues. However, for some approaches we may
wish to over-ride these thresholds to match the passed
minimum_folded_domain value. If this flag is set to
True this override occurs. This is generally not
recommended unless you expect there to be well-defined
sharp boundaries which could define small (20-30)
residue folded domains. This is not provided as an option
in the normal predict_disorder_domains for metapredict.
Default = False.
print_performance : bool
Flag which means the function prints the time taken
for the two stages in the prediction algorithm. Again
useful for profiling and debugging. Default = False
show_progress_bar : bool
Flag which, if set to True, means a progress bar is printed as
predictions are made, while if False no progress bar is printed.
Default = True
force_disable_batch : bool
Whether to override any use of batch predictions and predict
sequences individually.
Default = False
disable_pack_n_pad : bool
Whether to disable the use of pack_n_pad in the prediction
algorithm. This is useful for debugging and profiling. Also gives
us a way for people to use older versions of torch.
Default = False
silence_warnings : bool
whether to silence warnings such as the one about compatibility
to use pack-n-pad due to torch version restrictions.
legacy : bool
whether to use the legacy version of metapredict. This is
maintained for backwards compatibility, but the recommended
approach is to use the version parameter to specify which
version of metapredict you want to use. If you set this to
True, it will override any version parameter you set.
Default: False
Returns
--------
DisorderObject or list or np.ndarray
Depending on your input and specified desired output, can return a
np.array, list, or DisorderObject. If you input a string, you will
get back a single np.array, list, or DisorderObject. If you input a list,
you will get a list of your specified return value. If you input a dict,
if you do not set return_domains=True, you will get back a dictionary
where the key is the same as the key for your input dict and the corresponding
value is a nested list where the first element in that list is the sequence for
that key and the value is the disorder scores. If you set return_domains=True,
you will get back a dictionary where the key is the same as the key for your input
dict and the value is a DisorderObject for that sequence.
"""
# sanity check
_meta_tools.raise_exception_on_zero_length(inputs)
# check version and make sure it is an uppercase string
version = _meta_tools.valid_version(version, 'disorder')
# if legacy, make sure version is v1.
if legacy==True:
version='V1'
# get disorder
return _predict(inputs, version=version, use_device=device,
normalized=normalized, round_values=round_values,
return_numpy=return_numpy, return_domains=return_domains,
disorder_threshold=disorder_threshold, minimum_IDR_size=minimum_IDR_size,
minimum_folded_domain=minimum_folded_domain, gap_closure=gap_closure,
override_folded_domain_minsize=override_folded_domain_minsize,
print_performance=print_performance, show_progress_bar=show_progress_bar,
force_disable_batch=force_disable_batch, disable_pack_n_pad=disable_pack_n_pad,
silence_warnings=silence_warnings)
# ..........................................................................................
#
[docs]
def predict_disorder_domains_from_external_scores(disorder,
sequence=None,
disorder_threshold=0.5,
minimum_IDR_size=12,
minimum_folded_domain=50,
gap_closure=10,
override_folded_domain_minsize=False,
return_numpy=True):
"""
This function takes in disorder scores generated from another predictor
and applies the same domain-decomposition algorithm as
predict_disorder_domains() does to extract out congigous IDRs. For example,
if one were to predict disorder using the (excellent) ODiNPred, download the
resulting scores, and read the scores into a list, that list could be passed
as the $disorder argument to this function.
Note that the settings used here may be inapplicable to another disorder
predictor, so you may need to play around with the parameters including
disorder_threshold, minimum_IDR_size, minimum_folded_domain and
gap_closure.
Parameters
-------------
disorder : list
A list of per-residue disorder scores.
sequence : str
The protein sequence as a string. If no sequence is passed,
calling DisorderObject.sequence will return an fake sequence.
disorder_threshold : float
Value that defines what 'disordered' is based on the input predictor
score. The higher the value the more stringent the cutoff.
Default = 0.5.
minimum_IDR_size : int
Defines the smallest possible IDR. This is a hard limit - i.e. we
CANNOT get IDRs smaller than this.
Default = 12.
minimum_folded_domain : int
Defines where we expect the limit of small folded domains to be. This
is NOT a hard limit and functions to modulate the removal of large gaps
(i.e. gaps less than this size are treated less strictly). Note that, in
addition, gaps < 35 are evaluated with a threshold of
0.35*disorder_threshold and gaps < 20 are evaluated with a threshold
of 0.25*disorder_threshold. These two lengthscales were decided based on
the fact that coiled-coiled regions (which are IDRs in isolation) often
show up with reduced apparent disorder within IDRs, and but can be as
short as 20-30 residues. The folded_domain_threshold is used based on
the idea that it allows a 'shortest reasonable' folded domain to be
identified.
Default = 50.
gap_closure : int
Defines the largest gap that would be 'closed'. Gaps here refer to a
scenario in which you have two groups of disordered residues seprated
by a 'gap' of un-disordered residues. In general large gap sizes will
favour larger contigous IDRs. It's worth noting that gap_closure
becomes relevant only when minimum_region_size becomes very small
(i.e. < 5) because really gaps emerge when the smoothed disorder
fit is "noisy", but when smoothed gaps are increasingly rare.
Default = 10.
override_folded_domain_minsize : bool
By default this function includes a fail-safe check that assumes
folded domains really shouldn't be less than 35 or 20 residues.
However, for some approaches we may wish to over-ride these thresholds
to match the passed minimum_folded_domain value. If this flag is set to
True this override occurs. This is generally not recommended unless you
expect there to be well-defined sharp boundaries which could define
small (20-30) residue folded domains. This is not provided as an option
in the normal predict_disorder_domains for metapredict. Default = False.
return_numpy : bool
Flag which if set to true means all numerical types are returned
as numpy.ndlist. Default is True
Returns
---------
DisorderObject
Returns a DisorderObject. DisorderObject has 7 dot variables:
.sequence : str
Amino acid sequence
.disorder : list or np.ndaarray
Hybrid disorder score
.disordered_domain_boundaries : list
List of domain boundaries for IDRs using Python indexing
.folded_domain_boundaries : list
List of domain boundaries for folded domains using Python indexing
.disordered_domains : list
List of the actual sequences for IDRs
.folded_domains : list
List of the actual sequences for folded domains
"""
# sanity check
_meta_tools.raise_exception_on_zero_length(disorder)
# if a sequence was provided check it makes sense in terms of type and length...
if sequence is not None:
try:
if len(sequence) != len(disorder):
raise MetapredictError(f'Disorder and sequence info are not length matched [disorder length = {len(disorder)}, sequence length = {len(sequence)}')
except Exception:
raise MetapredictError(f'Could not compare length of disorder and sequence parameters. Make sure sequence is a str and disorder a list')
return_sequence = True
# if sequence is None create a fake sequence and set return_sequence to False
else:
sequence = 'A'*len(disorder)
return_sequence = False
# run the get_domains function, passing in parameters
return_tuple = _domain_definition.get_domains(sequence,
disorder,
disorder_threshold=disorder_threshold,
minimum_IDR_size=minimum_IDR_size,
minimum_folded_domain=minimum_folded_domain,
gap_closure=gap_closure,
override_folded_domain_minsize=override_folded_domain_minsize)
# extract out the IDR and FD boundaires, discarding the sequence info which is irrelevant
IDRs = []
for local_idr in return_tuple[1]:
IDRs.append([local_idr[0], local_idr[1]])
FDs = []
for local_fd in return_tuple[2]:
FDs.append([local_fd[0], local_fd[1]])
# return DisorderObject
return _DisorderObject(sequence, disorder, IDRs, FDs, return_numpy=return_numpy)
# ..........................................................................................
#
[docs]
def predict_disorder_domains(sequence,
disorder_threshold=None,
minimum_IDR_size=12,
minimum_folded_domain=50,
gap_closure=10,
normalized=True,
return_numpy=True,
version=DEFAULT_NETWORK,
return_list=False):
"""
This function takes an amino acid sequence and one or more
variable options and returns a data structure called a
`DisorderObject`. The object parameters associated with this
object are defined below.
The previous version of metapredict returned a list of values,
which can be obtained instead of the DisorderedObject if
return_list is set to True.
Parameters
-------------
sequence : str
Amino acid sequence
disorder_threshold : float
Set to None such that it will change to 0.42 for legacy
and 0.5 for metapredict. Can still manually set value.
minimum_IDR_size : int
Defines the smallest possible IDR. This is a hard limit -
i.e. we CANNOT get IDRs smaller than this. Default = 12.
minimum_folded_domain : int
Defines where we expect the limit of small folded domains
to be. This is NOT a hard limit and functions to modulate
the removal of large gaps (i.e. gaps less than this size
are treated less strictly). Note that, in addition,
gaps < 35 are evaluated with a threshold of
0.35*disorder_threshold and gaps < 20 are evaluated with
a threshold of 0.25*disorder_threshold. These two
lengthscales were decided based on the fact that
coiled-coiled regions (which are IDRs in isolation)
often show up with reduced apparent disorder within IDRs,
and but can be as short as 20-30 residues.
The folded_domain_threshold is used based on the
idea that it allows a 'shortest reasonable' folded domain
to be identified. Default=50.
gap_closure : int
Defines the largest gap that would be 'closed'. Gaps here
refer to a scenario in which you have two groups of
disordered residues seprated by a 'gap' of un-disordered
residues. In general large gap sizes will favour larger
contigous IDRs. It's worth noting that gap_closure becomes
relevant only when minimum_region_size becomes very small
(i.e. < 5) because really gaps emerge when the smoothed
disorder fit is "noisy", but when smoothed gaps
are increasingly rare. Default=10.
normalized : bool
whether the disorder scores are normalized between zero and
one, default is true
return_numpy : bool
Flag which if set to true means all numerical types are returned
as numpy.ndlist. Default is True
version : str
Which version of metapredict to use. Default is DEFAULT_NETWORK which
is the latest version as defined in parameters. Alternatively, 'V1', 'V2',
or 'V3' can be specified to access a specific version of metapredict
return_list : bool
Flag that determines i to return the old format where a
tuple is returned. This is retained for backwards compatibility
Returns
---------
DisorderObject
By default, the function returns a DisorderObject. A DisorderObject has 7 dot variables:
.sequence : str
Amino acid sequence
.disorder : list or np.ndaarray
disorder scores
.disordered_domain_boundaries : list
List of domain boundaries for IDRs using Python indexing
.folded_domain_boundaries : list
List of domain boundaries for folded domains using Python indexing
.disordered_domains : list
List of the actual sequences for IDRs
.folded_domains : list
List of the actual sequences for folded domains
Returns
---------
list
However, if ``return_list`` == True. Then, the function returns a
list with three elements, as outlined below.
* [0] - Smoothed disorder score used to aid in domain boundary identification. This can be useful for understanding how IDRs/folded domains were identified, and will vary depending on the settings provided
* [1] - a list of elements, where each element defines the start and end position of each IDR. If a sequence was provided the third element in each sub-element is the IDR sequence. If no sequence was provided, then each sub-element is simply len=2.
* [2] - a list of elements, where each element defines the start and end position of each folded region. If a sequence was provided the third element in each sub-element is the folded domain sequence. If no sequence was provided, then each sub-element is simply len=2.
"""
# sanity check
_meta_tools.raise_exception_on_zero_length(sequence)
# check version and make sure it is an uppercase string
version = _meta_tools.valid_version(version, 'disorder')
# get disorder threshold
if disorder_threshold == None:
disorder_threshold = metapredict_networks[version]['parameters']['disorder_threshold']
# check that a valid range was passed for disorder_threshold
_meta_tools.valid_range(disorder_threshold, 0.0, 1.0)
# if not returning list, we can do everything using the predict function
if return_list==False:
# get the disorder obj and return it.
DomObj = _predict(sequence, version=version, normalized=normalized,
return_numpy=return_numpy, return_domains=True,
disorder_threshold=disorder_threshold, minimum_IDR_size=minimum_IDR_size,
minimum_folded_domain=minimum_folded_domain, gap_closure=gap_closure)
return DomObj
else:
# get disorder
disorder = _predict(sequence, version=version, normalized=normalized,
return_numpy=return_numpy)
# extract out disordered
return_tuple = _domain_definition.get_domains(sequence,
disorder,
disorder_threshold=disorder_threshold,
minimum_IDR_size=minimum_IDR_size,
minimum_folded_domain=minimum_folded_domain,
gap_closure=gap_closure)
# return the list
return [disorder, return_tuple[0], return_tuple[1], return_tuple[2]]
# ..........................................................................................
#
[docs]
def predict_all(sequence):
"""
Function to return all three disorder predictions (V1, V2, V3 for
(metapredict), and predicted pLDDT (V1, V2). Returns as a tuple of numpy
arrays, with ppLDDT returned as normalized between 0 and 1.
Parameters
------------
sequence : str
Input amino acid sequence (as string) to be predicted.
Returns
--------
tuple with five np.ndarrays:
[0] - normalized ppLDDT scores using V1 pLDDT predictor (previously from AlphaPredict)
[1] - normalized ppLDDT scores using V2 pLDDT predictor
[2] - legacy metapredict disorder (original metapredict disorder)
[3] - meta disorder (updated metapredict disorder) - V2
[4] - meta disorder (updated metapredict disorder) - V3
"""
# sanity check
_meta_tools.raise_exception_on_zero_length(sequence)
# compute pLDDT and metapredict disorder
v1 = _predict(sequence, version='V1')
v2 = _predict(sequence, version='V2')
v3 = _predict(sequence, version='V3')
ppLDDT_v1 = _predict_pLDDT(sequence, return_numpy=True, normalized=True, return_decimals=True, version='v1')
ppLDDT_v2 = _predict_pLDDT(sequence, return_numpy=True, normalized=True, return_decimals=True, version='v2')
return (ppLDDT_v1, ppLDDT_v2, v1, v2, v3)
# ..........................................................................................
#
[docs]
def predict_disorder_batch(input_sequences,
version = DEFAULT_NETWORK,
device=None,
normalized=True,
round_values=True,
return_numpy=True,
return_domains=False,
disorder_threshold=None,
minimum_IDR_size=12,
minimum_folded_domain=50,
gap_closure=10,
override_folded_domain_minsize=False,
show_progress_bar = True,
disable_batch = False):
"""
Batch mode predictor which takes advantage of PyTorch
parallelization such that whether it's on a GPU or a
CPU, predictions for a set of sequences are performed
rapidly.
This now works with all versions of metapredict.
Note also that batch mode uses 32-bit float vectors
whereas non-batch uses 64-bit float vectors, so the
precise values in batch vs. non-batch may differ
slighly, however this is a numerical precision difference,
such that values by both methods are always within
1e-3 of one another.
Parameters
--------------
input_sequences : list or dictionary
A collection of sequences that are presented either
as a list of sequences or a dictionary of key-value
pairs where values are sequences.
version : string
The network to use for prediction. Default is DEFAULT_NETWORK,
which is defined at the top of /parameters.
Options currently include V1, V2, or V3.
device : int or str
Identifier for the device to be used for predictions.
Possible inputs: 'cpu', 'mps', 'cuda', or an int that corresponds to
the index of a specific cuda-enabled GPU. If 'cuda' is specified and
cuda.is_available() returns False, instead of falling back to CPU,
metapredict will raise an Exception so you know that you are not
using CUDA as you were expecting.
Default: None
When set to None, we will check if there is a cuda-enabled
GPU. If there is, we will try to use that GPU.
If you set the value to be an int, we will use cuda:int as the device
where int is the int you specify. The GPU numbering is 0 indexed, so 0
corresponds to the first GPU and so on. Only specify this if you
know which GPU you want to use.
* Note: MPS is only supported in Pytorch 2.1 or later. If I remember
right it might have been beta supported in 2.0 *.
normalized : bool
Whether or not to normalize disorder values to between 0 and 1.
Default : True
round_values : bool
Whether to round the values to 4 decimal places.
Default : True
return_numpy : bool
Whether to return a numpy array or a list for single predictions.
Default : True
return_domains : bool
Flag which, if set to true, means we return DisorderDomain
objects instead of simply the disorder scores. These
domain objects include the boundaries between IDRs and
folded domains, the disorder scores, and the individual
sequences for IDRs and folded domains. This adds a small
amount of overhead to the prediction, but typically only
increase prediction time by 10-15%.
disorder_threshold : float
Used only if return_domains = True.
Threshold used to deliniate between folded and disordered
regions. We use a value of 0.5 because predict_disorder_batch
does not support legacy.
minimum_IDR_size : int
Used only if return_domains = True.
Defines the smallest possible IDR. This is a hard limit -
i.e. we CANNOT get IDRs smaller than this. Default = 12.
minimum_folded_domain : int
Used only if return_domains = True.
Defines where we expect the limit of small folded domains
to be. This is NOT a hard limit and functions to modulate
the removal of large gaps (i.e. gaps less than this size
are treated less strictly). Note that, in addition,
gaps < 35 are evaluated with a threshold of
0.35*disorder_threshold and gaps < 20 are evaluated with
a threshold of 0.25*disorder_threshold. These two
lengthscales were decided based on the fact that
coiled-coiled regions (which are IDRs in isolation)
often show up with reduced apparent disorder within IDRs,
and but can be as short as 20-30 residues.
The folded_domain_threshold is used based on the
idea that it allows a 'shortest reasonable' folded domain
to be identified. Default=50.
gap_closure : int
Used only if return_domains = True.
Defines the largest gap that would be 'closed'. Gaps here
refer to a scenario in which you have two groups of
disordered residues seprated by a 'gap' of un-disordered
residues. In general large gap sizes will favour larger
contigous IDRs. It's worth noting that gap_closure becomes
relevant only when minimum_region_size becomes very small
(i.e. < 5) because really gaps emerge when the smoothed
disorder fit is "noisy", but when smoothed gaps
are increasingly rare. Default=10.
override_folded_domain_minsize : bool
By default this function includes a fail-safe check that
assumes folded domains really shouldn't be less than
35 or 20 residues. However, for some approaches we may
wish to over-ride these thresholds to match the passed
minimum_folded_domain value. If this flag is set to
True this override occurs. This is generally not
recommended unless you expect there to be well-defined
sharp boundaries which could define small (20-30)
residue folded domains. This is not provided as an option
in the normal predict_disorder_domains for metapredict.
Default = False.
show_progress_bar : bool
Flag which, if set to True, means a progress bar is printed as
predictions are made, while if False no progress bar is printed.
Default = True
disable_batch : bool
Whether to override any use of batch predictions and predict
sequences individually.
Default = False
Returns
-------------
dict or list
IF RETURN DOMAINS == FALSE: this function returns either
a list or a dictionary.
If a list was provided as input, the function returns a list
of the same length as the input list, where each element is
itself a sublist where element 0 = sequence and element 1 is
a numpy array of disorder scores. The order of the return list
matches the order of the input list.
If a dictionary was provided as input, the function returns
a dictionary, where the same input keys map to values which are
lists of 2 elements, where element 0 = sequence and element 1 is
a numpy array of disorder scores.
IF RETURN DOMAINS == TRUE: this function returns either a list
or a dictionary.
If a list was provided as input, the function returns a list
of the same length as the input list, where each element is
a DisorderDomain object. The order of the return list matches
the order of the input list.
If a dictionary was provided as input, the function returns
a dictionary, where the same input keys map to a DisorderDomain
object that corresponds to the input dictionary sequence.
"""
# check version and make sure it is an uppercase string
version = _meta_tools.valid_version(version, 'disorder')
return _predict(input_sequences,
version=version,
use_device=device,
round_values=round_values,
return_numpy=return_numpy,
return_domains = return_domains,
disorder_threshold = disorder_threshold,
minimum_IDR_size = minimum_IDR_size,
minimum_folded_domain = minimum_folded_domain,
override_folded_domain_minsize=False,
gap_closure = gap_closure,
show_progress_bar = show_progress_bar,
force_disable_batch = disable_batch)
# ..........................................................................................
#
[docs]
def graph_disorder(sequence,
version = DEFAULT_NETWORK,
pLDDT_version = DEFAULT_NETWORK_PLDDT,
title = 'Predicted protein disorder',
disorder_threshold = None,
pLDDT_scores=False,
shaded_regions = None,
shaded_region_color = 'red',
DPI=150,
output_file=None):
"""
Function to plot the disorder of an input sequece. Displays immediately.
Parameters
-------------
sequence : str
Input amino acid sequence (as string) to be predicted.
version : string
The network to use for prediction. Default is DEFAULT_NETWORK,
which is defined at the top of /parameters.
Options currently include V1, V2, or V3.
pLDDT_version : string
The network to use for pLDDT prediction. Default is DEFAULT_NETWORK,
which is defined at the top of /parameters.
Options currently include V1, V2.
title : str
Sets the title of the generated figure. Default = "Predicted protein
disorder"
disorder_threshold : float
Set to None by default such that if the user chooses to set
legacy=True, the threshhold line will be at 0.3 and if legacy
is set to false (default) then the threshold line will be at 0.5.
Sets a threshold which draws a horizontal black line as a visual
guide along the length of the figure. Must be a value between 0
and 1. Default = 0.3 for legacy and 0.5 for new version of metapredict.
pLDDT_scores : Bool
Sets whether to include the predicted pLDDT scores in the figure
shaded_regions : list of lists
A list of lists, where sub-elements are of length 2 and contain
start and end values for regions to be shaded. Assumes that sanity
checking on positions has already been done. Default is None, but
if there were specific regions you wanted to highlight this might,
for example, look like shaded_regions=[[1,10],[40,50]], which would
shade between 1 and 10 and then between 40 and 50. This can be useful
to either highlight specific IDRs or specific folded domains
shaded_region_color : str or list of sts
String that defines the color of the shaded region. The shaded region
is always set with an alpha of 0.3 but the color can be any valid
matplotlib color name or a hex color string (i.e. "#ff0000" is red).
Alternatively a list where number of elements matches number in
shaded_regions, assigning a color-per-shaded regions.
DPI : int
Dots-per-inch. Defines the resolution of the generated figure.
Passed to the dpi argument in ``matplotlib.pyplot.savefig()``.
output_file : str
If provided, the output_file variable defines the location and type
of the file to be saved. This should be a file location and filename
with a valid matplotlib extension (such as .png, or .pdf) and, if
provided, this value is passed directly to the
``matplotlib.pyplot.savefig()`` function as the ``fname`` parameter.
Default = None.
Returns
--------
None
No return object, but, the graph is saved to disk or displayed
locally.
"""
# sanity check
_meta_tools.raise_exception_on_zero_length(sequence)
# check version and make sure it is an uppercase string
version = _meta_tools.valid_version(version, 'disorder')
pLDDT_version = _meta_tools.valid_version(pLDDT_version, 'pLDDT')
# get disorder threshold
if disorder_threshold == None:
disorder_threshold=metapredict_networks[version]['parameters']['disorder_threshold']
# check that a valid range was passed for disorder_threshold
_meta_tools.valid_range(disorder_threshold, 0.0, 1.0)
# ensure sequence is upper case
sequence = sequence.upper()
# check that a valid set of shaded regions was passed
_meta_tools.valid_shaded_region(shaded_regions, len(sequence))
# call the graph function
_graph(sequence, title = title, disorder_threshold = disorder_threshold,
pLDDT_scores = pLDDT_scores, shaded_regions = shaded_regions,
shaded_region_color = shaded_region_color,
DPI=DPI, output_file = output_file, version=version,
pLDDT_version=pLDDT_version)
# ..........................................................................................
#
[docs]
def predict_pLDDT(inputs, pLDDT_version=DEFAULT_NETWORK_PLDDT, return_decimals=False,
device=None, normalized=True, round_values=True, return_numpy=True,
print_performance=False, show_progress_bar=False, force_disable_batch=False,
disable_pack_n_pad=False, silence_warnings=False, return_as_disorder_score=False):
"""
Function to return predicted pLDDT scores. pLDDT scores are the scores
reported by AlphaFold2 (AF2) that provide a measure of the confidence
with which AF2 has on the local structure prediction. This now has massively
improved functionality and supporst the same batch mode as the disorder.
Parameters
----------
inputs : string list or dictionary
An individual sequence or a collection of sequences
that are presented either as a list of sequences or
a dictionary of key-value pairs where values are sequences.
pLDDT_version : string
The network to use for prediction. Default is DEFAULT_NETWORK_PLDDT,
which is defined at the top of /parameters.
Options currently include V1 or V2. V1 is the version used
to make legacy metapredict and is from 'alphaPredict'. V2
is a new network made much more recently.
return_decimals : bool
If False, values be between 0 and 100. If True, the values
will be between 0 and 1.
Default is False.
device : int or str
Identifier for the device to be used for predictions.
Possible inputs: 'cpu', 'mps', 'cuda', or an int that corresponds to
the index of a specific cuda-enabled GPU. If 'cuda' is specified and
cuda.is_available() returns False, instead of falling back to CPU,
metapredict will raise an Exception so you know that you are not
using CUDA as you were expecting.
Default: None
When set to None, we will check if there is a cuda-enabled
GPU. If there is, we will try to use that GPU.
If you set the value to be an int, we will use cuda:int as the device
where int is the int you specify. The GPU numbering is 0 indexed, so 0
corresponds to the first GPU and so on. Only specify this if you
know which GPU you want to use.
* Note: MPS is only supported in Pytorch 2.1 or later. If I remember
right it might have been beta supported in 2.0 *.
normalized : bool
Whether or not to normalize disorder values to between 0 and 1.
Default : True
round_values : bool
Whether to round the values to 4 decimal places.
Default : True
return_numpy : bool
Whether to return a numpy array or a list for single predictions.
Default : True
print_performance : bool
Flag which means the function prints the time taken
for the two stages in the prediction algorithm. Again
useful for profiling and debugging. Default = False
show_progress_bar : bool
Flag which, if set to True, means a progress bar is printed as
predictions are made, while if False no progress bar is printed.
Default = True
force_disable_batch : bool
Whether to override any use of batch predictions and predict
sequences individually.
Default = False
disable_pack_n_pad : bool
Whether to disable the use of pack_n_pad in the prediction
algorithm. This is useful for debugging and profiling. Also gives
us a way for people to use older versions of torch.
Default = False
silence_warnings : bool
whether to silence warnings such as the one about compatibility
to use pack-n-pad due to torch version restrictions.
return_as_disorder_score : bool
Whether to return as a disorder score.
This basically inverts the score as a decimal and normalizes so that
it looks like a disorder score (higher value=disordereed and lower
value = not disordered). This is similar to the approach that we used
to generate the scores that were combined with legacy metapredict to make
V2 and V3.
Returns
--------
list or np.ndarray
Returns a list (or np.ndarray) of floats that corresponds to the
per-residue pLDDT score. Return type depends on the flag
return_numpy
"""
# sanity check
_meta_tools.raise_exception_on_zero_length(inputs)
# check version and make sure it is an uppercase string
pLDDT_version = _meta_tools.valid_version(pLDDT_version, 'pLDDT')
# return predicted values of disorder for sequence
return _predict_pLDDT(inputs,
version=pLDDT_version, return_decimals=return_decimals,
use_device=device, normalized=normalized,
round_values=round_values, return_numpy=return_numpy,
print_performance=print_performance,
show_progress_bar = show_progress_bar,
force_disable_batch=force_disable_batch,
disable_pack_n_pad = disable_pack_n_pad,
silence_warnings = silence_warnings,
return_as_disorder_score=return_as_disorder_score)
# ..........................................................................................
#
[docs]
def graph_pLDDT(sequence,
title = 'Predicted AF2 pLDDT Confidence Score',
disorder_scores=False,
shaded_regions = None,
shaded_region_color = 'red',
DPI=150,
output_file=None,
pLDDT_version = DEFAULT_NETWORK_PLDDT):
"""
Function to plot the AF2 pLDDT scores of an input sequece. Displays immediately.
Parameters
-------------
sequence : str
Input amino acid sequence (as string) to be predicted.
title : str
Sets the title of the generated figure.
Default = "Predicted AF2 pLDDT Confidence Score"
disorder_scores : Bool
Whether to include disorder scores. Can set to False if you
just want the AF2 confidence scores.
Default = False
shaded_regions : list of lists
A list of lists, where sub-elements are of length 2 and contain
start and end values for regions to be shaded. Assumes that sanity
checking on positions has already been done. Default is None, but
if there were specific regions you wanted to highlight this might,
for example, look like shaded_regions=[[1,10],[40,50]], which would
shade between 1 and 10 and then between 40 and 50. This can be useful
to either highlight specific IDRs or specific folded domains.
Default = None
shaded_region_color : str
String that defines the color of the shaded region. The shaded region
is always set with an alpha of 0.3 but the color can be any valid
matplotlib color name or a hex color string (i.e. "#ff0000" is red).
DPI : int
Dots-per-inch. Defines the resolution of the generated figure.
Passed to the dpi argument in ``matplotlib.pyplot.savefig()``.
output_file : str
If provided, the output_file variable defines the location and type
of the file to be saved. This should be a file location and filename
with a valid matplotlib extension (such as .png, or .pdf) and, if
provided, this value is passed directly to the
``matplotlib.pyplot.savefig()`` function as the ``fname`` parameter.
Default = None.
pLDDT_version : string
The network to use for prediction. Default is DEFAULT_NETWORK_PLDDT,
which is defined at the top of /parameters.
Options currently include V1 or V2
Returns
--------
None
No return object, but, the graph is saved to disk or displayed locally.
"""
# sanity check
_meta_tools.raise_exception_on_zero_length(sequence)
# check that a valid set of shaded regions was passed
_meta_tools.valid_shaded_region(shaded_regions, len(sequence))
# check version and make sure it is an uppercase string
pLDDT_version = _meta_tools.valid_version(pLDDT_version, 'pLDDT')
# call the graph function
_graph(sequence, title = title, pLDDT_scores = True,
disorder_scores=disorder_scores, shaded_regions = shaded_regions,
shaded_region_color = shaded_region_color,
DPI=DPI, output_file = output_file, pLDDT_version=pLDDT_version)
# ..........................................................................................
#
[docs]
def percent_disorder(sequence, disorder_threshold=None, mode='threshold',
version=DEFAULT_NETWORK):
"""
Function that returns the percent disorder for any given protein.
By default, uses 0.5 as a cutoff for the new version of metapredict
and 0.3 for the legacy version of metapredict (values greater than or equal
to 0.5 will be considered disordered). If a value for cutoff is specified,
that value will be used.
Mode lets you toggle between 'threshold' and 'disorder_domains'. If
threshold is used a simple per-residue logic operation is applied
and the fraction of residues above the disorder_threshold is used.
If 'disorder_domains' is used then the sequence is divided into
IDRs and folded domains using the predict_disordered_domains()
function.
Parameters
-------------
sequence : str
Input amino acid sequence (as string) to be predicted.
disorder_threshold : float
Set to None by default such that it will change depending
on whether legacy is set to True or False.
Sets a threshold which defines if a residue is considered disordered
or not. Default for new metapredict = 0.5. Default for legacy metapredict
is 0.3.
mode : str
Selector which lets you choose which mode to calculate percent disorder
with. Default is 'threshold', meaning the percentage of disorder is
calculated as what fraction of residues are above the disorder_threshold.
Alternatively, 'disorder_domains' means we use the
predict_disorder_domains() function and then calculate what fraction of
the protein's residues are in the predicted IDRs.
version : string
The network to use for prediction. Default is DEFAULT_NETWORK,
which is defined at the top of /parameters.
Options currently include V1, V2, or V3.
Returns
-----------
float
Returns a floating point value between 0 and 100 that defines what
percentage of the sequence is considered disordered.
"""
# sanity check
_meta_tools.raise_exception_on_zero_length(sequence)
# check mode is valid first
mode = mode.lower()
if mode not in ['threshold', 'disorder_domains']:
raise MetapredictError(f"Mode must be one of 'threshold' or 'disorder_domains', but '{mode}' was passed instead")
# check version and make sure it is an uppercase string
version = _meta_tools.valid_version(version, 'disorder')
# make all residues upper case
sequence = sequence.upper()
# set the disorder threshold
if disorder_threshold == None:
disorder_threshold=metapredict_networks[version]['parameters']['disorder_threshold']
if mode == 'threshold':
# get disorder
dis = _predict(sequence, version=version, return_numpy=True)
# check threshold is valid
_meta_tools.valid_range(disorder_threshold, 0.0, 1.0)
# get number over value
disorder_count = (dis >= disorder_threshold).sum()
"""
percent disorder is equal to n (number of residues with predicted
value >= cutoff) divided by the total number of residues in the
input sequence.
"""
percent_disordered = round(100*((disorder_count / len(sequence))), 3)
# else using disordered domains
else:
# if no specified threshold use the defaults
idrs=predict_disorder_domains(sequence, disorder_threshold, version=version).disordered_domains
fraction_disordered = sum([len(i) for i in idrs])/len(sequence)
percent_disordered = round(100*(fraction_disordered), 3)
return percent_disordered
#./\./\./\./\./\./\./\./\./\./\./\./\./\./\./\./\./\./\./\./\./\./\./\./\./\./\./\./\
#./\./\./\./\./\./\./\./\./\./\./\./\.FASTA STUFF./\./\./\./\./\./\./\./\./\./\./\./\
#./\./\./\./\./\./\./\./\./\./\./\./\./\./\./\./\./\./\./\./\./\./\./\./\./\././\./\
#Various functions for working with fasta files to make everyones life easier.
[docs]
def predict_disorder_fasta(filepath,
output_file = None,
normalized=True,
invalid_sequence_action='convert',
version=DEFAULT_NETWORK,
device=None,
show_progress_bar=True):
"""
Function to read in a .fasta file from a specified filepath.
Returns a dictionary of disorder values where the key is the
fasta header and the values are the predicted disorder values.
Parameters
-------------
filepath : str
The path to where the .fasta file is located. The filepath should
end in the file name, and can be an absolute or relative path
output_file : str
By default, a dictionary of predicted values is returned
immediately. However, you can specify an output filename and path
and a .csv file will be saved. This should include any file extensions.
Default = None.
normalized : bool
Flag which defines in the predictor should control and normalize such
that all values fall between 0 and 1. The underlying learning model can,
in fact output some negative values and some values greater than 1.
Normalization controls for this. Default = True
invalid_sequence_action : str
Tells the function how to deal with sequences that lack standard amino
acids. Default is convert, which as the name implies converts via standard
rules. See https://protfasta.readthedocs.io/en/latest/read_fasta.html
for more information.
version : string
The network to use for prediction. Default is DEFAULT_NETWORK,
which is defined at the top of /parameters.
Options currently include V1, V2, or V3.
device : string
the device to use for prediction. Default is None, which means
the function will try to use a GPU if one is available.
Options include 'cpu', 'cuda', 'mps', or an int that corresponds
to the index of a specific cuda-enabled GPU. To specify by index,
use 'cuda:int' where int is the index of the GPU you want to use.
For example, 'cuda:0' would use the first GPU. If 'cuda' is specified
and cuda.is_available() returns False, instead of falling back to
CPU, metapredict will raise an Exception so you know that you are
not using CUDA as you were expecting.
show_progress_bar : bool
Flag which, if set to True, means a progress bar is printed as
predictions are made, while if False no progress bar is printed.
Returns
--------
dict or None
If output_file is set to None (as default) then this fiction returns
a dictionary of sequence ID to disorder np.ndarrays(dtype=np.float32).
If output_file is set to a filename then a .csv file will instead
be written and no return data will be provided.
"""
# Importantly, by default this function corrects invalid residue
# values using protfasta.read_fasta() because the disorder predictor
# cannot have non-amino acid values as an input.
# Test to see if the data_file exists
test_data_file = os.path.abspath(filepath)
# check version and make sure it is an uppercase string
version = _meta_tools.valid_version(version, 'disorder')
# check path
if not os.path.isfile(test_data_file):
raise FileNotFoundError(f'Datafile [{filepath}] does not exist.')
# get seqs via protfasta
protfasta_seqs = _protfasta.read_fasta(filepath, invalid_sequence_action = invalid_sequence_action)
# initialize return dictionary
disorder_dict = _predict(protfasta_seqs, version=version,
normalized=normalized, return_numpy=False,
show_progress_bar=show_progress_bar,
use_device=device)
# if we did not request an output file
if output_file is None:
return disorder_dict
# else write to disk
else:
_meta_tools.write_csv(disorder_dict, output_file)
# ..........................................................................................
#
[docs]
def predict_pLDDT_fasta(filepath,
output_file = None,
invalid_sequence_action='convert',
pLDDT_version=DEFAULT_NETWORK_PLDDT,
device=None,
show_progress_bar=True):
"""
Function to read in a .fasta file from a specified filepath.
Returns a dictionary of pLDDT values where the key is the
fasta header and the values are the predicted pLDDT values.
Parameters
-------------
filepath : str
The path to where the .fasta file is located. The filepath should
end in the file name, and can be an absolute or relative path
output_file : str
By default, a dictionary of predicted values is returned
immediately. However, you can specify an output filename and path
and a .csv file will be saved. This should include any file extensions.
Default = None.
invalid_sequence_action : str
Tells the function how to deal with sequences that lack standard amino
acids. Default is convert, which as the name implies converts via standard
rules. See https://protfasta.readthedocs.io/en/latest/read_fasta.html
for more information.
pLDDT_version : string
The network to use for prediction. Default is DEFAULT_NETWORK_PLDDT,
which is defined at the top of /parameters.
Options currently include V1 or V2
device : string
the device to use for prediction. Default is None, which means
the function will try to use a GPU if one is available.
Options include 'cpu', 'cuda', 'mps', or an int that corresponds
to the index of a specific cuda-enabled GPU. To specify by index,
use 'cuda:int' where int is the index of the GPU you want to use.
For example, 'cuda:0' would use the first GPU. If 'cuda' is specified
and cuda.is_available() returns False, instead of falling back to
CPU, metapredict will raise an Exception so you know that you are
not using CUDA as you were expecting.
show_progress_bar : bool
Flag which, if set to True, means a progress bar is printed as
predictions are made, while if False no progress bar is printed.
Returns
--------
dict or None
If output_file is set to None (as default) then this fiction returns a
dictionary of sequence ID to pLDDT vector. If output_file is set to a
filename then a .csv file will instead be written and no return data
will be provided.
"""
# Importantly, by default this function corrects invalid residue
# values using protfasta.read_fasta() because the pLDDT predictor
# cannot have non-amino acid values as an input.
# Test to see if the data_file exists
test_data_file = os.path.abspath(filepath)
if not os.path.isfile(test_data_file):
raise FileNotFoundError(f'Datafile does not exist.')
protfasta_seqs = _protfasta.read_fasta(filepath, invalid_sequence_action = invalid_sequence_action)
# check version and make sure it is an uppercase string
pLDDT_version = _meta_tools.valid_version(pLDDT_version, 'pLDDT')
# new predict_pLDDT function can handle string, list, or dict.
confidence_dict = _predict_pLDDT(protfasta_seqs,
version=pLDDT_version,
return_numpy=False,
show_progress_bar=show_progress_bar,
use_device=device)
# if we did not request an output file
if output_file is None:
return confidence_dict
# else write to disk
else:
_meta_tools.write_csv(confidence_dict, output_file)
# ..........................................................................................
#
[docs]
def graph_disorder_fasta(filepath,
pLDDT_scores=False,
disorder_threshold = None,
DPI=150,
output_dir = None,
output_filetype='png',
invalid_sequence_action='convert',
indexed_filenames=False,
version=DEFAULT_NETWORK,
pLDDT_version=DEFAULT_NETWORK_PLDDT):
"""
Function to make graphs of predicted disorder from the sequences
in a specified .fasta file. By default will save the generated
graphs to the location output_path specified in filepath.
**WARNING**: It is unadvisable to not include an output directory if you are reading in a .fasta
file with many sequences! This is because each graph must be closed individually before the next
will appear. Therefore, you will spend a bunch of time closing each graph.
**NB**: You cannot specify the output file name here! By default, the file name will
be the first 14 characters of the FASTA header followed by the filetype as specified
by filetype. If you wish for the files to include a unique leading number (i.e. X_rest_of_name
where X starts at 1 and increments) then set indexed_filenames to True. This can be useful if you
have sequences where the 1st 14 characters may be identical, which would otherwise overwrite an
output file.
Parameters
-----------
filepath : str
The path to where the .fasta file is located. The filepath should end in the file name.
For example (on MacOS):filepath="/Users/thisUser/Desktop/folder_of_seqs/interesting_proteins.fasta"
pLDDT_scores : Bool
Sets whether to include the predicted pLDDT scores from
AlphaFold2
disorder_threshold : float
Sets a threshold which draws a horizontal black line as a visual guide along
the length of the figure. Must be a value between 0 and 1.
DPI : int
Dots-per-inch. Defines the resolution of the generated figure. Passed to the
dpi argument in ``matplotlib.pyplot.savefig()``.
output_dir : str
If provided, the output_dir variable defines the directory where file should besaved
to be saved. This should be a writeable filepath. Default is None. Output files are
saved with filename as first 14 chars of fasta header (minus bad characters) plus the
appropriate file extension, as defined by filetype.
output_filetype : str
String that defines the output filetype to be used. Must be one of pdf, png, jpg.
invalid_sequence_action : str
Tells the function how to deal with sequences that lack standard amino acids. Default is
convert, which as the name implies converts via standard rules. See
https://protfasta.readthedocs.io/en/latest/read_fasta.html for more information.
indexed_filenames : bool
Bool which, if set to true, means filenames start with an unique integer.
version : string
The network to use for prediction. Default is DEFAULT_NETWORK,
which is defined at the top of /parameters.
Options currently include V1, V2, or V3.
pLDDT_version : string
The network to use for pLDDT prediction.
Default is DEFAULT_NETWORK_PLDDT,
which is defined at the top of /parameters.
Options currently include V1 or V2
Returns
---------
None
No return object, but, the graph is saved to disk or displayed locally.
"""
# check version and make sure it is an uppercase string
version = _meta_tools.valid_version(version, 'disorder')
# see if we need to set the disorder threshold
if disorder_threshold == None:
disorder_threshold = metapredict_networks[version]['parameters']['disorder_threshold']
# check pLDDT version and make sure it is an uppercase string
pLDDT_version = _meta_tools.valid_version(pLDDT_version, 'pLDDT')
# check that a valid range was passed for disorder_threshold
_meta_tools.valid_range(disorder_threshold, 0.0, 1.0)
# Test to see if the data_file exists
if not os.path.isfile(filepath):
raise FileNotFoundError(f'Datafile [{filepath:s}] does not exist')
# Test to see if output directory exists
if output_dir is not None:
if not os.path.isdir(output_dir):
raise FileNotFoundError(f'Proposed output directory could not be found')
# validate disorder_threshold
_meta_tools.valid_range(disorder_threshold, 0,1)
# use protfasta to read in fasta file
sequences = _protfasta.read_fasta(filepath, invalid_sequence_action = invalid_sequence_action)
# now for each sequence...
idx_counter = 0
for idx in tqdm(sequences):
# increment the index counter...
idx_counter = idx_counter + 1
# grab the sequence and convert to upper as well
local_sequence = sequences[idx].upper()
# make sure file doesn't try to save if no output dir specified
if output_dir is not None:
# define the full filename with filetype. NOTE - we use os.sep as an OS-independent way to define
# filename and filepath. This may end up with the filename containing a double slash, but this is fine
# and matplotlib deals with this appropriately. This should be a POSIX-compliant way to do cross-platform
# file writing
if indexed_filenames:
filename = output_dir + os.sep + f"{idx_counter:d}_" + _meta_tools.sanitize_filename(idx)[0:14] + f".{output_filetype:s}"
else:
filename = output_dir + os.sep + _meta_tools.sanitize_filename(idx)[0:14] + f".{output_filetype:s}"
# define title (including bad chars)
title = idx[0:14]
# plot!
graph_disorder(local_sequence, title=title, pLDDT_scores=pLDDT_scores,
DPI=DPI, output_file=filename, version=version,
pLDDT_version=pLDDT_version, disorder_threshold=disorder_threshold)
# if no output_dir specified just graph the seq
else:
# define title (including bad chars)
title = idx[0:14]
graph_disorder(local_sequence, title=title, pLDDT_scores=pLDDT_scores, DPI=DPI,
version=version, pLDDT_version=pLDDT_version,
disorder_threshold=disorder_threshold)
# ..........................................................................................
#
[docs]
def graph_pLDDT_fasta(filepath,
DPI=150,
output_dir = None,
output_filetype='png',
invalid_sequence_action='convert',
indexed_filenames=False,
pLDDT_version=DEFAULT_NETWORK_PLDDT):
"""
Function to make graphs of predicted pLDDT from the sequences
in a specified .fasta file. By default will save the generated
graphs to the location output_path specified in filepath.
**WARNING**: It is unadvisable to not include an output directory if you are reading in a .fasta
file with many sequences! This is because each graph must be closed individually before the next
will appear. Therefore, you will spend a bunch of time closing each graph.
**NB**: You cannot specify the output file name here! By default, the file name will
be the first 14 characters of the FASTA header followed by the filetype as specified
by filetype. If you wish for the files to include a unique leading number (i.e. X_rest_of_name
where X starts at 1 and increments) then set indexed_filenames to True. This can be useful if you
have sequences where the 1st 14 characters may be identical, which would otherwise overwrite an
output file.
Parameters
-----------
filepath : str
The path to where the .fasta file is located. The filepath should end in the file name.
For example (on MacOS):filepath="/Users/thisUser/Desktop/folder_of_seqs/interesting_proteins.fasta"
DPI : int
Dots-per-inch. Defines the resolution of the generated figure. Passed to the
dpi argument in ``matplotlib.pyplot.savefig()``.
output_dir : str
If provided, the output_dir variable defines the directory where file should besaved
to be saved. This should be a writeable filepath. Default is None. Output files are
saved with filename as first 14 chars of fasta header (minus bad characters) plus the
appropriate file extension, as defined by filetype.
output_filetype : str
String that defines the output filetype to be used. Must be one of pdf, png, jpg.
invalid_sequence_action : str
Tells the function how to deal with sequences that lack standard amino acids. Default is
convert, which as the name implies converts via standard rules. See
https://protfasta.readthedocs.io/en/latest/read_fasta.html for more information.
indexed_filenames : bool
Bool which, if set to true, means filenames start with an unique integer.
pLDDT_version : string
The network to use for prediction. Default is DEFAULT_NETWORK_PLDDT,
which is defined at the top of /parameters.
Options currently include V1 or V2
Returns
---------
None
No return object, but, the graph is saved to disk or displayed locally.
"""
# Test to see if the data_file exists
if not os.path.isfile(filepath):
raise FileNotFoundError(f'Datafile [{filepath:s}] does not exist')
# Test to see if output directory exists
if output_dir is not None:
if not os.path.isdir(output_dir):
raise FileNotFoundError(f'Proposed output directory could not be found')
# check version and make sure it is an uppercase string
pLDDT_version = _meta_tools.valid_version(pLDDT_version, 'pLDDT')
# use protfasta to read in fasta file
sequences = _protfasta.read_fasta(filepath, invalid_sequence_action = invalid_sequence_action)
# now for each sequence...
idx_counter = 0
for idx in tqdm(sequences):
# increment the index counter...
idx_counter = idx_counter + 1
# grab the sequence and convert to upper as well
local_sequence = sequences[idx].upper()
# make sure file doesn't try to save if no output dir specified
if output_dir is not None:
# define the full filename with filetype. NOTE - we use os.sep as an OS-independent way to define
# filename and filepath. This may end up with the filename containing a double slash, but this is fine
# and matplotlib deals with this appropriately. This should be a POSIX-compliant way to do cross-platform
# file writing
if indexed_filenames:
filename = output_dir + os.sep + f"{idx_counter:d}_" + _meta_tools.sanitize_filename(idx)[0:14] + f".{output_filetype:s}"
else:
filename = output_dir + os.sep + _meta_tools.sanitize_filename(idx)[0:14] + f".{output_filetype:s}"
# define title (including bad chars)
title = idx[0:14]
# plot!
graph_pLDDT(local_sequence, title=title, DPI=DPI, output_file=filename, pLDDT_version=pLDDT_version)
# if no output_dir specified just graph the seq
else:
# define title (including bad chars)
title = idx[0:14]
graph_pLDDT(local_sequence, title=title, DPI=DPI, pLDDT_version=pLDDT_version)
# ..........................................................................................
#
[docs]
def predict_disorder_uniprot(uniprot_id, normalized=True, version=DEFAULT_NETWORK):
"""
Function to return disorder of a single input sequence. Uses a
Uniprot ID to get the sequence.
Parameters
------------
uniprot_ID : str
The uniprot ID of the sequence to predict
no_ID : str
The uniprot ID of the sequence to predict
version : string
The network to use for prediction. Default is DEFAULT_NETWORK,
which is defined at the top of /parameters.
Options currently include V1, V2, or V3.
Returns
----------
None
No return object, but, the graph is saved to disk or displayed locally.
"""
# check version and make sure it is an uppercase string
version = _meta_tools.valid_version(version, 'disorder')
# fetch sequence from Uniprot
sequence = _getseq(uniprot_id)[1]
# return predicted values of disorder for sequence
return _predict(sequence, normalized=normalized, version=version)
# ..........................................................................................
#
[docs]
def predict_pLDDT_uniprot(uniprot_id, pLDDT_version=DEFAULT_NETWORK_PLDDT):
"""
Function to return pLDDT score of a single input sequence. Uses a
Uniprot ID to get the sequence.
Parameters
------------
uniprot_ID : str
The uniprot ID of the sequence to predict
pLDDT_version : string
The network to use for prediction. Default is DEFAULT_NETWORK_PLDDT,
which is defined at the top of /parameters.
Options currently include V1 or V2
Returns
----------
None
No return object, but, the graph is saved to disk or displayed locally.
"""
# fetch sequence from Uniprot
sequence = _getseq(uniprot_id)[1]
# check version and make sure it is an uppercase string
pLDDT_version = _meta_tools.valid_version(pLDDT_version, 'pLDDT')
# return predicted values of disorder for sequence
return _predict_pLDDT(sequence, version=pLDDT_version)
# ..........................................................................................
#
[docs]
def graph_disorder_uniprot(uniprot_id,
title = 'Predicted protein disorder',
pLDDT_scores=False,
disorder_threshold = None,
shaded_regions = None,
shaded_region_color = 'red',
DPI=150,
output_file=None,
version=DEFAULT_NETWORK,
pLDDT_version=DEFAULT_NETWORK_PLDDT):
"""
Function to plot the disorder of an input sequece. Displays immediately.
Parameters
-------------
sequence : str
Input amino acid sequence (as string) to be predicted.
title : str
Sets the title of the generated figure. Default = "Predicted protein disorder"
pLDDT_scores : Bool
Sets whether to include the predicted pLDDT scores from
AlphaFold2
disorder_threshold : float
Set to None by default such that it will change depending of if the user
sets legacy to True of if legacy remains = False. Can still be set manually.
Sets a threshold which draws a horizontal black line as a visual guide along
the length of the figure. Must be a value between 0 and 1.
shaded_regions : list of lists
A list of lists, where sub-elements are of length 2 and contain start and end
values for regions to be shaded. Assumes that sanity checking on positions has
already been done. Default is None, but if there were specific regions you wanted
to highlight this might, for example, look like shaded_regions=[[1,10],[40,50]],
which would shade between 1 and 10 and then between 40 and 50. This can be useful
to either highlight specific IDRs or specific folded domains
shaded_region_color : str
String that defines the color of the shaded region. The shaded region is always
set with an alpha of 0.3 but the color can be any valid matplotlib color name
or a hex color string (i.e. "#ff0000" is red).
DPI : int
Dots-per-inch. Defines the resolution of the generated figure. Passed to the
dpi argument in ``matplotlib.pyplot.savefig()``.
output_file : str
If provided, the output_file variable defines the location and type of the file
to be saved. This should be a file location and filename with a valid matplotlib
extension (such as .png, or .pdf) and, if provided, this value is passed directly
to the ``matplotlib.pyplot.savefig()`` function as the ``fname`` parameter.
Default = None.
version : string
The network to use for prediction. Default is DEFAULT_NETWORK,
which is defined at the top of /parameters.
Options currently include V1, V2, or V3.
pLDDT_version : string
The network to use for prediction. Default is DEFAULT_NETWORK_PLDDT,
which is defined at the top of /parameters.
Options currently include V1 or V2
Returns
----------
None
No return object, but, the graph is saved to disk or displayed locally.
"""
# check version and make sure it is an uppercase string
version = _meta_tools.valid_version(version, 'disorder')
if disorder_threshold==None:
disorder_threshold=metapredict_networks[version]['parameters']['disorder_threshold']
# check version and make sure it is an uppercase string
pLDDT_version = _meta_tools.valid_version(pLDDT_version, 'pLDDT')
# check that a valid range was passed for
_meta_tools.valid_range(disorder_threshold, 0.0, 1.0)
# grab uniprot sequence
sequence = _getseq(uniprot_id)[1]
# graph sequence
_graph(sequence, title=title, pLDDT_scores=pLDDT_scores, disorder_threshold=disorder_threshold, shaded_regions=shaded_regions,
shaded_region_color=shaded_region_color, DPI=DPI, output_file = output_file,
version=version, pLDDT_version=pLDDT_version)
# ..........................................................................................
#
[docs]
def graph_pLDDT_uniprot(uniprot_id,
title = 'Predicted AF2 pLDDT Scores',
shaded_regions = None,
shaded_region_color = 'red',
DPI=150,
output_file=None,
pLDDT_version=DEFAULT_NETWORK_PLDDT):
"""
Function to plot the disorder of an input sequece. Displays immediately.
Parameters
-------------
sequence : str
Input amino acid sequence (as string) to be predicted.
title : str
Sets the title of the generated figure. Default = "Predicted protein disorder"
shaded_regions : list of lists
A list of lists, where sub-elements are of length 2 and contain start and end
values for regions to be shaded. Assumes that sanity checking on positions has
already been done. Default is None, but if there were specific regions you wanted
to highlight this might, for example, look like shaded_regions=[[1,10],[40,50]],
which would shade between 1 and 10 and then between 40 and 50. This can be useful
to either highlight specific IDRs or specific folded domains
shaded_region_color : str
String that defines the color of the shaded region. The shaded region is always
set with an alpha of 0.3 but the color can be any valid matplotlib color name
or a hex color string (i.e. "#ff0000" is red).
DPI : int
Dots-per-inch. Defines the resolution of the generated figure. Passed to the
dpi argument in ``matplotlib.pyplot.savefig()``.
output_file : str
If provided, the output_file variable defines the location and type of the file
to be saved. This should be a file location and filename with a valid matplotlib
extension (such as .png, or .pdf) and, if provided, this value is passed directly
to the ``matplotlib.pyplot.savefig()`` function as the ``fname`` parameter.
Default = None.
pLDDT_version : string
The network to use for prediction. Default is DEFAULT_NETWORK_PLDDT,
which is defined at the top of /parameters.
Options currently include V1 or V2
Returns
----------
None
No return object, but, the graph is saved to disk or displayed locally.
"""
# grab uniprot sequence
sequence = _getseq(uniprot_id)[1]
# check version and make sure it is an uppercase string
pLDDT_version = _meta_tools.valid_version(pLDDT_version, 'pLDDT')
# graph sequence
_graph(sequence, title=title, disorder_scores=False, pLDDT_scores=True,
shaded_regions=shaded_regions, shaded_region_color=shaded_region_color,
DPI=DPI, output_file = output_file, pLDDT_version=pLDDT_version)
# ..........................................................................................
#
[docs]
def predict_disorder_domains_uniprot(uniprot_id,
disorder_threshold=None,
minimum_IDR_size=12,
minimum_folded_domain=50,
gap_closure=10,
normalized=True,
return_numpy=True,
version=DEFAULT_NETWORK):
"""
This function takes an amino acid sequence, a disorder score, and
returns either a DisorderObjec 4-position tuple with the information
listed below.
Parameters
-------------
uniprot_ID : String
The uniprot ID of the sequence to predict
sequence : str
Amino acid sequence
disorder_threshold : float
Set to None by default such that the threshold value is is dependent
on whether legacy is set to True. The default for legacy is 0.42, the
default for the new metapredict is 0.5.
Value that defines what 'disordered' is based on the metapredict
disorder score.
minimum_IDR_size : int
Defines the smallest possible IDR. This is a hard limit - i.e. we
CANNOT get IDRs smaller than this. Default = 12.
minimum_folded_domain : int
Defines where we expect the limit of small folded domains to be.
This is NOT a hard limit and functions to modulate the removal of
large gaps (i.e. gaps less than this size are treated less strictly).
Note that, in addition, gaps < 35 are evaluated with a threshold of
0.35*disorder_threshold and gaps < 20 are evaluated with a threshold
of 0.25*disorder_threshold. These two lengthscales were decided based
on the fact that coiled-coiled regions (which are IDRs in isolation)
often show up with reduced apparent disorder within IDRs, and but can
be as short as 20-30 residues. The folded_domain_threshold is used
based on the idea that it allows a 'shortest reasonable' folded domain
to be identified. Default=50.
gap_closure : int
Defines the largest gap that would be 'closed'. Gaps here refer to a
scenario in which you have two groups of disordered residues seprated
by a 'gap' of un-disordered residues. In general large gap sizes will
favour larger contigous IDRs. It's worth noting that gap_closure becomes
relevant only when minimum_region_size becomes very small (i.e. < 5)
because really gaps emerge when the smoothed disorder fit is "noisy", but
when smoothed gaps are increasingly rare. Default=10.
return_numpy : bool
Flag which if set to true means all numerical types are returned
as numpy.ndlist. Default is True
version : string
The network to use for prediction. Default is DEFAULT_NETWORK,
which is defined at the top of /parameters.
Options currently include V1, V2, or V3.
Returns
---------
DisorderObject
Returns a DisorderObject. DisorderObject has 7 dot variables:
.sequence : str
Amino acid sequence
.disorder : list or np.ndaarray
Hybrid disorder score
.disordered_domain_boundaries : list
List of domain boundaries for IDRs using Python indexing
.folded_domain_boundaries : list
List of domain boundaries for folded domains using Python indexing
.disordered_domains : list
List of the actual sequences for IDRs
.folded_domains : list
List of the actual sequences for folded domains
"""
# check version and make sure it is an uppercase string
version = _meta_tools.valid_version(version, 'disorder')
if disorder_threshold==None:
disorder_threshold=metapredict_networks[version]['parameters']['disorder_threshold']
sequence = _getseq(uniprot_id)[1]
DisObj = _predict(sequence, normalized=normalized, version=version, return_domains=True,
disorder_threshold=disorder_threshold, minimum_IDR_size=minimum_IDR_size,
minimum_folded_domain=minimum_folded_domain,return_numpy=return_numpy,
gap_closure=gap_closure)
return DisObj
# ..........................................................................................
#
[docs]
def predict_disorder_caid(input_fasta, output_path, version=DEFAULT_NETWORK,
use_fixed_cutoff=None):
'''
executing script for generating a caid-compliant output file for disorder
predictions using a .fasta file as the input.
Parameters
-----------
input_fasta : str
the input file as a string that includes the file path preceeding
the file name if the file is not in the curdir
output_path : str
the path where to save the output files.
version : string
The network to use for prediction. Default is DEFAULT_NETWORK,
which is defined at the top of /parameters.
Options currently include V1, V2, or V3.
use_fixed_cutoff : float or None
If None (default) the per-residue binary classification in the CAID
output is derived from the metapredict domain-decomposition algorithm
(i.e. residues inside an IDR are classified as 1, otherwise 0). If a
float between 0 and 1 is provided, the binary classification is
instead assigned by simple thresholding of the per-residue disorder
score against this cutoff (score >= cutoff -> 1).
Returns
--------
None
Does not return anything, saves a file to the destination output file
'''
# check version and make sure it is an uppercase string
version = _meta_tools.valid_version(version, 'disorder')
# validate the fixed cutoff if one was provided
if use_fixed_cutoff is not None:
_meta_tools.valid_range(use_fixed_cutoff, 0.0, 1.0)
# read in the ids and seqs as a list of lists where each list has a first element that corresponds
# to the ID and the second corresponds to the sequence. Convert invalid amino acids if needed.
entry_id_and_seqs = _protfasta.read_fasta(input_fasta, return_list=False, invalid_sequence_action = 'convert')
# If a fixed cutoff was requested we don't need the domain decomposition;
# otherwise return DisorderObjects so we can use IDR domain boundaries to
# assign the binary disorder classifications in the CAID output.
return_domains = use_fixed_cutoff is None
predictions = _predict(entry_id_and_seqs, version=version, return_numpy=False,
return_domains=return_domains)
# write the output file
_meta_tools.write_caid_format(predictions, output_path, version=version,
use_fixed_cutoff=use_fixed_cutoff)