metapredict 0.60

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metapredict: A machine learning based tool for predicting protein disorder.

metapredict uses a bidirectional recurrent neural network trained on the consensus disorder values from 8 disorder predictors from 12 proteomes that were obtained from MobiDB. The creation of metapredict was made possible by idptools-parrot.

What is metapredict?

metapredict is a bit different than your typical protein disorder predictor. Instead of predicting the percent chance that a residue within a sequence might be disordered, metapredict tries to predict the consensus disorder score for the residue. This is because metapredict was trained on consensus values from MobiDB. These values are the percent of other disorder predictors that predicted a residue in a sequence to be disordered. For example, if a residue in a sequence has a value of 1 from the MobiDB consensus values, then all disorder predictors predicted that residue to be disordered. If the value was 0.5, than half of the predictors predicted that residue to be disordered. In this way, metapredict can help you quickly determine the likelihood that any given sequence is disordered by giving you an approximations of what other predictors would predict (things got pretty 'meta' there, hence the name metapredict).

Why is metapredict useful?

A major drawback of consensus disorder databases is that they can only give you values of previously predicted protein sequencecs. Therefore, if your sequence of interest is not in their database, tough luck. Fortunately, metapredict gives you a way around this problem!

For full documentation, please see: https://metapredict.readthedocs.io/en/latest/getting_started.html

metapredict allows for predicting disorder for any amino acid sequence, and predictions can be output as graphs or as raw values. Additionally, metapredict allows for predicting disorder values for protein sequences from .fasta files either directly in Python or from the command-line.

Installation:

metapredict is available through PyPI - to install simply run

$ pip install metapredict

Alternatively, you can get metapredict directly from GitHub.

To clone the GitHub repository and gain the ability to modify a local copy of the code, run

$ git clone https://github.com/idptools/metapredict.git
$ cd metapredict
$ pip install .

This will install metapredict locally.

Usage:

There are two ways you can use metapredict:

1. Directly from the command-line
2. From within Python

Using metapredict from the command-line:

Predicting Disorder

The metapredict-predict-disorder command from the command line takes a .fasta file as input and returns a .csv file containing rows where the first cell in the row is the fasta header and all subsequent cells in that row are predicted consensus disorder values for each residue in the amino acid sequence associated with the fasta header.

$ metapredict-predict-disorder <Path to .fasta file> <Path where to save the output> <Output file name>

This will save a .csv file to the location specified by Path where to save the output. The name specified in Output file name will be the name of the output file followed by .csv. The .csv extension is automatically added to the output file name.

Example

$ metapredict-predict-disorder /Users/thisUser/Desktop/interestingProteins.fasta /Users/thisUser/Desktop/DisorderPredictions/ myCoolPredictions

Additional Usage

Get raw prediction values - By default, the output prediction values are normalized between 0 and 1. However, some of the raw values from the predictor are slightly less than 0 or slightly greater than 1. The negative values are replaced with 0 and the values greater than 1 are replaced with 1 by default. However, if you want raw values, simply add the flag --no_normalization. There is not a very good reason to do this, and it is generally not recommended. However, I wanted to give users the maximum amount of flexibility when using metapredict, so I made it an option.

Example

$ metapredict-predict-disorder /Users/thisUser/Desktop/interestingProteins.fasta /Users/thisUser/Desktop/DisorderPredictions/ myCoolPredictions --no_normalization

Graphing Disorder

The metapredict-graph-disorder command from the command line takes a .fasta file as input and returns a .png for every sequence within the .fasta file. The .png file for each sequence will be saved to wherever the user specifies as the output location. Each file will be named as predicted_disorder_ followed by the first 10 characters of the .fasta header (which is typically the unique identifier for the protein). For example, a fasta header of >sp|Q8N6T3|ARFG1_HUMAN will return a file saved as predicted_disorder_sp|Q8N6T3|.png. Additionally, the title of each graph is automatically generated and will have the title Predicted Consensus Disorder followed by the first 10 characters of the .fasta header. In the previous example, the graph would be titled Predicted Consensus Disorder sp|Q8N6T3|.

$ metapredict-graph-disorder <Path to .fasta file> <Path where to save the output>

Example

$ metapredict-graph-disorder /Users/thisUser/Desktop/interestingProteins.fasta /Users/thisUser/Desktop/DisorderGraphsFolder/

WARNING - This command will generate a .png file for every sequence in the .fasta file. If you have 1,000 sequences in a .fasta file, it will generate 1,000 files. Therefore, I recommend saving the output to a dedicated folder (or at least not your Desktop...).

Additional Usage

Changing resolution of saved graphs - By default, the output graphs have a DPI of 150. However, the user can change the DPI of the output (higher values have greater resolution but take up more space). To change the DPI simply add the flag -D or -dpi followed by the wanted DPI value.

Example

$ metapredict-graph-disorder /Users/thisUser/Desktop/interestingProteins.fasta /Users/thisUser/Desktop/DisorderGraphsFolder/ -D 300

Specify the lines across a graph: -lines / --line_intervals

By default, the graphs have horizontal dashed lines at intervals of 0.2 from 0 to 1. Now, can specify the location of the dashed lines by using the -lines / --line_intervals argument

$ metapredict-graph-disorder /Users/thisUser/Desktop/interestingProteins.fasta /Users/thisUser/Desktop/DisorderGraphsFolder/ -lines 0.1 0.2 0.3 0.4 0.5

Remove non-alphabetic characters from file names - By default, the output files contain characters that are non-alphabetic (for example, predicted_disorder_sp|Q8N6T3|.png). This is not a problem on some operating systems, but others do not allow files to have names that contain certain characters. To get around this, you can add the --remove_characters flag. This will remove all non-alphabetic characters from the .fasta header when saving the file. The previous example with the header >sp|Q8N6T3|ARFG1_HUMAN would now save as predicted_disorder_spQ8N726AR.png.

Example

$ metapredict-graph-disorder /Users/thisUser/Desktop/interestingProteins.fasta /Users/thisUser/Desktop/DisorderGraphsFolder/ --remove_characters

Using metapredict in Python:

In addition to using metapredict from the command line, you can also use metapredict directly in Python.

First import metapredict -

import metapredict
from metapredict import meta

Once metapredict is imported you can work with individual sequences or .fasta files.

Predicting Disorder

The predict_disorder function will return a list of predicted disorder value for each residue of the input sequence. The input sequence should be a string. Running -

meta.predict_disorder("DSSPEAPAEPPKDVPHDWLYSYVFLTHHPADFLR")

would output -

[1, 1, 1, 1, 1, 1, 1, 0.958249, 0.915786, 0.845275, 0.75202, 0.687313, 0.588148, 0.603413, 0.506673, 0.476576, 0.407988, 0.432979, 0.286987, 0.160754, 0.102596, 0.094578, 0.073396, 0.140863, 0.27831, 0.327464, 0.336405, 0.351597, 0.356424, 0.354656, 0.379971, 0.351955, 0.456596, 0.365483]

By default, output prediction values are normalized between 0 and 1. However, some of the raw values from the predictor are slightly less than 0 or slightly greater than 1. The negative values are simply replaced with 0 and the values greater than 1 are replaced with 1 by default. However, the user can get the raw prediction values by specifying normalized=False as a second argument in meta.predict_disorder. There is not a very good reason to do this, and it is generally not recommended. However, we wanted to give users the maximum amount of flexibility when using metapredict, so we made it an option.

meta.predict_disorder("DAPTSQEHTQAEDKERDSKTHPQKKQSPS", normalized=False)

Graphing Disorder

The graph_disorder function will show a plot of the predicted disorder consensus values across the input amino acid sequence.

meta.graph_disorder("DAPTSQEHTQAEDKERDSKTHPQKKQSPS")

Additional Usage

Changing the title of the generated graph - There are two parameters that the user can change for graph_disorder. The first is the name of the title for the generated graph. The name by default is blank and the title of the graph is simply Predicted Consensus Disorder. However, the name can be specified in order to add the name of the protein after the default title. For example, specifing name = "- PAB1" would result in a title of Predicted Consensus Disorder - PAB1.

Example

meta.graph_disorder("DAPPTSQEHTQAEDKERD", name="Name of this nonexistant protein")

Changing the resolution of the generated graph - By default, the output graph has a DPI of 150. However, the user can change the DPI of the generated graph (higher values have greater resolution). To do so, simply specify DPI="Number" where the number is an integer.

Example

meta.graph_disorder("DAPPTSQEHTQAEDKERD", DPI=300)

Specify the lines across a graph - By default, the graphs have horizontal dashed lines at intervals of 0.2 from 0 to 1. Now, can specify the location of the dashed lines by using specifying line_intervals

Example

meta.graph_disorder("DAPPTSQEHTQAEDKERD", line_intervals = [0.1, 0.2, 0.3]

Calculating Percent Disorder

The percent_disorder function will return the percent of residues in a sequence that have predicted consensus disorder values of 50% or more (as a decimal value).

Example

meta.percent_disorder("DAPPTSQEHTQAEDKERD")

By default, this function uses a cutoff value of equal to or greater than 0.5 for a residue to be considered disordered.

Additional Usage

Changing the cutoff value - If you want to be more strict in what you consider to be disordered for calculating percent disorder of an input sequence, you can simply specify the cutoff value by adding the argument cutoff=decimal where the decimal corresponds to the percent you would like to use as the cutoff (for example, 0.8 would be 80%).

Example

meta.percent_disorder("DAPPTSQEHTQAEDKERD", cutoff=0.8)

The higher the cutoff value, the higher the value any given predicted residue must be greater than or equal to in order to be considered disordered when calculating the final percent disorder for the input sequence.

Predicting Disorder From a .fasta File

By using the predict_disorder_fasta function, you can predict disorder values for the amino acid sequences in a .fasta file. By default, this function will return a dictionary where the keys in the dictionary are the fasta headers and the values are the consensus disorder predictions of the amino acid sequence associated with each fasta header in the original .fasta file.

Example

meta.predict_disorder_fasta("file path to .fasta file/fileName.fasta")

An actual filepath would look something like:

meta.predict_disorder_fasta("/Users/thisUser/Desktop/coolSequences.fasta")

Additional Usage

Save the output values - By default the predict_disorder_fasta function will immediately return a dictionary. However, you can also save the output to a .csv file by specifying save=True and output_path = "location you want to save the file to". This will save a file called predicted_disorder_values.csv to the location you specify for the output_path. The first cell of each row will contain a fasta header and the subsequent cells in that row will contain predicted consensus disorder values for the protein associated with the fasta header.

Example

meta.predict_disorder_fasta("file path to .fasta file/fileName.fasta", save=True, output_path="file path where the output .csv should be saved")

An actual filepath would look something like:

meta.predict_disorder_fasta("/Users/thisUser/Desktop/coolSequences.fasta", save=True, output_path="/Users/thisUser/Desktop/")

Specifying the name of the output file - By default, the generated .csv file will save as predicted_disorder_values.csv. However, you can change the default by specifing output_name="file_name".

Example

meta.predict_disorder_fasta("file path to .fasta file/fileName.fasta", save=True, output_path="file path where the output .csv should be saved", output_name="name of file")

An actual filepath would look something like:

meta.predict_disorder_fasta("/Users/thisUser/Desktop/coolSequences.fasta", save=True output_path"/Users/thisUser/Desktop/", output_name="my_predictions")

Importantly, you do not need to add the .csv file extension to your file name specified in output_name. However, if you do specify .csv as a file extension, everything should still work.

Get raw prediction values - By default, this function will output prediction values that are normalized between 0 and 1. However, some of the raw values from the predictor are slightly less than 0 or slightly greater than 1. The negative values are simply replaced with 0 and the values greater than 1 are replaced with 1 by default. If you want the raw values simply specify normalized=False. There is not a very good reason to do this, and it is generally not recommended. However, we wanted to give users the maximum amount of flexibility when using metapredict, so we made it an option.

Example

meta.predict_disorder_fasta("/Users/thisUser/Desktop/coolSequences.fasta", normalized=False)

Generating Graphs From a .fasta File

By using the graph_disorder_fasta function, you can graph predicted consensus disorder values for the amino acid sequences in a .fasta file. The graph_disorder_fasta function takes a .fasta file as input and returns a .png for every sequence within the .fasta file. The .png files for each sequence will be saved to wherever the user specifies as the output location. Each file will be named as predicted_disorder_ followed by the first 10 characters of the .fasta header (which is typically the unique identifier for the protein). For example, a fasta header of >sp|Q8N6T3|ARFG1_HUMAN will return a file saved as predicted_disorder_sp|Q8N6T3|.png. Additionally, the title of each graph is automatically generated and will have the title Predicted Consensus Disorder followed by the first 10 characters of the .fasta header. In the previous example, the graph would be titled Predicted Consensus Disorder sp|Q8N6T3|.

WARNING - This command will generate a .png file for every sequence in the .fasta file. If you have 1,000 sequences in a .fasta file, it will generate 1,000 files. Therefore, I recommend saving the output to a dedicated folder (or at least not your Desktop...).

Example

meta.graph_disorder_fasta("file path to .fasta file/fileName.fasta", output_path="file path of where to save output graphs")

An actual filepath would look something like:

meta.graph_disorder_fasta("/Users/thisUser/Desktop/coolSequences.fasta", output_path="/Users/thisUser/Desktop/folderForGraphs")

Additional Usage

Changing resolution of saved graphs - By default, the output files have a DPI of 150. However, the user can change the DPI of the output files (higher values have greater resolution but take up more space). To change the DPI, specify DPI=Number where Number is an integer.

Example

meta.graph_disorder_fasta("/Users/thisUser/Desktop/coolSequences.fasta", DPI=300, output_path="/Users/thisUser/Desktop/folderForGraphs")

Remove non-alphabetic characters from file name - By default, the output files contain characters that are non-alphabetic (for example, predicted_disorder_sp|Q8N6T3|.png). This is not a problem on some operating systems, while others do not allow files to have names that contain certain characters. To get around this, you can add an additional argument remove_characters=True. This will remove all non-alphabetic characters from the .fasta header when saving the file. The previous example with the header >sp|Q8N6T3|ARFG1_HUMAN would now save as predicted_disorder_spQ8N726AR.png.

Example

meta.graph_disorder_fasta("/Users/thisUser/Desktop/coolSequences.fasta", DPI=300, output_path="/Users/thisUser/Desktop/folderForGraphs", remove_characters=True)

Viewing generated graphs without saving - The default behavior for the graph_disorder_fasta function is to save the generated graphs for viewing elsewhere. However, the user can choose to view the generated graphs without saving them by specifying save=False.

WARNING If you choose to view the generated graphs instead of saving them, you can only view one at a time and each must be closed before the next will open. This is not a problem if you only have around 10 sequences in your .fasta file. However, if you have 1,000 sequences in a .fasta file, you will have to close out 1,000 graphs. This isn't a problem if you don't mind clicking... a lot.

Example

meta.graph_disorder_fasta("/Users/thisUser/Desktop/coolSequences.fasta", save=False)

metapredict isn't working!

I have recieved occassional feedback that metapredict is not working for a user. A common problem is that the user is using a different version of Python than metapredict was made on. metapredict was made using Python version 3.7, and I recommend using this version while using metapredict to avoid problems (I haven't done extensive testing using other versions of Python, so if you're not using 3.7, do so at your own risk). A convenient workaround is to use a conda environment that has Python 3.7 set as the default version of Python. For more info on conda, please see https://docs.conda.io/projects/conda/en/latest/index.html

Once you have conda installed, simply use the command

conda create --name my_env python=3.7

where you can replace the name of your environment with whatever you'd like. Then, use metapredict from within this conda environment.

If you are having other problems, please report them to the issues section on the metapredict Github page at https://github.com/idptools/metapredict/issues

Recent changes

This section is a log of recent changes with metapredict. My hope is that as I change things, this section can help you figure out why a change was made and if it will break any of your current work flows. The first major changes were made for the 0.56 release, so tracking will start there. Reasons are not provided for bug fixes for because the reason can assumed to be fixing the bug...

V0.60

Change: Added functionality to specify the horizontal lines that appear across the graphs rather than only having the option of having the dashed lines appear at intervals of 0.2. This functionality is in both Python and the command line.

V0.58

Change: Updated the network with a newly trained network (using the same dataset as the original) that is slightly more accurate.

Reason: I am always trying to find ways to make metapredict more accurate. When I manage to make the predictor better, I will update it.

V0.57

Change: Bug fix that could result in prediction values to six decimal places in some scenarios

Change: Changed titles for graphs generated by metapredict-graph-disorder to be 14 characters instead of 10. This is reflected in the title graph and the saved files.

Reason: The 10 character save file was occassionally the same for multiple proteins. This resulted in the inability to discern which protein corresponded to which graph and could result in overwriting previously generated graphs. The 14 characters should be long enough to keep unique names for all proteins being analyzed.

Change: Fixed bug that could result in crashing due to short fasta headers.

V0.56

Change: Number of decimals in predictions was reduced from 6 to 3.

Reason: It is not necessary to have accuracy out to 6 decimal places.

Change: Added functionality to use . to specify current directory from command line.

Reason: Improve functionality.

Change: -DPI flag changed to -dpi in command line graphing function

Reason: It was annoying to have to do all caps for this flag.

Change: The predict-disorder command is now metapredict-predict-disorder and the graph-disorder command is now metapredict-graph-disorder

Reason: This will help users be able to use auto complete functionality from the command line using tab to pull up the graph or predict disorder commands while only having to remember metapredict.

Change: The output for .csv files will now have a comma space between each value instead of just a comma.

Reason: Improve readability.

Copyright

Copyright (c) 2020, Holehouse Lab - WUSM

Acknowledgements

IDP-Parrot, created by Dan Griffith, was used to generate the network used for metapredict. See https://pypi.org/project/idptools-parrot/ for some very cool machine learning stuff.

In addition to using Dan Griffith's tool for creating metapredict, the code for brnn_architecture.py and encode_sequence.py was written by Dan (originally for idp-parrot).

I would also like to thank the team at MobiDB for creating the database that was used to train this predictor. Check out their awesome stuff at https://mobidb.bio.unipd.it

Project based on the Computational Molecular Science Python Cookiecutter version 1.3.