The text below is older text which is redundant and has been removed from the main readme.md.
Important update - as of February 15, 2022 we have updated metapredict to V2.0. This comes with important changes that improve the accuracy of metapredict. Please see the section on the update Major update to metapredict predictions to increase overall accuracy below. In addition, this update changes the functionality of the predict_disorder_domains() function, so please read the documentation on that function if you were using it previously.
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. In addition, as of version 2, metapredict incorporates an additional layer of predictions by counter-selecting based on structure predictions from AlphaFold2. The creation of metapredict was made possible by parrot.
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 approximation of what other predictors would predict (things got pretty 'meta' there, hence the name metapredict).
We are always working to make metapredict better, and we have recently managed just that. More details will be below, but the short story is that we have made significant improvements in the accuracy of disorder predictions using metapredict. By analyzing our new network using the Disprot-PDB dataset predictions, we found that the MCC (which is a measurement accounting for false positives, false negatives, true positives, and true negatives) for metapredict increased from 0.588 for the old (original) network to 0.7 for our new network. To put this in perspective, our original network was ranked 12th most accurate when analyzing the Disprot-PDB dataset, and it is now ranked as the 2nd most accurate available predictor. We should also note that we are still trying a few 'tweaks' to this new network and plan to updated it if we can improve accuracy any further We will be publishing the updated benchmarks for the 'new metapredict' in the near future (unless we already have but forgot to take this sentence out of the documentation...).
No worries! We left users access to the old network. The default network is now our new, more accurate network. However, by calling -l or --legacy from the command line or by specifying legacy=True from Python, you will be able to use the original metapredict network. We wanted to keep making metapredict better, but we also wanted to minimize disruptions to anyone currently relying on the original metapredict predictions for whatever reason.
We didn't think it was possible, but metapredict has somehow become even more meta. Get ready, because things are about to get a little weird. When we implemented the AlphaFold2 pLDDT prediction feature (see section below), we noticed that there were occasional discrepancies between metapredict and the predicted pLDDT (ppLDDT) scores. When the ppLDDT scores get high enough, it is unlikely that a given region is actually disordered. So, we developed a version of metapredict that we originally called 'metapredict-hybrid' that essentially combined aspects of the ppLDDT scores and the original metapredict scores. We found that this 'hybrid predictor' was much better than the original metapredict disorder predictor at predicting disordered regions. But we didn't stop there. We think one of metapredicts best features is it is really really fast. This 'hybrid-predictor' was a little on the slow side, coming in at about 1/3 the speed of the original metapredict predictor. This is still VERY fast, but we thought we could do better. So, we took a little over 300,000 protein sequences and generated metapredict-hybrid scores for those sequences. We then fed those sequences and the corresponding metapredict-hybrid scores and generated a new bidirectional recurrent neural network (BRNN) using PARROT. We then tested this new network against the original metapredict-hybrid predictions and the original metapredict network. The new network that was trained on metapredict-hybrid scores actually outperformed the metapredict-hybrid predictions when benchmarking against Disprot-PDB. Importantly, this new (super accurate) network was only 30% slower than the original metapredict network, which is substantially better than the 70% hit that metapredict-hybrid took.
TL;DR We made the original metapredict predictor using a network trained on consensus scores from MobiDB. We then trained a network on AlphaFold2 pLDDT scores. Next, we made a predictor that combined prediction values from the original metapredict predictor and the AlphaFold2 pLDDT predictor to make very accurate disorder predictions. Finally, we took hundreds of thousands of proteins, generated disorder prediction scores using the aforementioned combination of the original metapredict predictions and the AlphaFold2 predictions, and then trained our final network on those scores. That's pretty dang meta.
In addition, metapredict offers predicted pLDDT confidence scores from AlphaFold2. These predicted scores use a bidirectional recurrent neural network (BRNN) trained on the per residue pLDDT (predicted IDDT-Ca) confidence scores generated by AlphaFold2 (AF2). The confidence scores (pLDDT) from the proteomes of Danio rerio, Candida albicans, Mus musculus, Escherichia coli, Drosophila melanogaster, Methanocaldococcus jannaschii, Plasmodium falciparum, Mycobacterium tuberculosis, Caenorhabditis elegans, Dictyostelium discoideum, Trypanosoma cruzi, Saccharomyces cerevisiae, Schizosaccharomyces pombe, Rattus norvegicus, Homo sapiens, Arabidopsis thaliana, Zea mays, Leishmania infantum, Staphylococcus aureus, Glycine max, Oryza sativa were used to generate the BRNN. These pLDDT scores measure the local confidence that AlphaFold2 has in its predicted structure. The scores go from 0-100 where 0 represents low confidence and 100 represents high confidence. For more information, please see: Highly accurate protein structure prediction with AlphaFold https://doi.org/10.1038/s41586-021-03819-2. In describing these scores, the team states that regions with pLDDT scores of less than 50 should not be interpreted except as possible disordered regions.
These scores can be used for many applications such as generating a quick preview of which regions of your protein of interest AF2 might be able to predict with high confidence, or which regions of your protein might be disordered. AF2 is not (strictly speaking) a disorder predictor, and the pLDDT scores are not directly representative of protein disorder. Therefore, any conclusions drawn with regards to disorder from predicted AF2 pLDDT scores should be interpreted with care, but they may be able to provide an additional metric to assess the likelihood that any given protein region may be disordered.
A major drawback of consensus disorder databases is that they can only give you values of previously predicted protein sequences. Therefore, if your sequence of interest is not in their database, tough luck. In addition, installing multiple different predictors to generate consensus scores locally is computationally expensive, time consuming, and in some cases simply not possible. Fortunately, metapredict gives you a way around this problem!
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. This gives maximum flexibility so the user can easily predict/graph disorder from a single sequence or for an entire proteome.
For full documentation, please see: https://metapredict.readthedocs.io/en/latest/getting_started.html
For disorder predictions using our server, please see: https://metapredict.net
The most recent update to metapredict on March 15, 2023 introduced multiprocessing support to the command-line interface (you're welcome, Jeff). This is only implemented for the CLI at this time. We were able to predict disorder in the entire human proteome in about 4 minutes with metapredict V2 and in about 2 minutes using legacy metapredict! We didn't even have to close the 40 chrome tabs we definitely had to have open for testing. If there is additional interest in speeding up other functionality by using multiple cores, let us know!
If you use metapredict for your work, please cite the metapredict paper -
Emenecker RJ, Griffith D, Holehouse AS, metapredict: a fast, accurate, and easy-to-use predictor of consensus disorder and structure, Biophysical Journal (2021), doi: https:// doi.org/10.1016/j.bpj.2021.08.039.
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.
There are two ways you can use metapredict:
- Directly from the command-line
- From within Python
Note for any commands from the command-line, if you need to use the original metapredict predictor as opposed to our new, updated predictor, use the -l or --legacy flag!
The metapredict-predict-disorder command from the command line takes a .fasta file as input and returns disorder scores for the sequences in the FASTA file.
$ metapredict-predict-disorder <Path to .fasta file>
Example
$ metapredict-predict-disorder /Users/thisUser/Desktop/interestingProteins.fasta
Additional Usage
specifying where to save the output -
If you would like to specify where to save the output, simply use the -o or --output-file flag and then specify the file path and file name. By default this command will save the output file as disorder_scores.csv to your current working directory. However, you can specify the file name in the output path.
Example
$ metapredict-predict-disorder /Users/thisUser/Desktop/interestingProteins.fasta -o /Users/thisUser/Desktop/disorder_predictions/my_disorder_predictions.csv
Using the original metapredict predictor
To use the original metapredict predictor as opposed to our new, updated predictor, use the -l or --legacy flag!
metapredict-quick-predict is a command that will let you input a sequence and get disorder values immediately printed to the terminal. The only argument that can be input is the sequence.
Example:
$ metapredict-quick-predict ISQQMQAQPAMVKSQQQQQQQQQQHQHQQQQLQQQQQLQMSQQQVQQQGIYNNGTIAVAN
Using the original metapredict predictor
To use the original metapredict predictor as opposed to our new, updated predictor, use the -l or --legacy flag!
The metapredict-predict-pLDDT command from the command line takes a .fasta file as input and returns predicted AlphaFold2 pLDDT scores for the sequences in the FASTA file.
$ metapredict-predict-pLDDT <Path to .fasta file>
Example
$ metapredict-predict-pLDDT /Users/thisUser/Desktop/interestingProteins.fasta
Additional Usage
Specify where to save the output -
If you would like to specify where to save the output, simply use the -o or --output-file flag and then specify the file path. By default this command will save the output file as pLDDT_scores.csv to your current working directory. However, you can specify the file name in the output path.
Example
$ metapredict-predict-pLDDT /Users/thisUser/Desktop/interestingProteins.fasta -o /Users/thisUser/Desktop/disorder_predictions/my_pLDDT_predictions.csv
The metapredict-graph-disorder command from the command line takes a .fasta file as input and returns a graph for every sequence within the .fasta file. Warning This will return a graph for every sequence in the FASTA file.
$ metapredict-graph-disorder <Path to .fasta file>
Example
$ metapredict-graph-disorder /Users/thisUser/Desktop/interestingProteins.fasta
If no output directory is specified, this function will make an output directory in the current working directory called disorder_out. This directory will hold all generated graphs.
Additional Usage
Adding AlphaFold2 Confidence Scores -
To add predicted AlphaFold2 pLDDT scores, simply use the -p or --pLDDT flag.
Example
$ metapredict-graph-disorder /Users/thisUser/Desktop/interestingProteins.fasta -p
Specifying where to save the output -
To specify where to dave the output, simply use the -o or --output-directory flag.
Example
$ metapredict-graph-disorder /Users/thisUser/Desktop/interestingProteins.fasta -o /Users/thisUser/Desktop/FolderForCoolPredictions
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 -o /Users/thisUser/Desktop/DisorderGraphsFolder/ -D 300
Changing the file type -
By default the graphs will save as .png files. However, you can specify the file type by calling --filetype and then specifying the file type. Any matplotlib compatible file extension should work (for example, pdf).
Example
$ metapredict-graph-disorder /Users/thisUser/Desktop/interestingProteins.fasta -o /Users/thisUser/Desktop/DisorderGraphsFolder/ --filetype pdf
Indexing file names -
If you would like to index the file names with a leading unique integer starting at 1, use the --indexed-filenames flag.
Example
$ metapredict-graph-disorder /Users/thisUser/Desktop/interestingProteins.fasta -o /Users/thisUser/Desktop/DisorderGraphsFolder/ --indexed-filenames
Changing the disorder threshhold line on the graph -
If you would like to change the disorder threshold line plotted on the graph, use the --disorder-threshold flag followed by some value between 0 and 1. Default is 0.3.
Example
$ metapredict-graph-disorder /Users/thisUser/Desktop/interestingProteins.fasta -o /Users/thisUser/Desktop/DisorderGraphsFolder/ --disorder-threshold 0.5
Using the original metapredict predictor
To use the original metapredict predictor as opposed to our new, updated predictor, use the -l or --legacy flag!
metapredict-quick-graph is a command that will let you input a sequence and get a plot of the disorder back immediately. You cannot input fasta files for this command. The command only takes three arguments, 1. the sequence 2. optional DPI -D or --dpi of the output graph which defaults to 150 DPI, and 3. optional to include predicted AlphaFold2 condience scores, use the -p or --pLDDT flag.
Example:
$ metapredict-quick-graph ISQQMQAQPAMVKSQQQQQQQQQQHQHQQQQLQQQQQLQMSQQQVQQQGIYNNGTIAVAN
Example:
$ metapredict-quick-graph ISQQMQAQPAMVKSQQQQQQQQQQHQHQQQQLQQQQQLQMSQQQVQQQGIYNNGTIAVAN -D 200
Example:
$ metapredict-quick-graph ISQQMQAQPAMVKSQQQQQQQQQQHQHQQQQLQQQQQLQMSQQQVQQQGIYNNGTIAVAN -D 200 -p
metapredict-uniprot is a command that will let you input any Uniprot ID and get a plot of the disorder for the corresponding protein. The default behavior is to have a plot automatically appear. Apart from the Uniprot ID which is required for this command, the command has four possible additional optional arguments, 1. To include predicted AlphaFold2 2 pLDDT confidence scores, use the -p or --pLDDT flag. DPI can be changed with the -D or --dpi flags, default is 150 DPI, 3. Using -o or --output-file will save the plot to a specified directory (default is current directory) - filenames and file extensions (pdf, jpg, png, etc) can be specified here. If there is no file name specified, it will save as the Uniprot ID and as a .png, 4. -t or --title will let you specify the title of the plot. By default the title will be Disorder for followed by the Uniprot ID.
Example:
$ metapredict-uniprot Q8RYC8
Example:
$ metapredict-uniprot Q8RYC8 -p
Example:
$ metapredict-uniprot Q8RYC8 -D 300
Example:
$ metapredict-uniprot Q8RYC8 -o /Users/ThisUser/Desktop/MyFolder/DisorderGraphs
Example:
$ metapredict-uniprot Q8RYC8 -o /Users/ThisUser/Desktop/MyFolder/DisorderGraphs/my_graph.png
Example:
$ metapredict-uniprot Q8RYC8 -t ARF19
Using the original metapredict predictor
To use the original metapredict predictor as opposed to our new, updated predictor, use the -l or --legacy flag!
Sometimes you just don't know the Uniprot ID for your favorite protein, and looking it up can be a pain. With the metapredict-name command, you can input the common name of your favorite protein and get a graph in return. Metapredict will also print the name of the organisms and the uniprot ID it found so you know you're looking at the correct protein. This is because this functionality queries your input protein name on Uniprot and takes the top hit. Sometimes this is the protein you're looking for, but not always. To increase the likelihood of success, use your protein name and the organism name for this command.
Example
$ metapredict-name p53
will graph the metapredict disorder scores for the Homo sapiens p53 protein. This is because Homo sapiens p53 is the top hit on Uniprot when you search p53. However...
$ metapredict-name p53 chicken
will graph the p53 from Gallus gallus!
Additional Usage
Changing the DPI
Changing the DPI will adjust the resolution of the graph. To change the DPI, use the -D or --dpi flag.
Example
$ metapredict-name p53 -D 300
Graphing predicted pLDDT scores
To add predicted pLDDT scores to the graph, use the -p or --pLDDT flag.
Example
$ metapredict-name p53 -p
Changing the title
To change the title, use the -t or --title flag.
Example
$ metapredict-name p53 -t my_cool_graph_of_p53
Using the legacy version of metapredict
To use the legacy version of metapredict for your disorder scores, use the -l or --legacy flag.
Example
$ metapredict-name p53 -l
Printing the full Uniprot ID to your terminal
To have your terminal print the entire Uniprot ID as well as the full protein sequence from your specified protein upon graphing, use the -v or --verbose flag.
Example
$ metapredict-name p53 -v
Turning off all printing to the terminal
By default, the metapredict-name command prints the uniprot ID as well as other information related to your protein to the terminal. The purpose of this is to make it explicitly clear which protein was graphed because grabbing the top hit from Uniprot does not gaurentee that it is the protein you want or expected. However, this behavior can be turned off by using the -s or --silent flag.
Example
$ metapredict-name p53 -s
The metapredict-graph-pLDDT command from the command line takes a .fasta file as input and returns a graph of the predicted AlphaFold2 pLDDT confidence score for every sequence within the .fasta file. Warning This will return a graph for every sequence in the FASTA file.
$ metapredict-graph-pLDDT <Path to .fasta file>
Example
$ metapredict-graph-pLDDT /Users/thisUser/Desktop/interestingProteins.fasta
If no output directory is specified, this function will make an output directory in the current working directory called pLDDT_out. This directory will hold all generated graphs.
Additional Usage
Specifying where to save the output -
To specify where to dave the output, simply use the -o or --output-directory flag.
Example
$ metapredict-graph-pLDDT /Users/thisUser/Desktop/interestingProteins.fasta -o /Users/thisUser/Desktop/FolderForCoolPredictions
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-pLDDT /Users/thisUser/Desktop/interestingProteins.fasta -o /Users/thisUser/Desktop/pLLDTGraphsFolder/ -D 300
Changing the file type -
By default the graphs will save as .png files. However, you can specify the file type by calling --filetype and then specifying the file type. Any matplotlib compatible file extension should work (for example, pdf).
Example
$ metapredict-graph-pLDDT /Users/thisUser/Desktop/interestingProteins.fasta -o /Users/thisUser/Desktop/pLDDTGraphsFolder/ --filetype pdf
Indexing file names -
If you would like to index the file names with a leading unique integer starting at 1, use the --indexed-filenames flag.
Example
$ metapredict-graph-pLDDT /Users/thisUser/Desktop/interestingProteins.fasta -o /Users/thisUser/Desktop/pLDDTGraphsFolder/ --indexed-filenames
The metapredict-predict-idrs command from the command line takes a .fasta file as input and returns a .fasta file containing the IDRs for every sequence from the input .fasta file.
$ metapredict-predict-idrs <Path to .fasta file>
Example
$ metapredict-predict-idrs /Users/thisUser/Desktop/interestingProteins.fasta
Additional Usage
specifying where to save the output -
If you would like to specify where to save the output, simply use the -o or --output-file flag and then specify the file path and file name.
Example
$ metapredict-predict-idrs /Users/thisUser/Desktop/interestingProteins.fasta -o /Users/thisUser/Desktop/disorder_predictions/my_idrs.fasta
Using the original metapredict predictor
To use the original metapredict predictor as opposed to our new, updated predictor, use the -l or --legacy flag!
Example
$ metapredict-predict-idrs /Users/thisUser/Desktop/interestingProteins.fasta -o /Users/thisUser/Desktop/disorder_predictions/my_idrs.fasta -l
Changing output threshold for disorder-
To change the cutoff value for something to be considered disordered, simply use the --threshold flag and then specify your value. For legacy, the default is 0.42. For the new version of metapredict, the value is 0.5.
Example
$ metapredict-predict-idrs /Users/thisUser/Desktop/interestingProteins.fasta -o /Users/thisUser/Desktop/disorder_predictions/my_idrs.fasta --threshold 0.3
In addition to using metapredict from the command line, you can also use metapredict directly in Python.
First import metapredict -
import metapredict as meta
Once metapredict is imported you can work with individual sequences or .fasta files.
The predict_disorder function will return a list of predicted disorder values 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, 0.957, 0.934, 0.964, 0.891, 0.863, 0.855, 0.793, 0.719, 0.665, 0.638, 0.576, 0.536, 0.496, 0.482, 0.306, 0.152, 0.096, 0.088, 0.049, 0.097, 0.235, 0.317, 0.341, 0.377, 0.388, 0.412, 0.46, 0.47, 0.545, 0.428]
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)
NOTE - using the original metapredict predictor
To use the original metapredict predictor as opposed to our new, updated predictor, set legacy=True
meta.predict_disorder("DAPTSQEHTQAEDKERDSKTHPQKKQSPS", legacy=True)
The predict_pLDDT function will return a list of predicted AlphaFold2 pLDDT confidence scores for each residue of the input sequence. The input sequence should be a string. Running -
meta.predict_pLDDT("DAPPTSQEHTQAEDKERD")
would output -
[35.7925, 40.4579, 46.3753, 46.2976, 42.3189, 42.0248, 43.5976, 40.7481, 40.1676, 41.9618, 43.3977, 43.938, 41.8352, 44.0462, 44.5382, 46.3081, 49.2345, 46.0671]
The predict_disorder_domains() function takes in an amino acid sequence and returns a DisorderObject. The DisorderObject has 6 dot variables that can be called to get information about your input sequence. They are as follows:
.sequence : str
Amino acid sequence
.disorder : list or np.ndarray 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
Examples
seq = meta.predict_disorder_domains("MKAPSNGFLPSSNEGEKKPINSQLWHACAGPLVSLPPVGSLVVYFPQGHSEQVAASMQKQTDFIPNYPNLPSKLICLLHS")
Now we can call the various dot values for seq.
Getting the sequence
print(seq.sequence)
returns
MKAPSNGFLPSSNEGEKKPINSQLWHACAGPLVSLPPVGSLVVYFPQGHSEQVAASMQKQTDFIPNYPNLPSKLICLLHS
Getting the disorder scores
print(seq.disorder)
returns
[0.922 0.9223 0.9246 0.9047 0.8916 0.8956 0.8931 0.883 0.8613 0.8573
0.852 0.8582 0.8614 0.8455 0.826 0.7974 0.7616 0.7248 0.6782 0.6375
0.5886 0.5476 0.5094 0.4774 0.4472 0.4318 0.4266 0.4222 0.3953 0.3993
0.3904 0.4004 0.3962 0.3721 0.3855 0.3582 0.3456 0.3682 0.3488 0.3274
0.3258 0.2937 0.2864 0.3004 0.3358 0.3815 0.4397 0.4594 0.4673 0.4535
0.4446 0.4481 0.4546 0.4454 0.4549 0.4564 0.4677 0.4539 0.4713 0.49
0.4934 0.4835 0.4815 0.4692 0.4548 0.4856 0.495 0.4809 0.502 0.4944
0.4612 0.4561 0.436 0.4203 0.3784 0.3624 0.3739 0.3983 0.4348 0.4369]
Getting the disorder domain boundaries
print(seq.disordered_domain_boundaries)
returns
[[0, 23]]
Where each nested list is the boundaries for a specific disordered region and the first element in each list is the start of that region and the second element is the end of that region.
Getting the folded domain boundaries
print(seq.folded_domain_boundaries)
returns
[[23, 80]]
Where each nested list is the boundaries for a specific folded region and the first element in each list is the start of that region and the second element is the end of that region.
Getting the disordered domain sequences
print(seq.disordered_domains)
returns
['MKAPSNGFLPSSNEGEKKPINSQ']
Where each element in the list is a specific disordered region identified in the sequence.
Getting the folded domain sequences
print(seq.folded_domains)
returns
['LWHACAGPLVSLPPVGSLVVYFPQGHSEQVAASMQKQTDFIPNYPNLPSKLICLLHS']
Where each element in the list is a specific folded region identified in the sequence.
Additional Usage
Altering the disorder theshhold - To alter the disorder threshold, simply set disorder_threshold=my_value where my_value is a float. The higher the threshold value, the more conservative metapredict will be for designating a region as disordered. Default = 0.42
Example
meta.predict_disorder_domains("MKAPSNGFLPSSNEGEKKPINSQLWHACAGPLV", disorder_threshold=0.3)
Altering minimum IDR size - The minimum IDR size will define the smallest possible region that could be considered an IDR. In other words, you will not be able to get back an IDR smaller than the defined size. Default is 12.
Example
meta.predict_disorder_domains("MKAPSNGFLPSSNEGEKKPINSQLWHACAGPLV", minimum_IDR_size = 10)
Altering the minimum folded domain size - The minimum folded domain size defines where we expect the limit of small folded domains to be. NOTE this is not a hard limit and functions more to modulate the removal of large gaps. In other words, gaps less than this size are treated less strictly. Note that, in addition, gaps < 35 are evaluated with a threshold of 0.35 x disorder_threshold and gaps < 20 are evaluated with a threshold of 0.25 x disorder_threshold. These two lengths were decided based on the fact that coiled-coiled regions (which are IDRs in isolation) often show up with reduced apparent disorder within IDRs 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.
Example
meta.predict_disorder_domains("MKAPSNGFLPSSNEGEKKPINSQLWHACAGPLV", minimum_folded_domain = 60)
Altering gap_closure - The gap closure defines the largest gap that would be closed. Gaps here refer to a scenario in which you have two groups of disordered residues separated by a 'gap' of not disordered residues. In general large gap sizes will favor larger contiguous 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.
Example
meta.predict_disorder_domains("MKAPSNGFLPSSNEGEKKPINSQLWHACAGPLV", gap_closure = 5)
Using the original metapredict network-
To use the original metapredict network, simply set legacy=True.
Example:
predict_disorder_domains("MKAPSNGFLPSSNEGEKKPINSQLWHACAGPLV", legacy=True)
The predict_disorder_domains function takes in an amino acid sequence and returns a 4-position tuple with: 0. the raw disorder scores from 0 to 1 where 1 is the highest probability that a residue is disordered, 1. the smoothed disorder score used for boundary identification, 2. a list of elements where each element is a list where 0 and 1 define the IDR location and 2 gives the actual sequence, and 3. a list of elements where each element is a list where 0 and 1 define the folded domain location and 2 gives the actual sequence
meta.predict_disorder_domains("MKAPSNGFLPSSNEGEKKPINSQLWHACAGPLVSLPPVGSLVVYFPQGHSEQVAASMQKQTDFIPNYPNLPSKLICLLHS")
would output -
[[0.828, 0.891, 0.885, 0.859, 0.815, 0.795, 0.773, 0.677, 0.66, 0.736, 0.733, 0.708, 0.66, 0.631, 0.601, 0.564, 0.532, 0.508, 0.495, 0.458, 0.383, 0.373, 0.398, 0.36, 0.205, 0.158, 0.135, 0.091, 0.09, 0.102, 0.126, 0.129, 0.114, 0.106, 0.097, 0.085, 0.099, 0.114, 0.093, 0.119, 0.117, 0.043, 0.015, 0.05, 0.139, 0.172, 0.144, 0.121, 0.124, 0.128, 0.147, 0.173, 0.129, 0.152, 0.169, 0.2, 0.172, 0.22, 0.216, 0.25, 0.272, 0.308, 0.248, 0.255, 0.301, 0.274, 0.264, 0.28, 0.25, 0.235, 0.221, 0.211, 0.235, 0.185, 0.14, 0.168, 0.307, 0.509, 0.544, 0.402], array([0.87596856, 0.86139124, 0.84596224, 0.82968293, 0.81255466,
0.79457882, 0.77575677, 0.75608988, 0.73557951, 0.71422703,
0.69203382, 0.66900124, 0.63956894, 0.62124099, 0.60188696,
0.57893168, 0.55241615, 0.52131925, 0.4859528 , 0.44109689,
0.39353789, 0.35264348, 0.31495776, 0.28 , 0.24661615,
0.21469814, 0.18500621, 0.15963478, 0.13604845, 0.1172087 ,
0.10798882, 0.1026882 , 0.09419503, 0.08462484, 0.08256398,
0.08832671, 0.0908559 , 0.09263851, 0.09438758, 0.09309938,
0.09102733, 0.09338137, 0.09665342, 0.10073913, 0.10392671,
0.11010311, 0.11402981, 0.11898634, 0.12430683, 0.13169441,
0.1381764 , 0.15245093, 0.16746957, 0.17518385, 0.18167578,
0.18893043, 0.20013416, 0.21581491, 0.23015652, 0.2420559 ,
0.25209814, 0.25817391, 0.26588944, 0.27456894, 0.27429068,
0.26411925, 0.24452671, 0.23076894, 0.22834783, 0.21689842,
0.20887549, 0.20564427, 0.20856996, 0.21901779, 0.23835296,
0.26794071, 0.30914625, 0.36333478, 0.43187154, 0.51612174]), [[0, 20, 'MKAPSNGFLPSSNEGEKKPI']], [[20, 80, 'NSQLWHACAGPLVSLPPVGSLVVYFPQGHSEQVAASMQKQTDFIPNYPNLPSKLICLLHS']]]
Additional Usage
Altering the disorder theshhold - To alter the disorder threshold, simply set disorder_threshold=my_value where my_value is a float. The higher the threshold value, the more conservative metapredict will be for designating a region as disordered. Default = 0.42
Example
meta.predict_disorder_domains("MKAPSNGFLPSSNEGEKKPINSQLWHACAGPLV", disorder_threshold=0.3)
Altering minimum IDR size - The minimum IDR size will define the smallest possible region that could be considered an IDR. In other words, you will not be able to get back an IDR smaller than the defined size. Default is 12.
Example
meta.predict_disorder_domains("MKAPSNGFLPSSNEGEKKPINSQLWHACAGPLV", minimum_IDR_size = 10)
Altering the minimum folded domain size - The minimum folded domain size defines where we expect the limit of small folded domains to be. NOTE this is not a hard limit and functions more to modulate the removal of large gaps. In other words, gaps less than this size are treated less strictly. Note that, in addition, gaps < 35 are evaluated with a threshold of 0.35 x disorder_threshold and gaps < 20 are evaluated with a threshold of 0.25 x disorder_threshold. These two lengths were decided based on the fact that coiled-coiled regions (which are IDRs in isolation) often show up with reduced apparent disorder within IDRs 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.
Example
meta.predict_disorder_domains("MKAPSNGFLPSSNEGEKKPINSQLWHACAGPLV", minimum_folded_domain = 60)
Altering gap_closure - The gap closure defines the largest gap that would be closed. Gaps here refer to a scenario in which you have two groups of disordered residues separated by a 'gap' of not disordered residues. In general large gap sizes will favor larger contiguous 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.
Example
meta.predict_disorder_domains("MKAPSNGFLPSSNEGEKKPINSQLWHACAGPLV", gap_closure = 5)
Using the original metapredict predictor
To use the original metapredict predictor as opposed to our new, updated predictor, set legacy=True
meta.predict_disorder_domains("DAPTSQEHTQAEDKERDSKTHPQKKQSPS", legacy=True)
In addition to inputting a sequence, you can predict disorder domains by inputting a Uniprot ID by using the predict_disorder_domains_uniprot function. This function has the exact same functionality as predict_disorder_domains except you can now input a Uniprot ID.
Example
meta.predict_disorder_domains_uniprot('Q8N6T3')
Using the original metapredict predictor
To use the original metapredict predictor as opposed to our new, updated predictor, set legacy=True
meta.predict_disorder_domains_uniprot('Q8N6T3', legacy=True)
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
Adding Predicted AlphaFold2 Confidence Scores - To add predicted AlphaFold2 pLDDT confidence scores, simply specify pLDDT_scores=True.
Example
meta.graph_disorder("DAPTSQEHTQAEDKERDSKTHPQKKQSPS", pLDDT_scores=True)
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 protein disorder. However, the title can be specified by specifying title = "my cool title" would result in a title of my cool title.
Example
meta.graph_disorder("DAPPTSQEHTQAEDKERD", title="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)
Changing the disorder threshold line - The disorder threshold line for graphs defaults to 0.3. However, if you want to change where the line designating the disorder cutoff is, simply specify disorder_threshold = Float where Float is some decimal value between 0 and 1.
Example
meta.graph_disorder("DAPPTSQEHTQAEDKERD", disorder_threshold=0.5)
Adding shaded regions to the graph - If you would like to shade specific regions of your generated graph (perhaps shade the disordered regions), you can specify shaded_regions=[[list of regions]] where the list of regions is a list of lists that defines the regions to shade.
Example
meta.graph_disorder("DAPPTSQEHTQAEDKERDDAPPTSQEHTQAEDKERDDAPPTSQEHTQAEDKERD", shaded_regions=[[1, 20], [30, 40]])
In addition, you can specify the color of the shaded regions by specifying shaded_region_color. The default for this is red. You can specify any matplotlib color or a hex color string.
Example
meta.graph_disorder("DAPPTSQEHTQAEDKERDDAPPTSQEHTQAEDKERDDAPPTSQEHTQAEDKERD", shaded_regions=[[1, 20], [30, 40]], shaded_region_color="blue")
Saving the graph - By default, the graph will automatically appear. However, you can also save the graph if you'd like. To do this, simply specify output_file = path_where_to_save/filename.file_extension. For example, output_file=/Users/thisUser/Desktop/cool_graphs/myCoolGraph.png. You can save the file with any valid matplotlib extension (.png, .pdf, etc.).
Example
meta.graph_disorder("DAPPTSQEHTQAEDKER", output_file=/Users/thisUser/Desktop/cool_graphs/myCoolGraph.png)
Using the original metapredict predictor
To use the original metapredict predictor as opposed to our new, updated predictor, set legacy=True
meta.graph_disorder("DAPPTSQEHTQAEDKER", legacy=True)
The graph_pLDDT function will show a plot of the predicted AlphaFold2 pLDDT confidence scores across the input amino acid sequence.
meta.graph_pLDDT("DAPTSQEHTQAEDKERDSKTHPQKKQSPS")
This function has all of the same functionality as graph_disorder.
The percent_disorder function will return the percent of residues in a sequence that have predicted consensus disorder values of 30% 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 0.3 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.
Using the original metapredict predictor
To use the original metapredict predictor as opposed to our new, updated predictor, set legacy=True
meta.percent_disorder("DAPPTSQEHTQAEDKERD", legacy=True)
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 output_file = "location you want to save the file to". When specifying the file path, you also want to specify the file name. 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", output_file="file path where the output .csv should be saved")
An actual file path would look something like:
meta.predict_disorder_fasta("/Users/thisUser/Desktop/coolSequences.fasta", output_file="/Users/thisUser/Desktop/cool_predictions.csv")
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 no 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)
Using the original metapredict predictor
To use the original metapredict predictor as opposed to our new, updated predictor, set legacy=True
meta.predict_disorder_fasta("/Users/thisUser/Desktop/coolSequences.fasta", legacy=True)
Just like with predict_disorder_fasta, you can use predict_pLDDT_fasta to get predicted AlphaFold2 pLDDT confidence scores from a fasta file. All the same functionality in predict_disorder_fasta is in predict_pLDDT_fasta.
Example
meta.predict_pLDDT_fasta("/Users/thisUser/Desktop/coolSequences.fasta")
By using the predict_disorder_uniprot function, you can return predicted consensus disorder values for the amino acid sequence of a protein by specifying the Uniprot ID.
Example
meta.predict_disorder_uniprot("Q8N6T3")
Using the original metapredict predictor
To use the original metapredict predictor as opposed to our new, updated predictor, set legacy=True
meta.predict_disorder_uniprot("Q8N6T3", legacy=True)
By using the predict_pLDDT_uniprot function, you can generate predicted AlphaFold2 pLDDT confidence scores by inputting a Uniprot ID.
Example
meta.predict_pLDDT_uniprot('P16892')
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 by default will return the graphs immediately. However, you can specify output_dir=path_to_save_files, which results in a .png file saved to that directory for every sequence within the .fasta file. 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 = True. This can be useful if you have sequences where the 1st 14 characters may be identical, which would otherwise overwrite an output file. By default, this will return a single graph for every sequence in the FASTA file.
WARNING - This command will generate a graph for every sequence in the .fasta file. If you have 1,000 sequences in a .fasta file and you do not specify the output_dir, it will generate 1,000 graphs that you will have to close sequentially. Therefore, I recommend specifying the output_dir such that the output is saved to a dedicated folder.
Example
meta.graph_disorder_fasta("file path to .fasta file/fileName.fasta", output_dir="file path of where to save output graphs")
An actual file path would look something like:
meta.graph_disorder_fasta("/Users/thisUser/Desktop/coolSequences.fasta", output_dir="/Users/thisUser/Desktop/folderForGraphs")
Additional Usage
Adding Predicted AlphaFold2 Confidence Scores - To add predicted AlphaFold2 pLDDT confidence scores, simply specify pLDDT_scores=True.
Example
meta.graph_disorder_fasta("/Users/thisUser/Desktop/coolSequences.fasta", pLDDT_scores=True)
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_dir="/Users/thisUser/Desktop/folderForGraphs")
Changing the output File Type - By default the output file is a .png. However, you can specify the output file type by using output_filetype="file_type" where file_type is some matplotlib compatible file type (such as .pdf).
Example
meta.graph_disorder_fasta("/Users/thisUser/Desktop/coolSequences.fasta", output_dir="/Users/thisUser/Desktop/folderForGraphs", output_filetype = "pdf")
Indexing generated files - If you would like to index the file names with a leading unique integer starting at 1, set indexed_filenames=True.
Example
meta.graph_disorder_fasta("/Users/thisUser/Desktop/coolSequences.fasta", output_dir="/Users/thisUser/Desktop/folderForGraphs", indexed_filenames=True)
Using the original metapredict predictor
To use the original metapredict predictor as opposed to our new, updated predictor, set legacy=True
meta.graph_disorder_fasta("/Users/thisUser/Desktop/coolSequences.fasta", output_dir="/Users/thisUser/Desktop/folderForGraphs", legacy=True)
By using the graph_pLDDT_fasta function, you can graph predicted AlphaFold2 pLDDT confidence scores for the amino acid sequences in a .fasta file. This works the same as graph_disorder_fasta but instead returns graphs with just the predicted AlphaFold2 pLDDT scores.
Example
meta.graph_pLDDT_fasta("/Users/thisUser/Desktop/coolSequences.fasta", output_dir="/Users/thisUser/Desktop/folderForGraphs")
By using the graph_disorder_uniprot function, you can graph predicted consensus disorder values for the amino acid sequence of a protein by specifying the Uniprot ID.
Example
meta.graph_disorder_uniprot("Q8N6T3")
This function carries all of the same functionality as graph_disorder including specifying disorder_threshold, title of the graph, the DPI, and whether or not to save the output.
Example
meta.graph_disorder_uniprot("Q8N6T3", disorder_threshold=0.5, title="my protein", DPI=300, output_file="/Users/thisUser/Desktop/my_cool_graph.png")
Additional usage
Adding Predicted AlphaFold2 Confidence Scores - To add predicted AlphaFold2 pLDDT confidence scores, simply specify pLDDT_scores=True.
Example
meta.graph_disorder_uniprot("Q8N6T3", pLDDT_scores=True)
Using the original metapredict predictor
To use the original metapredict predictor as opposed to our new, updated predictor, set legacy=True
meta.graph_disorder_uniprot("Q8N6T3", legacy=True)
Just like with disorder predictions, you can also get AlphaFold2 pLDDT confidence score graphs using the Uniprot ID. This will only display the pLDDT confidence scores and not the predicted disorder scores.
Example
meta.graph_pLDDT_uniprot("Q8N6T3")
The predict_disorder_domains_from_external_scores() function takes in an array or list of disorder scores, an amino acid sequence (optionally), and returns a DisorderObject. This function lets you use other disorder predictor scores and still use the predict_disorder_domains() functionality. The DisorderObject has 6 dot variables that can be called to get information about your input sequence. They are as follows:
.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
Examples
seq = meta.predict_disorder_domains_from_external_scores(disorder=[0.8577, 0.9313, 0.9313, 0.9158, 0.8985, 0.8903, 0.8895, 0.869, 0.8444, 0.8594, 0.8643, 0.8605, 0.8697, 0.8627, 0.8641, 0.8633, 0.8487, 0.8512, 0.8236, 0.8079, 0.8047, 0.8021, 0.7954, 0.7867, 0.7797, 0.7982, 0.7842, 0.7614, 0.7931, 0.8166, 0.8298, 0.8222, 0.8227, 0.8183, 0.8279, 0.838, 0.8535, 0.8512, 0.8464, 0.8469, 0.8322, 0.8265, 0.794, 0.7827, 0.7699, 0.7575, 0.7178, 0.5988], sequence = 'MKAPSNGFLPSSNEGEKKPINSQLMKAPSNGFLPSSNEGEKKPINSQL')
Now we can call the various dot values for seq.
Getting the sequence
print(seq.sequence)
returns
MKAPSNGFLPSSNEGEKKPINSQLMKAPSNGFLPSSNEGEKKPINSQL
Getting the disorder scores
print(seq.disorder)
Getting the disorder domain boundaries
print(seq.disordered_domain_boundaries)
Getting the folded domain boundaries
print(seq.folded_domain_boundaries)
Getting the disordered domain sequences
print(seq.disordered_domains)
Getting the folded domain sequences
print(seq.folded_domains)
Additional Usage
Altering the disorder theshhold - To alter the disorder threshold, simply set disorder_threshold=my_value where my_value is a float. The higher the threshold value, the more conservative metapredict will be for designating a region as disordered. Default = 0.42
Example
meta.predict_disorder_domains_from_external_scores("MKAPSNGFLPSSNEGEKKPINSQLWHACAGPLV", disorder_threshold=0.3)
Altering minimum IDR size - The minimum IDR size will define the smallest possible region that could be considered an IDR. In other words, you will not be able to get back an IDR smaller than the defined size. Default is 12.
Example
meta.predict_disorder_domains_from_external_scores("MKAPSNGFLPSSNEGEKKPINSQLWHACAGPLV", minimum_IDR_size = 10)
Altering the minimum folded domain size - The minimum folded domain size defines where we expect the limit of small folded domains to be. NOTE this is not a hard limit and functions more to modulate the removal of large gaps. In other words, gaps less than this size are treated less strictly. Note that, in addition, gaps < 35 are evaluated with a threshold of 0.35 x disorder_threshold and gaps < 20 are evaluated with a threshold of 0.25 x disorder_threshold. These two lengths were decided based on the fact that coiled-coiled regions (which are IDRs in isolation) often show up with reduced apparent disorder within IDRs 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.
Example
meta.predict_disorder_domains_from_external_scores("MKAPSNGFLPSSNEGEKKPINSQLWHACAGPLV", minimum_folded_domain = 60)
Altering gap_closure - The gap closure defines the largest gap that would be closed. Gaps here refer to a scenario where you have two groups of disordered residues separated by a 'gap' of not disordered residues. In general large gap sizes will favor larger contiguous IDRs. It's worth noting that gap_closure becomes relevant only when minimum_region_size becomes very small (i.e., < 5) because gaps really emerge when the smoothed disorder fit is "noisy", but when smoothed, gaps are increasingly rare. Default=10.
Example
meta.predict_disorder_domains_from_external_scores("MKAPSNGFLPSSNEGEKKPINSQLWHACAGPLV", gap_closure = 5)
I have received occasional 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 should work without issue on Python versions 3.x and 3.8.x. It was developed on Python 3.7 and has been tested extensively on Python 3.8. It should also work on Python 3.9 although this has been less well-tested
In general, we recommend using a conda environment for any Python computing you're doing. This lets you control the packages being used and the Python version. 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
Below we include documentation on known issues.
macOS libiomp clash
PyTorch current ships with its own version of the OpenMP library (libiomp.dylib). Unfortunately when numpy is installed from conda (although not from pip) this leads to a collision because the conda-derived numpy library also includes a local copy of the libiomp5.dylib library. This leads to the following error message (included here for google-ability).
OMP: Error #15: Initializing libiomp5.dylib, but found libomp.dylib already initialized. OMP: Hint This means that multiple copies of the OpenMP runtime have been linked into the program. That is dangerous, since it can degrade performance or cause incorrect results. The best thing to do is to ensure that only a single OpenMP runtime is linked into the process, e.g. by avoiding static linking of the OpenMP runtime in any library. As an unsafe, unsupported, undocumented workaround you can set the environment variable KMP_DUPLICATE_LIB_OK=TRUE to allow the program to continue to execute, but that may cause crashes or silently produce incorrect results. For more information, please see http://www.intel.com/software/products/support/.
To avoid this error we make the executive decision to ignore this clash. This has largely not appeared to have any deleterious issues on performance or accuracy across the tests run. If you are uncomfortable with this then the code in metapredict/__init__.py can be edited with IGNORE_LIBOMP_ERROR set to False and metapredict re-installed from the source directory.
To see if your installation of metapredict is working properly, you can run the unit test included in the package by navigating to the metapredict/tests folder within the installation directory and
running:
$ pytest -v
Example data that can be used with metapredict can be found in the metapredict/data folder on GitHub. The example data set is just a .fasta file containing 5 protein sequences.