| Name | yunta JSON |
| Version |
0.0.1.post1
JSON |
| download |
| home_page | None |
| Summary | Predicting protein-protein interactions and structures from multiple sequence alignments. |
| upload_time | 2024-09-22 20:46:32 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
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| keywords |
science
biology
systems biology
biochemistry
machine learning
analysis
deep learning
alphafold2
rosettafold
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
# 🍐 yunta
Predicting a pairwise protein-protein interactions and structures from multiple sequence alignments.
- [Installation](#installation)
- [Credit](#credit)
- [Command-line usage](#command-line-usage)
- [Generating multiple-sequence alignments](#generating-multiple-sequence-alignments)
- [Calculating contact maps](#calculating-contact-maps)
- [Predicting protein complex structures]
- [Command-line tools](#command-line-tools)
- [Python API](#python-api)
- [Scaling up](#-if-you-want-to-scale-up)
- [Issues, problems, suggestions](#issues-problems-suggestions)
- [Further help](#further-help)
**`yunta`** provides several implementations of protein-protein interaction evaluation. In increasing computational cost:
- GPU-accelerated direct coupling analysis (DCA) (in Tensorflow and PyTorch)
- [RoseTTAFold](https://github.com/RosettaCommons/RoseTTAFold)-2track via the `rf2t-micro` package
- [AlphaFold2](https://github.com/google-deepmind/alphafold) for protein-protein structure prediction
`yunta` has streamlined installation, a command-line interface, a Python API, and some resilience to GPU out-of-memory error (though CPU-fallback). It takes as input unpaired multiple-sequence alignments in A3M format (as generated by tools like [`hhblits`](https://github.com/soedinglab/hh-suite)), and outputs a matrix of inter-residue contacts.
Rough timings for a pair of ~200 amino-acid proteins (_S. cerevisiae_ DHFR and WW domain-containing protein) on CPU:
- **DCA**: 5 seconds
<img src="docs/source/_static/P07807-P43582.apc=True.png" alt="" width="200">
- **RosettaFold-2track**: 10 seconds
<img src="docs/source/_static/P07807-P43582-rf2t.png" alt="" width="200">
- **AlphaFold2**: 1 hour
<img src="docs/source/_static/P07807-P43582-af2.png" alt="" width="200">
Note that these times will increase quadratically with the total length of the proteins.
## Installation
Obtaining and setting up `yunta` is easy.
```bash
$ pip install yunta
```
If you want to enable GPU, use
```bash
$ pip install yunta[cuda12]
```
If you want to use a local CUDA installation instead, use
```bash
$ pip install yunta[cuda12_local]
```
Using the embedded model requires using the [RoseTTAFold](https://github.com/RosettaCommons/RoseTTAFold)-2track and AlphaFold2 weights. These are automatically downloaded, but by using `yunta` you agree that the trained weights for RoseTTAFold are made available for non-commercial use only under the terms of the [Rosetta-DL Software license](https://files.ipd.uw.edu/pub/RoseTTAFold/Rosetta-DL_LICENSE.txt) and AlphaFold2's pretrained parameters fall under the [CC BY 4.0 license](https://creativecommons.org/licenses/by/4.0/legalcode).
## Credit
`yunta` is a fork of [SpeedPPI](https://www.biorxiv.org/content/10.1101/2023.04.15.536993v1), which is itself inspired by [FoldDock](https://www.nature.com/articles/s41467-022-28865-w). This method used AlphaFold2 to evaluate 65,484 protein-protein interactions from the human proteome in [Towards a structurally resolved human protein interaction network](https://www.nature.com/articles/s41594-022-00910-8).
The idea of using DCA, [RoseTTAFold](https://github.com/RosettaCommons/RoseTTAFold)-2track, and [AlphaFold2](https://github.com/google-deepmind/alphafold) in a cascade of increasingly expensive and specific PPI detection methods has been explored in a series of papers from David Baker's lab:
- [Cong et al., Protein interaction networks revealed by proteome coevolution. _Science_, 2019](https://doi.org/10.1126/science.aaw6718)
- [Humpreys et al., Computed structures of core eukaryotic protein complexes. _Science_, 2021](https://doi.org/10.1126/science.abm4805)
- [Humpreys et al., Protein interactions in human pathogens revealed through deep learning. _Nature Microbiology_, 2024](https://doi.org/10.1038/s41564-024-01791-x)
`yunta` puts these algorithms in one place with easy installation, a command-line interface, and a Python API.
## Command-line usage
You can always get more help by running
```bash
$ yunta --help
usage: yunta [-h] {dca-single,dca-many,rf2t-single,af2-single,af2-many} ...
Screening protein-protein interactions using DCA and AlphaFold2.
options:
-h, --help show this help message and exit
Sub-commands:
{dca-single,dca-many,rf2t-single,af2-single,af2-many}
Use these commands to specify the tool you want to use.
dca-single Calculate DCA for one protein-protein interaction.
dca-many Calculate DCA between two sets of proteins, or all pairs in one set of proteins.
rf2t-single Calculate RF-2track contacts for between one protein and a series of others.
af2-single Model one protein-protein interaction.
af2-many Model all interactions between two sets of proteins, or all pairs in one set of proteins.
```
### Generating multiple-sequence alignments
All the algorithms depend on pre-computed multiple-sequence alignments (MSAs) between a protein of interest and as many other proteins as possible. This allows computations to be sped up by separating out this phase of the calculation. You can generate MSAs using a dedicated tool like [`hhblits`](https://github.com/soedinglab/hh-suite), which will speed up the process by using pre-clustered datasbes like UniClust. We typically use a command like:
```bash
hhblits -e 0.01 -v 3 -d /path/to/UniClust-database -i input.fasta -oa3m output-msa.a3m -o /dev/null -cov 60 -n 3 -realign -realign_max 10000
```
In our experience, this can take 1-40 min depending on the complexity of the query. Check the [`hhsuite`](https://github.com/soedinglab/hh-suite) documentation for more details.
Once you have your MSAs, you can use the information contained within them using tools in `yunta` to calculate contact maps and predict structures of protein complexes with AlphaFold2.
### Calculating contact maps
Given two MSAs, `yunta` will calculate the contact map using DCA, RF2t, or AlphaFold2, and produce a summary table for each pair provided as input.
Using DCA or RF2t will produce a table like this:
```bash
$ yunta dca-single test/inputs/DYR_YEAST.a3m -2 test/inputs/CAPZA_YEAST.a3m -o test/outputs/dca-single.tsv --apc
```
| ID | uniprot_id_1 | uniprot_id_2 | seq_len | chain_a_len | chain_b_len | msa1_depth | msa2_depth | msa_depth | n_eff | apc | mean | median | maximum | minimum |
| -- | ------------ | ------------ | ------- | ------------ | ----------- | ----------- | ----------- | --------- | ----- | --- | ---- | ------ | -------- | ------- |
O13297-D6VTK4 | O13297 | D6VTK4 | 980 | 549 | 431 | 14246 | 1546 | 670 | 2 | False | 0.01830857 | 0.014683756 | 0.07428725 | 2.284808e-06 |
If you also give the `--plot` option, then the contact maps for the entore complex and only the inter-chain contacts will be saved, along with CSV files containing the numerical values as matrix. For example,
```bash
$ yunta dca-single test/inputs/DYR_YEAST.a3m -2 test/inputs/CAPZA_YEAST.a3m -o test/outputs/dca-single.tsv --apc --plot test/outputs/DYR_YEAST-CAPZA_YEAST
```
<img src="docs/source/_static/P07807-P43582.apc=True.png" alt="" width="350">
<img src="docs/source/_static/P07807-P43582.apc=True.interaction.png" alt="" width="350">
| 0 | 1 | 2 | ... | 420 | 421 |
| - | - | - | --- | --- | --- |
| -0.0 | 0.0009014737 | 0.0010275221 | ... | 0.0005961701 | -1.9190367e-05 |
`...`
### Predicting protein complex structures
`yunta` can also feed your MSAs into the AlphaFold2 model to predict structures of binary protein complexes.
```bash
$ yunta af2-single test/inputs/DYR_YEAST.a3m -2 test/inputs/CAPZA_YEAST.a3m -o test/outputs/af2-single
```
This will also generate a table
Using `--plot` will generate the contact maps as with the other commands.
```bash
$ yunta af2-single test/inputs/DYR_YEAST.a3m -2 test/inputs/CAPZA_YEAST.a3m -o test/outputs/af2-single --plot test/outputs/af2-single-plot
```
<img src="docs/source/_static/P07807-P43582-af2.png" alt="" width="350">
<img src="docs/source/_static/P07807-P43582-af2.interaction.png" alt="" width="350">
### Command-line tools
You can run 1-vs-many with the `*-single` commands. For example:
```bash
$ yunta dca-single --help
usage: yunta dca-single [-h] [--msa2 [MSA2 ...]] [--list-file] [--output [OUTPUT]] [--plot PLOT] [--apc] [msa1]
positional arguments:
msa1 MSA file. Default: STDIN.
options:
-h, --help show this help message and exit
--msa2 [MSA2 ...], -2 [MSA2 ...]
Second MSA file(s). Default: if not provided, all pairwise from msa1.
--list-file, -l Treat inputs as plain-text list of MSA files, rather than MSA filenames. Default: treat as MSA filenames.
--output [OUTPUT], -o [OUTPUT]
Output filename. Default: STDOUT.
--plot PLOT, -p PLOT Directory for saving plots. Default: don't plot.
--apc, -a Whether to use APC correction in DCA. Default: don't apply correction.
```
If one MSA is provided, then homodimeric interactions are probed. For convenience, you can use the `--list-file` option to provide a single file containing a list of MSA files (one per line).
You can run many-vs-many with the `*-many` commands. For example:
```bash
$ yunta af2-many --help
usage: yunta af2-many [-h] [--msa2 [MSA2 ...]] [--list-file] --output OUTPUT [--params PARAMS] [--recycles RECYCLES] [--plot PLOT] [msa1 ...]
positional arguments:
msa1 MSA file(s). Default: "<_io.TextIOWrapper name='<stdin>' mode='r' encoding='utf-8'>".
options:
-h, --help show this help message and exit
--msa2 [MSA2 ...], -2 [MSA2 ...]
Second MSA file(s). Default: if not provided, all pairwise from msa1.
--list-file, -l Treat inputs as plain-text list of MSA files, rather than MSA filenames. Default: treat as MSA filenames.
--output OUTPUT, -o OUTPUT
Output directory. Required.
--params PARAMS, -w PARAMS
Path to AlphaFold2 params file (.npz).
--recycles RECYCLES, -x RECYCLES
Maximum number of recyles through the model. Default: "10".
--plot PLOT, -p PLOT Directory for saving plots. Default: don't plot.
```
## Python API
We provide an API for using MSAs in your own programs.
```python
>>> from yunta.structs.msa import *
>>> msa = MSA.from_file("my-msa-file.a3m")
>>> msa.neff()
6
```
We also provide a reusable GPU-accelerated Tensorflow implementation of DCA (adapted from [Humpreys, _Science_, 2021](https://doi.org/10.1126/science.abm4805)).
```python
>>> from yunta.dca import calculate_dca
>>> from yunta.structs.msa import *
>>> paired_msa = PairedMSA.from_file("my-msa-file1.a3m", "my-msa-file2.a3m")
>>> calculate_dca(paired_msa, apc=True, gpu=False)
```
In case you prefer, you can also import a PyTorch implementation (which anecdotally is faster on both CPU and GPU).
```python
>>> from yunta.dca_torch import calculate_dca
>>> from yunta.structs.msa import *
>>> paired_msa = PairedMSA.from_file("my-msa-file1.a3m", "my-msa-file2.a3m")
>>> calculate_dca(paired_msa, apc=True, gpu=False)
```
(More documentation coming soon!)
## ... if you want to scale up
While the `*-many` commands can deal with processing multiple possible protein-protein interactions, if you want to screen more than a few and have access to a HPC cluster then using our [`nf-ggi` Nextflow pipeline](https://github.com/scbirlab/nf-ggi) will be more efficient.
## Issues, problems, suggestions
Add to the [issue tracker](https://www.github.com/scbirlab/yunta/issues).
## Further help
- [RoseTTAFold](https://github.com/RosettaCommons/RoseTTAFold)
- [AlphaFold2](https://github.com/google-deepmind/alphafold)
- [rf2t-micro](https://github.com/scbirlab/rf2t-micro)
- [hhblits](https://github.com/soedinglab/hh-suite)
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"download_url": "https://files.pythonhosted.org/packages/a6/1a/9d04f7121f33617593999fcd9c0ce427fd8bda0330965ff3fba8d05da9b2/yunta-0.0.1.post1.tar.gz",
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"description": "# \ud83c\udf50 yunta\n\n\n\n\n\nPredicting a pairwise protein-protein interactions and structures from multiple sequence alignments.\n\n- [Installation](#installation)\n- [Credit](#credit)\n- [Command-line usage](#command-line-usage)\n - [Generating multiple-sequence alignments](#generating-multiple-sequence-alignments)\n - [Calculating contact maps](#calculating-contact-maps)\n - [Predicting protein complex structures]\n - [Command-line tools](#command-line-tools)\n- [Python API](#python-api)\n- [Scaling up](#-if-you-want-to-scale-up)\n- [Issues, problems, suggestions](#issues-problems-suggestions)\n- [Further help](#further-help)\n\n**`yunta`** provides several implementations of protein-protein interaction evaluation. In increasing computational cost:\n\n- GPU-accelerated direct coupling analysis (DCA) (in Tensorflow and PyTorch)\n- [RoseTTAFold](https://github.com/RosettaCommons/RoseTTAFold)-2track via the `rf2t-micro` package\n- [AlphaFold2](https://github.com/google-deepmind/alphafold) for protein-protein structure prediction\n\n`yunta` has streamlined installation, a command-line interface, a Python API, and some resilience to GPU out-of-memory error (though CPU-fallback). It takes as input unpaired multiple-sequence alignments in A3M format (as generated by tools like [`hhblits`](https://github.com/soedinglab/hh-suite)), and outputs a matrix of inter-residue contacts.\n\nRough timings for a pair of ~200 amino-acid proteins (_S. cerevisiae_ DHFR and WW domain-containing protein) on CPU:\n\n- **DCA**: 5 seconds\n\n<img src=\"docs/source/_static/P07807-P43582.apc=True.png\" alt=\"\" width=\"200\">\n\n- **RosettaFold-2track**: 10 seconds\n\n<img src=\"docs/source/_static/P07807-P43582-rf2t.png\" alt=\"\" width=\"200\">\n\n- **AlphaFold2**: 1 hour\n\n<img src=\"docs/source/_static/P07807-P43582-af2.png\" alt=\"\" width=\"200\">\n\nNote that these times will increase quadratically with the total length of the proteins.\n\n## Installation\n\nObtaining and setting up `yunta` is easy.\n\n```bash\n$ pip install yunta\n```\n\nIf you want to enable GPU, use\n\n```bash\n$ pip install yunta[cuda12]\n```\n\nIf you want to use a local CUDA installation instead, use\n\n```bash\n$ pip install yunta[cuda12_local]\n```\n\nUsing the embedded model requires using the [RoseTTAFold](https://github.com/RosettaCommons/RoseTTAFold)-2track and AlphaFold2 weights. These are automatically downloaded, but by using `yunta` you agree that the trained weights for RoseTTAFold are made available for non-commercial use only under the terms of the [Rosetta-DL Software license](https://files.ipd.uw.edu/pub/RoseTTAFold/Rosetta-DL_LICENSE.txt) and AlphaFold2's pretrained parameters fall under the [CC BY 4.0 license](https://creativecommons.org/licenses/by/4.0/legalcode).\n\n## Credit\n\n`yunta` is a fork of [SpeedPPI](https://www.biorxiv.org/content/10.1101/2023.04.15.536993v1), which is itself inspired by [FoldDock](https://www.nature.com/articles/s41467-022-28865-w). This method used AlphaFold2 to evaluate 65,484 protein-protein interactions from the human proteome in [Towards a structurally resolved human protein interaction network](https://www.nature.com/articles/s41594-022-00910-8).\n\nThe idea of using DCA, [RoseTTAFold](https://github.com/RosettaCommons/RoseTTAFold)-2track, and [AlphaFold2](https://github.com/google-deepmind/alphafold) in a cascade of increasingly expensive and specific PPI detection methods has been explored in a series of papers from David Baker's lab:\n\n- [Cong et al., Protein interaction networks revealed by proteome coevolution. _Science_, 2019](https://doi.org/10.1126/science.aaw6718)\n- [Humpreys et al., Computed structures of core eukaryotic protein complexes. _Science_, 2021](https://doi.org/10.1126/science.abm4805)\n- [Humpreys et al., Protein interactions in human pathogens revealed through deep learning. _Nature Microbiology_, 2024](https://doi.org/10.1038/s41564-024-01791-x)\n\n`yunta` puts these algorithms in one place with easy installation, a command-line interface, and a Python API.\n\n## Command-line usage \n\nYou can always get more help by running\n\n```bash\n$ yunta --help\nusage: yunta [-h] {dca-single,dca-many,rf2t-single,af2-single,af2-many} ...\n\nScreening protein-protein interactions using DCA and AlphaFold2.\n\noptions:\n -h, --help show this help message and exit\n\nSub-commands:\n {dca-single,dca-many,rf2t-single,af2-single,af2-many}\n Use these commands to specify the tool you want to use.\n dca-single Calculate DCA for one protein-protein interaction.\n dca-many Calculate DCA between two sets of proteins, or all pairs in one set of proteins.\n rf2t-single Calculate RF-2track contacts for between one protein and a series of others.\n af2-single Model one protein-protein interaction.\n af2-many Model all interactions between two sets of proteins, or all pairs in one set of proteins.\n```\n\n### Generating multiple-sequence alignments\n\nAll the algorithms depend on pre-computed multiple-sequence alignments (MSAs) between a protein of interest and as many other proteins as possible. This allows computations to be sped up by separating out this phase of the calculation. You can generate MSAs using a dedicated tool like [`hhblits`](https://github.com/soedinglab/hh-suite), which will speed up the process by using pre-clustered datasbes like UniClust. We typically use a command like:\n\n```bash\nhhblits -e 0.01 -v 3 -d /path/to/UniClust-database -i input.fasta -oa3m output-msa.a3m -o /dev/null -cov 60 -n 3 -realign -realign_max 10000\n```\n\nIn our experience, this can take 1-40 min depending on the complexity of the query. Check the [`hhsuite`](https://github.com/soedinglab/hh-suite) documentation for more details.\n\nOnce you have your MSAs, you can use the information contained within them using tools in `yunta` to calculate contact maps and predict structures of protein complexes with AlphaFold2.\n\n### Calculating contact maps\n\nGiven two MSAs, `yunta` will calculate the contact map using DCA, RF2t, or AlphaFold2, and produce a summary table for each pair provided as input. \n\nUsing DCA or RF2t will produce a table like this:\n\n```bash\n$ yunta dca-single test/inputs/DYR_YEAST.a3m -2 test/inputs/CAPZA_YEAST.a3m -o test/outputs/dca-single.tsv --apc\n```\n\n| ID | uniprot_id_1\t| uniprot_id_2 | seq_len | chain_a_len\t| chain_b_len\t| msa1_depth\t| msa2_depth\t| msa_depth\t| n_eff\t| apc\t| mean | median | maximum\t| minimum |\n| -- | ------------ | ------------ | ------- | ------------ | ----------- | ----------- | ----------- | ---------\t| ----- | ---\t| ---- | ------ | -------- | ------- |\nO13297-D6VTK4\t| O13297 | D6VTK4 | 980\t| 549\t| 431\t| 14246\t| 1546 | 670\t| 2\t| False\t| 0.01830857 | 0.014683756 | 0.07428725\t| 2.284808e-06 |\n\nIf you also give the `--plot` option, then the contact maps for the entore complex and only the inter-chain contacts will be saved, along with CSV files containing the numerical values as matrix. For example,\n\n```bash\n$ yunta dca-single test/inputs/DYR_YEAST.a3m -2 test/inputs/CAPZA_YEAST.a3m -o test/outputs/dca-single.tsv --apc --plot test/outputs/DYR_YEAST-CAPZA_YEAST\n```\n\n<img src=\"docs/source/_static/P07807-P43582.apc=True.png\" alt=\"\" width=\"350\">\n<img src=\"docs/source/_static/P07807-P43582.apc=True.interaction.png\" alt=\"\" width=\"350\">\n\n| 0 | 1 | 2 | ... | 420 | 421 |\n| - | - | - | --- | --- | --- |\n| -0.0 | 0.0009014737 | 0.0010275221 | ... | 0.0005961701 | -1.9190367e-05 | \n`...`\n\n### Predicting protein complex structures\n\n`yunta` can also feed your MSAs into the AlphaFold2 model to predict structures of binary protein complexes.\n\n```bash\n$ yunta af2-single test/inputs/DYR_YEAST.a3m -2 test/inputs/CAPZA_YEAST.a3m -o test/outputs/af2-single\n```\n\nThis will also generate a table\n\n\nUsing `--plot` will generate the contact maps as with the other commands.\n\n```bash\n$ yunta af2-single test/inputs/DYR_YEAST.a3m -2 test/inputs/CAPZA_YEAST.a3m -o test/outputs/af2-single --plot test/outputs/af2-single-plot\n```\n\n<img src=\"docs/source/_static/P07807-P43582-af2.png\" alt=\"\" width=\"350\">\n<img src=\"docs/source/_static/P07807-P43582-af2.interaction.png\" alt=\"\" width=\"350\">\n\n\n### Command-line tools\n\nYou can run 1-vs-many with the `*-single` commands. For example:\n\n```bash\n$ yunta dca-single --help\nusage: yunta dca-single [-h] [--msa2 [MSA2 ...]] [--list-file] [--output [OUTPUT]] [--plot PLOT] [--apc] [msa1]\n\npositional arguments:\n msa1 MSA file. Default: STDIN.\n\noptions:\n -h, --help show this help message and exit\n --msa2 [MSA2 ...], -2 [MSA2 ...]\n Second MSA file(s). Default: if not provided, all pairwise from msa1.\n --list-file, -l Treat inputs as plain-text list of MSA files, rather than MSA filenames. Default: treat as MSA filenames.\n --output [OUTPUT], -o [OUTPUT]\n Output filename. Default: STDOUT.\n --plot PLOT, -p PLOT Directory for saving plots. Default: don't plot.\n --apc, -a Whether to use APC correction in DCA. Default: don't apply correction.\n```\n\nIf one MSA is provided, then homodimeric interactions are probed. For convenience, you can use the `--list-file` option to provide a single file containing a list of MSA files (one per line).\n\nYou can run many-vs-many with the `*-many` commands. For example:\n\n```bash\n$ yunta af2-many --help\nusage: yunta af2-many [-h] [--msa2 [MSA2 ...]] [--list-file] --output OUTPUT [--params PARAMS] [--recycles RECYCLES] [--plot PLOT] [msa1 ...]\n\npositional arguments:\n msa1 MSA file(s). Default: \"<_io.TextIOWrapper name='<stdin>' mode='r' encoding='utf-8'>\".\n\noptions:\n -h, --help show this help message and exit\n --msa2 [MSA2 ...], -2 [MSA2 ...]\n Second MSA file(s). Default: if not provided, all pairwise from msa1.\n --list-file, -l Treat inputs as plain-text list of MSA files, rather than MSA filenames. Default: treat as MSA filenames.\n --output OUTPUT, -o OUTPUT\n Output directory. Required.\n --params PARAMS, -w PARAMS\n Path to AlphaFold2 params file (.npz).\n --recycles RECYCLES, -x RECYCLES\n Maximum number of recyles through the model. Default: \"10\".\n --plot PLOT, -p PLOT Directory for saving plots. Default: don't plot.\n```\n\n## Python API\n\nWe provide an API for using MSAs in your own programs. \n\n```python\n>>> from yunta.structs.msa import *\n>>> msa = MSA.from_file(\"my-msa-file.a3m\")\n>>> msa.neff()\n6\n```\n\nWe also provide a reusable GPU-accelerated Tensorflow implementation of DCA (adapted from [Humpreys, _Science_, 2021](https://doi.org/10.1126/science.abm4805)).\n\n```python\n>>> from yunta.dca import calculate_dca\n>>> from yunta.structs.msa import *\n>>> paired_msa = PairedMSA.from_file(\"my-msa-file1.a3m\", \"my-msa-file2.a3m\")\n>>> calculate_dca(paired_msa, apc=True, gpu=False)\n```\n\nIn case you prefer, you can also import a PyTorch implementation (which anecdotally is faster on both CPU and GPU).\n\n```python\n>>> from yunta.dca_torch import calculate_dca\n>>> from yunta.structs.msa import *\n>>> paired_msa = PairedMSA.from_file(\"my-msa-file1.a3m\", \"my-msa-file2.a3m\")\n>>> calculate_dca(paired_msa, apc=True, gpu=False)\n```\n\n(More documentation coming soon!)\n\n## ... if you want to scale up\n\nWhile the `*-many` commands can deal with processing multiple possible protein-protein interactions, if you want to screen more than a few and have access to a HPC cluster then using our [`nf-ggi` Nextflow pipeline](https://github.com/scbirlab/nf-ggi) will be more efficient. \n\n## Issues, problems, suggestions\n\nAdd to the [issue tracker](https://www.github.com/scbirlab/yunta/issues).\n\n## Further help\n\n- [RoseTTAFold](https://github.com/RosettaCommons/RoseTTAFold)\n- [AlphaFold2](https://github.com/google-deepmind/alphafold)\n- [rf2t-micro](https://github.com/scbirlab/rf2t-micro)\n- [hhblits](https://github.com/soedinglab/hh-suite)\n",
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