# Protein pairs & ColabFold pipeline


<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->

We use ColabFold MSA for protein pairs pipeline, as it takes shorter
time

## Setup

## Setup

``` sh
docker pull sky1ove/alphafold3
```

## Protein pairs

Since protein protein screening involves a lot of proteins, it takes a
long time for AF3 default MSA pipeline, so we used colabfold MSA
pipeline

------------------------------------------------------------------------

<a
href="https://github.com/sky1ove/af_kit/blob/main/af_kit/protein_pairs.py#L16"
target="_blank" style="float:right; font-size:smaller">source</a>

### get_colabfold_cmd

>  get_colabfold_cmd (csv_path, project_name)

``` python
project_name='sdf'
```

``` python
get_colabfold_cmd('sdf.csv',project_name)
```

    Run below in terminal:

     colabfold_batch sdf.csv msa_sdf --msa-only

## MSA

------------------------------------------------------------------------

<a
href="https://github.com/sky1ove/af_kit/blob/main/af_kit/protein_pairs.py#L21"
target="_blank" style="float:right; font-size:smaller">source</a>

### copy_a3m

>  copy_a3m (a3m_dir:str, dest_dir:str)

*Copies all .a3m files from the source directory to the destination
directory.*

<table>
<colgroup>
<col style="width: 9%" />
<col style="width: 38%" />
<col style="width: 52%" />
</colgroup>
<thead>
<tr>
<th></th>
<th><strong>Type</strong></th>
<th><strong>Details</strong></th>
</tr>
</thead>
<tbody>
<tr>
<td>a3m_dir</td>
<td>str</td>
<td>Path to the source directory containing .a3m files.</td>
</tr>
<tr>
<td>dest_dir</td>
<td>str</td>
<td>Path to the destination directory where files will be copied</td>
</tr>
</tbody>
</table>

``` python
copy_a3m(a3m_dir='data',dest_dir='af_input')
```

    Copying files: 100%|██████████| 1/1 [00:00<00:00, 637.53file/s]

    Copied 1 a3m files from data to af_input

## Protein-protein input

<div>

> **Important**
>
> Make sure a3m files are under af_input, otherwise it won’t detect the
> files

</div>

------------------------------------------------------------------------

<a
href="https://github.com/sky1ove/af_kit/blob/main/af_kit/protein_pairs.py#L37"
target="_blank" style="float:right; font-size:smaller">source</a>

### a3m_to_seq

>  a3m_to_seq (file_path:pathlib.Path)

*Get protein sequence from a3m file*

``` python
a3m_to_seq(Path(f'af_input/{project_name}/a3m/CD8A.a3m'))
```

    'SQFRVSPLDRTWNLGETVELKCQVLLSNPTSGCSWLFQPRGAAASPTFLLYLSQNKPKAAEGLDTQRFSGKRLGDTFVLTLSDFRRENEGYYFCSALSNSIMYFSHFVPVFLPAKPTTTPAPRPPTPAPTIASQPLSLRPEACRPAAGGAVHTRGLDFACD'

------------------------------------------------------------------------

<a
href="https://github.com/sky1ove/af_kit/blob/main/af_kit/protein_pairs.py#L42"
target="_blank" style="float:right; font-size:smaller">source</a>

### get_protein_subjson

>  get_protein_subjson (gene_name, a3m_dir='.', idx='A', run_template=True)

*Get subjson (protein part) with colabfold unpairedMSA .a3m path*

``` python
sub_json = get_protein_subjson('CD8A',a3m_dir=f'af_input/{project_name}/a3m')
```

``` python
sub_json
```

    {'id': 'A',
     'sequence': 'SQFRVSPLDRTWNLGETVELKCQVLLSNPTSGCSWLFQPRGAAASPTFLLYLSQNKPKAAEGLDTQRFSGKRLGDTFVLTLSDFRRENEGYYFCSALSNSIMYFSHFVPVFLPAKPTTTPAPRPPTPAPTIASQPLSLRPEACRPAAGGAVHTRGLDFACD',
     'modifications': [],
     'unpairedMsaPath': '/root/af_input/sdf/a3m/CD8A.a3m',
     'pairedMsa': '',
     'templates': None}

------------------------------------------------------------------------

<a
href="https://github.com/sky1ove/af_kit/blob/main/af_kit/protein_pairs.py#L59"
target="_blank" style="float:right; font-size:smaller">source</a>

### dump_json_folder

>  dump_json_folder (json_data, folder)

*Save json under a folder*

------------------------------------------------------------------------

<a
href="https://github.com/sky1ove/af_kit/blob/main/af_kit/protein_pairs.py#L65"
target="_blank" style="float:right; font-size:smaller">source</a>

### get_multi_protein_json

>  get_multi_protein_json (gene_list, a3m_dir, run_template=True,
>                              save_folder=None)

*Get json of multiple proteins, with unpaired MSA path indicated (from
colabfold MSA)*

``` python
AF_input = get_multi_protein_json(['CD8A','CD8A'],
                        a3m_dir=f'af_input/{project_name}/a3m',
                        save_folder=f'af_input/{project_name}')
```

You can generate a list of json files under a folder.

``` python
AF_input.keys(), len(AF_input['sequences'])
```

    (dict_keys(['name', 'modelSeeds', 'sequences', 'bondedAtomPairs', 'dialect', 'version']),
     2)

------------------------------------------------------------------------

<a
href="https://github.com/sky1ove/af_kit/blob/main/af_kit/protein_pairs.py#L86"
target="_blank" style="float:right; font-size:smaller">source</a>

### generate_pair_df

>  generate_pair_df (gene_list, self_pair=True)

*Unique pair genes in a gene list*

``` python
generate_pair_df(list('ABC'))
```

<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }
&#10;    .dataframe tbody tr th {
        vertical-align: top;
    }
&#10;    .dataframe thead th {
        text-align: right;
    }
</style>

<table class="dataframe" data-quarto-postprocess="true" data-border="1">
<thead>
<tr style="text-align: right;">
<th data-quarto-table-cell-role="th"></th>
<th data-quarto-table-cell-role="th">Gene1</th>
<th data-quarto-table-cell-role="th">Gene2</th>
</tr>
</thead>
<tbody>
<tr>
<td data-quarto-table-cell-role="th">0</td>
<td>A</td>
<td>B</td>
</tr>
<tr>
<td data-quarto-table-cell-role="th">1</td>
<td>A</td>
<td>C</td>
</tr>
<tr>
<td data-quarto-table-cell-role="th">2</td>
<td>B</td>
<td>C</td>
</tr>
<tr>
<td data-quarto-table-cell-role="th">3</td>
<td>A</td>
<td>A</td>
</tr>
<tr>
<td data-quarto-table-cell-role="th">4</td>
<td>B</td>
<td>B</td>
</tr>
<tr>
<td data-quarto-table-cell-role="th">5</td>
<td>C</td>
<td>C</td>
</tr>
</tbody>
</table>

</div>

``` python
df = generate_pair_df(['CD8A'])
df
```

<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }
&#10;    .dataframe tbody tr th {
        vertical-align: top;
    }
&#10;    .dataframe thead th {
        text-align: right;
    }
</style>

<table class="dataframe" data-quarto-postprocess="true" data-border="1">
<thead>
<tr style="text-align: right;">
<th data-quarto-table-cell-role="th"></th>
<th data-quarto-table-cell-role="th">Gene1</th>
<th data-quarto-table-cell-role="th">Gene2</th>
</tr>
</thead>
<tbody>
<tr>
<td data-quarto-table-cell-role="th">0</td>
<td>CD8A</td>
<td>CD8A</td>
</tr>
</tbody>
</table>

</div>

Generate json files first:

``` python
for idx, row in tqdm(df.iterrows(),total=len(df)):
    json_data = get_multi_protein_json([row['Gene1'], row['Gene2']], 
                             a3m_dir=f'af_input/{project_name}/a3m', 
                             save_folder=f'af_input/{project_name}')
```

    100%|██████████| 1/1 [00:00<00:00, 147.81it/s]

Split them to subfolder:

``` python
split_nfolder(f'af_input/{project_name}')
```

    Distributed 1 files into 4 folders.

## Docker

Todo: Pair proteins

``` python
for i in range(4):
    get_docker_command(input_dir=f"af_input/{project_name}/folder_{i}",
                       output_dir=f"af_output/{project_name}",
                       gpus=i)
```

## End

## Utils

``` python
# #| export
# def split_files_into_subfolders(input_folder: str, nfolder: int = 4):
    
#     "Splits `.a3m` files in a folder into subfolders (folder_0, folder_1, ..., folder_N)."
    
#     input_path = Path(input_folder)
#     if not input_path.is_dir():
#         raise ValueError(f"Input folder {input_folder} does not exist or is not a directory.")

#     # List all `.a3m` files
#     a3m_files = sorted(input_path.glob("*.a3m"))
#     if not a3m_files:
#         print("No `.a3m` files found in the input folder.")
#         return

#     # Create the subfolders
#     subfolders = [input_path / f"folder_{i}" for i in range(nfolder)]
#     for folder in subfolders:
#         folder.mkdir(exist_ok=True)

#     # Distribute the files into the subfolders
#     for idx, file in enumerate(a3m_files):
#         target_folder = subfolders[idx % nfolder]
#         shutil.move(str(file), target_folder / file.name)

#     print(f"Distributed {len(a3m_files)} files into {nfolder} folders.")
```