# Protein pairs


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## Setup

``` python
from af_kit.core import *
from af_kit.protein_pairs import *
import pandas as pd
from tqdm import tqdm
```

``` bash
pip install "colabfold[alphafold] @ git+https://github.com/sokrypton/ColabFold"

## MSA

MSA can be run in cpu only server

Prepare a csv that have first column `id` and second column `sequence` of amino acid sequence

::: {.cell}
``` {.python .cell-code}
project_name='sdf'
```

:::

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

    Run below in terminal:

     colabfold_batch a.csv msa_sdf --msa-only

After finish, copy a3m files to a gpu available place

``` python
copy_a3m(a3m_dir=f'/teamspace/studios/alphfold3/msa_{project_name}',
         dest_dir=f'af_input/{project_name}/msa')
```

## JSON file

Read the file that contained id and sequence

``` python
df = pd.read_csv('file.csv')
```

``` python
protein_list = df['gene_id'].tolist()
```

``` python
df = generate_pair_df(protein_list)
```

``` 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}')
```

This will generate a number of json files in the save_folder.

We need to distribute them to nfolders for parallel running when
multiple gpus are available.

``` python
split_nfolder(f'af_input/{project_name}',n=4) # default n is 4
```

## Docker Command

``` bash
docker pull sky1ove/alphafold3
```

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

Run the printed command in your terminal

## Report for protein pairs

``` python
df_sum, top_genes = get_report(f"af_output/{project_name}",
                               save_dir=f'af_report/{project_name}')

df_sum.sort_values('iptm_ptm_rnk_add').head(10)
```

A 3d plot will be generated with
x=‘iptm’,y=‘ptm’,z=‘chain_pair_pae_min_add’

Top genes are: - Smallest 30 from ‘iptm_ptm_rnk_add’,
‘chain_pair_pae_min_add’, ‘chain_pair_pae_min_0_1’,
‘chain_pair_pae_min_1_0’, ‘iptm_pae_add_rnk’ - Largest 30 from
‘ranking_score’, ‘iptm’, ‘iptm_ptm_add’

df_sum contains the score for each metric

## Copy top protein structures to a local folder

``` python
from fastcore.utils import L
copy_file('proA_proB',source_dir='af_output/proA',dest_dir='af_top')

# Or 
L(top_genes).map(copy_file,source_dir='af_output/proA',dest_dir='af_top')
```

## Embeddings

To do