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update documentation #7

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update README.md
Mar 22, 2024
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support custom chain_ids
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76 changes: 47 additions & 29 deletions README.md
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@@ -1,7 +1,10 @@
[![build](https://github.com/haddocking/DeepRank-GNN-esm/actions/workflows/build.yml/badge.svg)](https://github.com/haddocking/DeepRank-GNN-esm/actions/workflows/build.yml)

# DeepRank-GNN-esm
Graph Network for protein-protein interface including language model features

Graph Network for protein-protein interface including language model features.

For details refer to our publication at https://academic.oup.com/bioinformaticsadvances/article/4/1/vbad191/7511844

## Installation

Expand Down Expand Up @@ -36,14 +39,16 @@ $ pytest tests/

### As a scoring function

We provide a command-line interface for DeepRank-GNN-ESM that can be used to score protein-protein complexes. The command-line interface can be used as follows:
We provide a command-line interface for DeepRank-GNN-esm that can easily be used to score protein-protein complexes. The command-line interface can be used as follows:

```bash
$ deeprank-gnn-esm-predict -h
usage: deeprank-gnn-esm-predict [-h] pdb_file
$ deeprank-gnn-esm-predict -h
usage: deeprank-gnn-esm-predict [-h] pdb_file chain_id_1 chain_id_2

positional arguments:
pdb_file Path to the PDB file.
chain_id_1 First chain ID.
chain_id_2 Second chain ID.

optional arguments:
-h, --help show this help message and exit
Expand All @@ -57,59 +62,70 @@ $ wget https://files.rcsb.org/view/1B6C.pdb -q

# make sure the environment is activated
$ conda activate deeprank-gnn-esm-gpu-env
(deeprank-gnn-esm-gpu-env) $ deeprank-gnn-esm-predict 1B6C.pdb
2023-06-28 06:08:21,889 predict:64 INFO - Setting up workspace - /home/DeepRank-GNN-esm/1B6C-gnn_esm_pred
(deeprank-gnn-esm-gpu-env) $ deeprank-gnn-esm-predict 1B6C.pdb A B
2023-06-28 06:08:21,889 predict:64 INFO - Setting up workspace - /home/DeepRank-GNN-esm/1B6C-gnn_esm_pred_A_B
2023-06-28 06:08:21,945 predict:72 INFO - Renumbering PDB file.
2023-06-28 06:08:22,294 predict:104 INFO - Reading sequence of PDB 1B6C.pdb
2023-06-28 06:08:22,423 predict:131 INFO - Generating embedding for protein sequence.
2023-06-28 06:08:22,423 predict:132 INFO - ################################################################################
2023-06-28 06:08:32,447 predict:138 INFO - Transferred model to GPU
2023-06-28 06:08:32,450 predict:147 INFO - Read /home/DeepRank-GNN-esm/1B6C-gnn_esm_pred/all.fasta with 8 sequences
2023-06-28 06:08:32,459 predict:157 INFO - Processing 1 of 2 batches (4 sequences)
2023-06-28 06:08:34,061 predict:157 INFO - Processing 2 of 2 batches (4 sequences)
2023-06-28 06:08:32,450 predict:147 INFO - Read /home/1B6C-gnn_esm_pred_A_B/all.fasta with 2 sequences
2023-06-28 06:08:32,459 predict:157 INFO - Processing 1 of 1 batches (2 sequences)
2023-06-28 06:08:36,462 predict:200 INFO - ################################################################################
2023-06-28 06:08:36,470 predict:205 INFO - Generating graph, using 79 processors
Graphs added to the HDF5 file
Embedding added to the /home/DeepRank-GNN-esm/1B6C-gnn_esm_pred/graph.hdf5 file
2023-06-28 06:09:03,345 predict:220 INFO - Graph file generated: /home/DeepRank-GNN-esm/1B6C-gnn_esm_pred/graph.hdf5
Embedding added to the /home/1B6C-gnn_esm_pred_A_B/graph.hdf5 file file
2023-06-28 06:09:03,345 predict:220 INFO - Graph file generated: /home/DeepRank-GNN-esm/1B6C-gnn_esm_pred_A_B/graph.hdf5
2023-06-28 06:09:03,345 predict:226 INFO - Predicting fnat of protein complex.
2023-06-28 06:09:03,345 predict:234 INFO - Using device: cuda:0
# ...
2023-06-28 06:09:07,794 predict:280 INFO - Predicted fnat for 1B6C: 0.342
2023-06-28 06:09:07,803 predict:290 INFO - Output written to /home/DeepRank-GNN-esm/1B6C-gnn_esm_pred/output.csv
2023-06-28 06:09:07,794 predict:280 INFO - Predicted fnat for 1B6C between chainA and chainB: 0.359
2023-06-28 06:09:07,803 predict:290 INFO - Output written to /home/DeepRank-GNN-esm/1B6C-gnn_esm_pred/GNN_esm_prediction.csv
```

From the output above you can see that the predicted fnat for the 1B6C complex is **0.342**, this information is also written to the `output.csv` file.
From the output above you can see that the predicted fnat for the 1B6C complex is **0.359**, this information is also written to the `GNN_esm_prediction.csv` file.

The command above will generate a folder in the current working directory, containing the following:

```
1B6C-gnn_esm_pred
├── 1B6C.A.pt
├── 1B6C.B.pt
├── 1B6C.pdb
├── GNN_esm_prediction.csv
├── GNN_esm_prediction.hdf5
├── graph.hdf5
└── output.csv
1B6C-gnn_esm_pred_A_B
├── 1B6C.pdb #input pdb file
├── all.fasta #fasta sequence for the pdb input
├── 1B6C.A.pt #esm-2 embedding for chainA in protein 1B6C
├── 1B6C.B.pt #esm-2 embedding for chainB in protein 1B6C
├── graph.hdf5 #input protein graph in hdf5 format
├── GNN_esm_prediction.hdf5 #prediction output in hdf5 format
└── GNN_esm_prediction.csv #prediction output in csv format
```

* * *
### As a framework

### Note about input pdb files

To ensure the mapping between interface residue and esm-2 embeddings is correct, make sure that for all the chains, residue numbering in the PDB file is continuous and starts with residue '1'.

We provide a script (scripts/pdb_renumber.py) to do the numbering.


#### Generate esm-2 embeddings for your protein

#### Generate ems-2 embeddings for your protein
1. Generate fasta sequence in bulk, use script 'get_fasta.py'
1. To generate fasta sequences from PDBs, use script 'get_fasta.py'
```bash
usage: get_fasta.py [-h] pdb_dir output_fasta_name
usage: get_fasta.py [-h] pdb_file_path chain_id1 chain_id2

positional arguments:
pdb_dir Path to the directory containing PDB files
output_fasta_name Name of the combined output FASTA file
pdb_file_path Path to the directory containing PDB files
chain_id1 Chain ID for the first sequence
chain_id2 Chain ID for the second sequence

options:
-h, --help show this help message and exit
```


python scripts/get_fasta.py tests/data/pdb/1ATN/ A B


2. Generate embeddings in bulk from combined fasta files, use the script provided inside esm-2 package,

```bash
Expand All @@ -123,6 +139,7 @@ The command above will generate a folder in the current working directory, conta
Replace 'esm_2_installation_location' with your installation location, 'all.fasta' with fasta sequence generated above, 'tests/data/embedding/1ATN/' with the output folder name for esm embeddings

#### Generate graph

* Example code to generate residue graphs in hdf5 format:
```python
from deeprank_gnn.GraphGenMP import GraphHDF5
Expand Down Expand Up @@ -157,6 +174,7 @@ The command above will generate a folder in the current working directory, conta
```

#### Use pre-trained models to predict

* Example code to use pre-trained DeepRank-GNN-esm model
```python
from deeprank_gnn.ginet import GINet
Expand All @@ -167,7 +185,7 @@ The command above will generate a folder in the current working directory, conta
target = "fnat"
edge_attr = ["dist"]
threshold = 0.3
pretrained_model = deeprank-GNN-esm/paper_pretrained_models/scoring_of_docking_models/gnn_esm/treg_yfnat_b64_e20_lr0.001_foldall_esm.pth.tar
pretrained_model = 'deeprank-GNN-esm/paper_pretrained_models/scoring_of_docking_models/gnn_esm/treg_yfnat_b64_e20_lr0.001_foldall_esm.pth.tar'
node_feature = ["type", "polarity", "bsa", "charge", "embedding"]
device_name = "cuda:0"
num_workers = 10
Expand Down
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