Awesome

DeepRank-GNN-esm

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

For detailed protocol to use our DeepRank-GNN-esm software, refer to our publication at https://arxiv.org/abs/2407.16375

Installation

With Anaconda

Clone the repository

$ git clone https://github.com/DeepRank/DeepRank-GNN-esm.git
$ cd DeepRank-GNN-esm

Install either the CPU or GPU version of DeepRank-GNN-esm

$ conda env create -f environment-cpu.yml && conda activate deeprank-gnn-esm-cpu

$ conda env create -f environment-gpu.yml && conda activate deeprank-gnn-esm-gpu

Install the command line tool

$ pip install .

Run the tests to make sure everything is working

$ pytest tests/

Usage

As a scoring function

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:

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

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

optional arguments:
  -h, --help  show this help message and exit

Example, score the 1B6C complex

# download it
$ 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) $ export MODEL=../paper_pretrained_models/scoring_of_docking_models/gnn_esm/treg_yfnat_b64_e20_lr0.001_foldall_esm.pth.tar
(deeprank-gnn-esm-gpu) $ deeprank-gnn-esm-predict 1B6C.pdb A B $MODEL
 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/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/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 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 0.359, 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_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

To generate fasta sequences from PDBs, use script 'get_fasta.py'

usage: get_fasta.py [-h] pdb_file_path chain_id1 chain_id2

positional arguments:
  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

Generate embeddings in bulk from combined fasta files, use the script provided inside esm-2 package,
```
$ python esm_2_installation_location/scripts/extract.py \
    esm2_t33_650M_UR50D \
    all.fasta \
    tests/data/embedding/1ATN/ \
    --repr_layers 0 32 33 \
    --include mean per_tok
```
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:

from deeprank_gnn.GraphGenMP import GraphHDF5

pdb_path = "tests/data/pdb/1ATN/"
pssm_path = "tests/data/pssm/1ATN/"
embedding_path = "tests/data/embedding/1ATN/"
nproc = 20
outfile = "1ATN_residue.hdf5"

GraphHDF5(
    pdb_path = pdb_path,
    pssm_path = pssm_path,
    embedding_path = embedding_path,
    graph_type = "residue",
    outfile = outfile,
    nproc = nproc,    #number of cores to use
    tmpdir="./tmpdir")

Example code to add continuous or binary targets to the hdf5 file

import h5py
import random

hdf5_file = h5py.File('1ATN_residue.hdf5', "r+")
for mol in hdf5_file.keys():
    fnat = random.random()
    bin_class = [1 if fnat > 0.3 else 0]
    hdf5_file.create_dataset(f"/{mol}/score/binclass", data=bin_class)
    hdf5_file.create_dataset(f"/{mol}/score/fnat", data=fnat)
hdf5_file.close()

Use pre-trained models to predict

Example code to use pre-trained DeepRank-GNN-esm model

from deeprank_gnn.ginet import GINet
from deeprank_gnn.NeuralNet import NeuralNet

database_test = "1ATN_residue.hdf5"
gnn = GINet
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'
node_feature = ["type", "polarity", "bsa", "charge", "embedding"]
device_name = "cuda:0"
num_workers = 10

model = NeuralNet(
    database_test,
    gnn,
    device_name = device_name,
    edge_feature = edge_attr,
    node_feature = node_feature,
    target = target,
    num_workers = num_workers,
    pretrained_model = pretrained_model,
    threshold = threshold)

model.test(hdf5 = "tmpdir/GNN_esm_prediction.hdf5")