Graph Network for protein-protein interface including language model features

With Anaconda

1. Clone the repository

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

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

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

OR

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

3. Run the tests to make sure everything is working

$ pytest tests/

• Step1. Generate fasta sequence in bulk, use script 'get_fasta.py'

usage: get_fasta.py [-h] pdb_dir output_fasta_name

positional arguments:
  pdb_dir            Path to the directory containing PDB files
  output_fasta_name  Name of the combined output FASTA file

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

• Step2. 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

• 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()
  

• 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")