This repository is no longer being maintained and has been archived for historical purposes.
There is a new version called DeepRank-GNN-esm which incorporates the ESM embeddings as replacement of the PSSM profile as features. This version is accessible from a separate repository here. Next to providing ESM features, it has the same functionalities as the original DeepRank-GNN version. For details refer to the following publication.
Next to that, there is a new DeepRank2 version, an improved and unified version of DeepRank-GNN, DeepRank, and DeepRank-Mut.
✨ DeepRank2 allows for transformation and storage of 3D representations of both protein-protein interfaces (PPIs) and protein single-residue variants (SRVs) into either graphs or volumetric grids containing structural and physico-chemical information. These can be used for training neural networks for a variety of patterns of interest, using either our pre-implemented training pipeline for graph neural networks (GNNs) or convolutional neural networks (CNNs) or external pipelines.
- 🔧 Pull Requests at github.com/DeepRank/deeprank2/pulls
- 🐛 Bugs: Reports of bugs can be filed agains our new repo github.com/DeepRank/deeprank2/issues
- ⭐ Feature Requests: Add your request or discuss the project w/ the community at github.com/DeepRank/deeprank2/issues
We look forward to seeing you in our new space - DeepRank2!
Before installing DeepRank-GNN you need to install pytorch_geometric according to your needs. You can find detailled instructions here :
- pytorch_geometric : https://pytorch-geometric.readthedocs.io/en/latest/notes/installation.html
By default the CPU version of pytorch will be installed but you can also customize that installation following the instructions at:
- pytorch : https://pytorch.org/
Once the dependencies installed, you can install the latest release of DeepRank-GNN using the PyPi package manager:
pip install DeepRank-GNN Alternatively you can get all the new developments by cloning the repo and installing the code with
git clone https://github.com/DeepRank/Deeprank-GNN cd DeepRank-GNN pip install -e ./ The documentation can be found here : https://deeprank-gnn.readthedocs.io/
All the graphs/line graphs of all the pdb/pssm stored in data/pdb/ and data/pssm/ with the GenGraph.py script. This will generate the hdf5 file graph_residue.hdf5 which contains the graph of the different conformations.
from GraphGenMP import GraphHDF5 pdb_path = './data/pdb' pssm_path = './data/pssm' ref = './data/ref' GraphHDF5(pdb_path=pdb_path,ref_path=ref,pssm_path=pssm_path, graph_type='residue',outfile='graph_residue.hdf5')Using the graph interaction network is rather simple :
from deeprank_gnn.NeuralNet import NeuralNet from deeprank_gnn.ginet import GINet database = './hdf5/1ACB_residue.hdf5' NN = NeuralNet(database, GINet, node_feature=['type', 'polarity', 'bsa', 'depth', 'hse', 'ic', 'pssm'], edge_feature=['dist'], target='irmsd', index=range(400), batch_size=64, percent=[0.8, 0.2]) NN.train(nepoch=250, validate=False) NN.plot_scatter()It is also possible to define new network architecture and to specify the loss and optimizer to be used during the training.
def normalized_cut_2d(edge_index, pos): row, col = edge_index edge_attr = torch.norm(pos[row] - pos[col], p=2, dim=1) return normalized_cut(edge_index, edge_attr, num_nodes=pos.size(0)) class CustomNet(torch.nn.Module): def __init__(self): super(Net, self).__init__() self.conv1 = SplineConv(d.num_features, 32, dim=2, kernel_size=5) self.conv2 = SplineConv(32, 64, dim=2, kernel_size=5) self.fc1 = torch.nn.Linear(64, 128) self.fc2 = torch.nn.Linear(128, 1) def forward(self, data): data.x = F.elu(self.conv1(data.x, data.edge_index, data.edge_attr)) weight = normalized_cut_2d(data.edge_index, data.pos) cluster = graclus(data.edge_index, weight) data = max_pool(cluster, data) data.x = F.elu(self.conv2(data.x, data.edge_index, data.edge_attr)) weight = normalized_cut_2d(data.edge_index, data.pos) cluster = graclus(data.edge_index, weight) x, batch = max_pool_x(cluster, data.x, data.batch) x = scatter_mean(x, batch, dim=0) x = F.elu(self.fc1(x)) x = F.dropout(x, training=self.training) return F.log_softmax(self.fc2(x), dim=1) device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') model = NeuralNet(database, CustomNet, node_feature=['type', 'polarity', 'bsa', 'depth', 'hse', 'ic', 'pssm'], edge_feature=['dist'], target='irmsd', index=range(400), batch_size=64, percent=[0.8, 0.2]) model.optimizer = torch.optim.Adam(model.parameters(), lr=0.01) model.loss = MSELoss() model.train(nepoch=50)After installing h5xplorer (https://github.com/DeepRank/h5xplorer), you can execute the python file deeprank_gnn/h5x/h5x.py to explorer the connection graph used by DeepRank-GNN. The context menu (right click on the name of the structure) allows to automatically plot the graphs using plotly as shown below.
