MetalDiagnosis

MetalDiagnosis combines an improved equivariant graph neural network and protein pre-trained language model ESMC to predict disease-related mutation sites in metal binding proteins.

Step 1: Clone the GitHub repository

git clone https://github.com/MetalDiagnosis
cd MetalDiagnosis

Step 2: Build required dependencies

It is recommended to use Anaconda to install PyTorch, PyTorch Geometrics and other required Python libraries.

source install.sh

Step 3: Download required software

First, Using API to extract ESMC embeddings online at (https://github.com/evolutionaryscale/esm#esm-c-forge-) using ESMC_embedding_extract.ipynb.

Then constructing protein graph and extracting node features from graphein(https://github.com/a-r-j/graphein) using construct_graph&node_feature.ipynb.

Step 4: Running MetalDiagnosis on the independent test set.

python test.py -i test_dataset.pt 

Other

We provide prediction results for an independent test set and 611 sites with uncertain significance.