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GLINTER: Graph Learning of INTER-protein contacts

Installation

Require python>=3.7

Clone the repository and install it

git clone https://github.com/zw2x/glinter.git
cd glinter
pip install -e .

Manually install the following softwares and models

The taxonomy database can be found at DOI

Usage

The following commands are executed in the repository glinter.

Replace the environment variables in set_env.sh and run.sh in the scripts directory by your installation paths and then run

source scripts/set_env.sh

There are two example pdb files in the examples directory. To predict the inter-protein heterodimeric contacts between 1a59A.pdb and 1a59B.pdb, run

scripts/build_hetero.sh examples/1a59A.pdb examples/1a59B.pdb examples/

To predict the inter-protein homodimeric contacts between 1a59A.pdb and 1a59B.pdb, and use 1a59B as the representative monomer (only build MSA for 1a59B), run

scripts/build_homo.sh examples/1a59A.pdb examples/1a59B.pdb examples/ 1a59B

Make sure the names of the receptor and ligand are different.

For heterodimers, the raw outputs are examples/1a59A:1a59B/1a59A:1a59B.out.pkl and examples/1a59A:1a59B/1a59B:1a59A.out.pkl (both directions). For homodimers, the raw output is examples/1a59A:1a59B/1a59A:1a59B.out.pkl.

For both cases, the final score (the probability that a pair of residue positions is in contact) is examples/1a59A:1a59B/score_mat.pkl. The ranked list of residue position pairs is examples/1a59A:1a59B/ranked_pairs.txt; the first column is the positions in the receptor, the second column is the positions in the ligand, the third column is the probability.

Integration with AlphaFold-Multimer

See alphafold/example_run.sh for using GLINTER given the predicted structures from AlphaFold.

Note

Please use the uniclust database A3M_SpecBloc requires the header of each hit starts with tr| and contains OS=$TAX, $TAX is the taxonomy name.

Please use the model provided in the repo ckpts/glinter1.pt instead of the one from zenodo

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