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CrossBind

CrossBind: Collaborative Cross-Modal Identification of Protein Nucleic-Acid-Binding Residues

Codes of protein residue binding prediction.

Getting Started

Setup

(1) Pytorch version.

conda install pytorch==1.4.0 cudatoolkit=10.1 -c pytorch

(2) Compile SparseConvNet ops.

cd lib/
python setup.py develop

Data Preparation

(1) You can download the dataset (DNA/RNA pdb files) (http://www.csbio.sjtu.edu.cn/bioinf/GraphBind/ , https://github.com/biomed-AI/GraphSite)

(2) To transfer original PDB files to XYZ files, you need prepare LIG_TOOL.

git clone https://github.com/realbigws/PDB_Tool.git

(3) Modify the path in datasets/prepare_pdb_to_xyz.py and run

cd datasets/
python prepare_pdb_to_xyz.py

Cross model

(1) Load pre-trained structure representation DNA_127

/models/DNA_127_Structure.pkl

DNA_181

/models/Test_181.pkl

(2) Load ESM2 representation The details are described in (https://github.com/facebookresearch/esm)

Training

Fine tune the CrossBind modal Before the training phase, you can modify your model setting in cfgs/*yaml, and choose the yaml file you want to use. Run full version of CrossBind:

python CrossBind.py --log_dir SparseConv_default --cfg_file cfgs/SparseConv-Cath-Decoys-Clf-Only.yaml --gpu 0

Visualization Case

Figure_case

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