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https://github.com/phermosilla/IEConv_proteins
Intrinsic-Extrinsic Convolution and Pooling for Learning on 3D Protein Structures
https://github.com/phermosilla/IEConv_proteins
Last synced: 3 months ago
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Intrinsic-Extrinsic Convolution and Pooling for Learning on 3D Protein Structures
- Host: GitHub
- URL: https://github.com/phermosilla/IEConv_proteins
- Owner: phermosilla
- License: other
- Created: 2021-03-08T17:17:33.000Z (over 3 years ago)
- Default Branch: master
- Last Pushed: 2022-01-24T09:57:59.000Z (almost 3 years ago)
- Last Synced: 2024-06-29T07:47:27.794Z (4 months ago)
- Language: Python
- Size: 2.68 MB
- Stars: 47
- Watchers: 3
- Forks: 6
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
### Intrinsic-Extrinsic Convolution and Pooling for Learning on 3D Protein Structures
This is the official code of the ICRL 2021 paper *Intrinsic-Extrinsic Convolution and Pooling for Learning on 3D Protein Structures*.
![teaser](https://github.com/phermosilla/IEConv_proteins/blob/master/imgs/conv.png)
![teaser](https://github.com/phermosilla/IEConv_proteins/blob/master/imgs/pooling.png)
### Citation
If you find this code useful please consider citing us:
@article{hermosilla2021ieconv,
title={Intrinsic-Extrinsic Convolution and Pooling for Learning on 3D Protein Structures},
author={Hermosilla, Pedro and Schäfer, Marco and Lang, Matěj and Fackelmann, Gloria and Vázquez, Pere Pau and Kozlíková, Barbora and Krone, Michael and Ritschel, Tobias and Ropinski, Timo},
journal={International Conference on Learning Representations},
year={2021}
}### Instalation
Open a docker container with the following command:
sudo docker run --gpus all --privileged -it -v ${PWD}:/working_dir -w /working_dir tensorflow/tensorflow:1.12.0-devel-gpu-py3
Execute the following command to compile the custom ops of tensorflow:
cd IEProtLib/tf_ops
python genCompileScript.py --cudaFolder /usr/local/cuda
sh compile.shWe already provide a compiled version of the library for the docker container, so if you are using the docker container indicated above you can skip the compilation.
### Download the preprocessed datasets.
In the following links the different datasets can be downloaded:
* **Enzymes vs Non-Enzymes**:
https://drive.google.com/uc?export=download&id=1KTs5cUYhG60C6WagFp4Pg8xeMgvbLfhB
Extract content in: Datasets/data/ProteinsDD/
* **Scope 1.75**:
https://drive.google.com/uc?export=download&id=1chZAkaZlEBaOcjHQ3OUOdiKZqIn36qar
Extract content in: Datasets/data/HomologyTAPE/
* **Protein function**:
https://drive.google.com/uc?export=download&id=1udP6_90WYkwkvL1LwqIAzf9ibegBJ8rI
Extract content in: Datasets/data/ProtFunct
### Train Ennzymes vs Non-Enzymes
Execute the following commands to train a network on the task:
cd Tasks/ProteinsDD
python Train.py --configFile confs/train_fold0.ini
python Train.py --configFile confs/train_fold1.ini
python Train.py --configFile confs/train_fold2.ini
python Train.py --configFile confs/train_fold3.ini
python Train.py --configFile confs/train_fold4.ini
python Train.py --configFile confs/train_fold5.ini
python Train.py --configFile confs/train_fold6.ini
python Train.py --configFile confs/train_fold7.ini
python Train.py --configFile confs/train_fold8.ini
python Train.py --configFile confs/train_fold9.ini### Train SCOPe 1.75
Execute the following commands to train a network on the task:
cd Tasks/ProtHomology
python Train.py --configFile confs/train.iniTo evalute the trained model on the different test set use the following commands:
python Test.py --configFile confs/test_fold.ini
python Test.py --configFile confs/test_superfamily.ini
python Test.py --configFile confs/test_family.ini### Train Protein function prediction
Execute the following commands to train a network on the task:
cd Tasks/ProtFunct
python Train.py --configFile confs/train.iniTo evaluate the trained model execute:
python Test.py --configFile confs/test.ini
### Trained models comming soon