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

Awesome Lists containing this project

• StarryDivineSky - phermosilla/IEConv_proteins - 外在卷积和池化 (蛋白质结构 / 网络服务_其他)

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.sh

We 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.ini

To 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.ini

To evaluate the trained model execute:

    python Test.py --configFile confs/test.ini

### Trained models comming soon