https://github.com/QData/DeepVHPPI
Motif Transformers for Predicting Protein-Protein Interactions Between a Novel Virus and Humans
https://github.com/QData/DeepVHPPI
Last synced: 10 months ago
JSON representation
Motif Transformers for Predicting Protein-Protein Interactions Between a Novel Virus and Humans
- Host: GitHub
- URL: https://github.com/QData/DeepVHPPI
- Owner: QData
- Created: 2020-11-25T17:35:36.000Z (about 5 years ago)
- Default Branch: main
- Last Pushed: 2024-12-04T16:57:49.000Z (about 1 year ago)
- Last Synced: 2025-03-21T00:41:19.560Z (10 months ago)
- Language: Python
- Size: 872 KB
- Stars: 9
- Watchers: 3
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-virome - DeepVHPPI - Deep learning for virus-host protein-protein interactions. [Python] (Other Tools / Interaction Analysis)
README
### Title: Transfer Learning for Predicting Virus-Host Protein Interactions for Novel Virus Sequences
+ authors: Jack Lanchantin, Tom Weingarten, Arshdeep Sekhon, Clint Miller, Yanjun Qi
+ 2021 ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM BCB)
### PDF
- @[BioArxiv](https://www.biorxiv.org/content/10.1101/2020.12.14.422772v2)
- @[ACM](https://dl.acm.org/doi/abs/10.1145/3459930.3469527)
- GitHub [https://github.com/QData/DeepVHPPI](https://github.com/QData/DeepVHPPI)
### Talk: [Slide](https://docs.google.com/presentation/d/1LfSVsZ2hSy7F-AVXt1WIUfT2Qsw-db7VITZhmifAniQ/edit)
### Abstract
Viruses such as SARS-CoV-2 infect the human body by forming interactions between virus proteins and human proteins. However, experimental methods to find protein interactions are inadequate: large scale experiments are noisy, and small scale experiments are slow and expensive. Inspired by the recent successes of deep neural networks, we hypothesize that deep learning methods are well-positioned to aid and augment biological experiments, hoping to help identify more accurate virus-host protein interaction maps. Moreover, computational methods can quickly adapt to predict how virus mutations change protein interactions with the host proteins.
We propose DeepVHPPI, a novel deep learning framework combining a self-attention-based transformer architecture and a transfer learning training strategy to predict interactions between human proteins and virus proteins that have novel sequence patterns. We show that our approach outperforms the state-of-the-art methods significantly in predicting Virus–Human protein interactions for SARS-CoV-2, H1N1, and Ebola. In addition, we demonstrate how our framework can be used to predict and interpret the interactions of mutated SARS-CoV-2 Spike protein sequences.
We make all of our data and code available on GitHub [https://github.com/QData/DeepVHPPI](https://github.com/QData/DeepVHPPI).
### Citations
```
@article {Lanchantin2020.12.14.422772,
author = {Lanchantin, Jack and Weingarten, Tom and Sekhon, Arshdeep and Miller, Clint and Qi, Yanjun},
title = {Transfer Learning for Predicting Virus-Host Protein Interactions for Novel Virus Sequences},
elocation-id = {2020.12.14.422772},
year = {2021},
doi = {10.1101/2020.12.14.422772},
publisher = {Cold Spring Harbor Laboratory},
abstract = {Viruses such as SARS-CoV-2 infect the human body by forming interactions between virus proteins and human proteins. However, experimental methods to find protein interactions are inadequate: large scale experiments are noisy, and small scale experiments are slow and expensive. Inspired by the recent successes of deep neural networks, we hypothesize that deep learning methods are well-positioned to aid and augment biological experiments, hoping to help identify more accurate virus-host protein interaction maps. Moreover, computational methods can quickly adapt to predict how virus mutations change protein interactions with the host proteins.We propose DeepVHPPI, a novel deep learning framework combining a self-attention-based transformer architecture and a transfer learning training strategy to predict interactions between human proteins and virus proteins that have novel sequence patterns. We show that our approach outperforms the state-of-the-art methods significantly in predicting Virus{\textendash}Human protein interactions for SARS-CoV-2, H1N1, and Ebola. In addition, we demonstrate how our framework can be used to predict and interpret the interactions of mutated SARS-CoV-2 Spike protein sequences.Availability We make all of our data and code available on GitHub https://github.com/QData/DeepVHPPI.ACM Reference Format Jack Lanchantin, Tom Weingarten, Arshdeep Sekhon, Clint Miller, and Yanjun Qi. 2021. Transfer Learning for Predicting Virus-Host Protein Interactions for Novel Virus Sequences. In Proceedings of ACM Conference (ACM-BCB). ACM, New York, NY, USA, 10 pages. https://doi.org/??Competing Interest StatementThe authors have declared no competing interest.},
URL = {https://www.biorxiv.org/content/early/2021/06/08/2020.12.14.422772},
eprint = {https://www.biorxiv.org/content/early/2021/06/08/2020.12.14.422772.full.pdf},
journal = {bioRxiv}
}
```
# How to get the data
```
wget https://www.cs.virginia.edu/~yq2h/jack/ppi/deepvhppi.tar.gz
tar -xvf deepvhppi.tar.gz
```
# How to run the code
**SARS-CoV-2 PPI**
```
CUDA_VISIBLE_DEVICES=0,1,2,3 python main.py --data_root ./data/ -tr yang/train.json -va yang/test.json -te HVPPI/test.json -v vocab.data -s 1024 -hs 512 -l 12 -o results --lr 0.00001 --dropout 0.1 --epochs 200 --attn_heads 8 --activation 'gelu' --task biogrid --emb_type 'conv' --overwrite --batch_size 4 --grad_ac_steps 4
```
**ZHOU PPI**
```
CUDA_VISIBLE_DEVICES=0,1,2,3 python main.py --data_root ./data/ -tr zhou/h1n1/human/train.json -va zhou/h1n1/human/test.json -v vocab.data -s 1024 -hs 512 -l 12 -o results --lr 0.00001 --dropout 0.1 --epochs 20000 --attn_heads 8 --activation 'gelu' --task ppi --emb_type 'conv' --overwrite --batch_size 8 --grad_ac_steps 2 --name ''
```
**BARMAN PPI**
```
CUDA_VISIBLE_DEVICES=0,1,2,3 python main.py --data_root ./data/ -tr barman/train1.json -va barman/test1.json -v vocab.data -s 1600 -hs 512 -l 12 -o results --lr 0.00001 --dropout 0.1 --epochs 200 --attn_heads 8 --activation 'gelu' --task ppi --emb_type 'conv' --overwrite --batch_size 4 --grad_ac_steps 4
```
**DeNovo SLIM PPI**
```
CUDA_VISIBLE_DEVICES=0,1,2,3 python main.py --data_root ./data/ -tr denovo/train.json -va denovo/test.json -v vocab.data -s 1024 -hs 512 -l 12 -o results --lr 0.00001 --dropout 0.1 --epochs 20000 --attn_heads 8 --activation 'gelu' --task ppi --emb_type 'conv' --overwrite --batch_size 8 --grad_ac_steps 2 --name '' --saved_bert ./results/multi.bert.bsz_16.layers_12.size_512.heads_8.drop_10.lr_1e-05.saved_bert.torch/best_model.pt
```
### Support or Contact
Having trouble with our tools? Please [contact Jack](mailto:jacklanchantin@gmail.com) and we’ll help you sort it out.