ProtTrans is providing state of the art pre-trained models for proteins. ProtTrans was trained on thousands of GPUs from Summit and hundreds of Google TPUs using various Transformers Models.
Have a look at our paper ProtTrans: cracking the language of life’s code through self-supervised deep learning and high performance computing for more information about our work.
This repository will be updated regulary with new pre-trained models for proteins as part of supporting bioinformatics community in general, and Covid-19 research specifically through our Accelerate SARS-CoV-2 research with transfer learning using pre-trained language modeling models project.
- ⌛️ Models Availability
- 🚀 Usage
- 📊 Expected Results
- ❤️ Community and Contributions
- 📫 Have a question?
- 🤝 Found a bug?
- ✅ Requirements
- 🤵 Team
- 💰 Sponsors
- 📘 License
- ✏️ Citation
| Model | Availability |
|---|---|
| ProtBert-BFD | coming soon |
| ProtBert | Public |
| ProtAlbert | Public |
| ProtXLNet | Public |
| ProtElectra-Generator | coming soon |
| ProtElectra-Discriminator | coming soon |
| ProtTXL | coming soon |
| ProtTXL-BFD | coming soon |
| ProtT5 | Training |
How to use ProtTrans:
- 🧬 Feature Extraction:
Please check: Embedding Section. More information coming soon.
- 🧐 Visualization:
Please check: Visualization Section. More information coming soon.
- 📈 Benchmark:
Please check: Benchmark Section. More information coming soon.
- 🧬 Secondary Structure Prediction (Q3):
| Model | CASP12 | TS115 | CB513 |
|---|---|---|---|
| ProtBert-BFD | 76 | 84 | 83 |
| ProtBert | 75 | 83 | 81 |
| ProtAlbert | 74 | 82 | 79 |
| ProtXLNet | 73 | 81 | 78 |
| ProtElectra-Generator | 73 | 78 | 76 |
| ProtElectra-Discriminator | 74 | 81 | 79 |
| ProtTXL | 71 | 76 | 74 |
| ProtTXL-BFD | 72 | 75 | 77 |
- 🧬 Secondary Structure Prediction (Q8):
| Model | CASP12 | TS115 | CB513 |
|---|---|---|---|
| ProtBert-BFD | 65 | 73 | 70 |
| ProtBert | 63 | 72 | 66 |
| ProtAlbert | 62 | 70 | 65 |
| ProtXLNet | 62 | 69 | 63 |
| ProtElectra-Generator | coming soon | coming soon | coming soon |
| ProtElectra-Discriminator | coming soon | coming soon | coming soon |
| ProtTXL | 59 | 64 | 59 |
| ProtTXL-BFD | 60 | 65 | 60 |
- 🧬 Membrane-bound vs Water-soluble (Q2):
| Model | DeepLoc |
|---|---|
| ProtBert-BFD | 89 |
| ProtBert | 89 |
| ProtAlbert | 88 |
| ProtXLNet | 87 |
| ProtElectra-Generator | coming soon |
| ProtElectra-Discriminator | coming soon |
| ProtTXL | 85 |
| ProtTXL-BFD | 86 |
- 🧬 Subcellular Localization (Q10):
| Model | DeepLoc |
|---|---|
| ProtBert-BFD | 74 |
| ProtBert | 74 |
| ProtAlbert | 74 |
| ProtXLNet | 68 |
| ProtElectra-Generator | coming soon |
| ProtElectra-Discriminator | coming soon |
| ProtTXL | 66 |
| ProtTXL-BFD | 65 |
The ProtTrans project is a open source project supported by various partner companies and research institutions. We are committed to share all our pre-trained models and knowledge. We are more than happy if you could help us on sharing new ptrained models, fixing bugs, proposing new feature, improving our documentation, spreading the word, or support our project.
We are happy to hear your question in our issues page ProtTrans! Obviously if you have a private question or want to cooperate with us, you can always reach out to us directly via our RostLab email
Feel free to file a new issue with a respective title and description on the the ProtTrans repository. If you already found a solution to your problem, we would love to review your pull request!.
For protein feature extraction or fine-tuninng our pre-trained models, Pytorch and Transformers library from huggingface is needed. For model visualization, you need to install BertViz library.
- Technical University of Munich:
| Ahmed Elnaggar | Michael Heinzinger | Christian Dallago | Ghalia Rihawi | Burkhard Rost |
|---|---|---|---|---|
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- Med AI Technology:
| Yu Wang |
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- Google:
| Llion Jones |
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- Nvidia:
| Tom Gibbs | Tamas Feher | Christoph Angerer |
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- ORNL:
| Debsindhu Bhowmik |
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| Nvidia | ORNL | Software Campus | |
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The ProtTrans pretrained models are released under the under terms of the MIT License.
If you use this code or our pretrained models for your publication, please cite the original paper:
@article {Elnaggar2020.07.12.199554,
author = {Elnaggar, Ahmed and Heinzinger, Michael and Dallago, Christian and Rihawi, Ghalia and Wang, Yu and Jones, Llion and Gibbs, Tom and Feher, Tamas and Angerer, Christoph and BHOWMIK, DEBSINDHU and Rost, Burkhard},
title = {ProtTrans: Towards Cracking the Language of Life{\textquoteright}s Code Through Self-Supervised Deep Learning and High Performance Computing},
elocation-id = {2020.07.12.199554},
year = {2020},
doi = {10.1101/2020.07.12.199554},
publisher = {Cold Spring Harbor Laboratory},
abstract = {Motivation: Natural Language Processing (NLP) continues improving substantially through auto-regressive (AR) and auto-encoding (AE) Language Models (LMs). These LMs require expensive computing resources for self-supervised or un-supervised learning from huge unlabelled text corpora. The information learned is transferred through so-called embeddings to downstream prediction tasks. Computational biology and bioinformatics provide vast gold-mines of structured and sequentially ordered text data leading to extraordinarily successful protein sequence LMs that promise new frontiers for generative and predictive tasks at low inference cost. As recent NLP advances link corpus size to model size and accuracy, we addressed two questions: (1) To which extent can High-Performance Computing (HPC) up-scale protein LMs to larger databases and larger models? (2) To which extent can LMs extract features from single proteins to get closer to the performance of methods using evolutionary information? Methodology: Here, we trained two auto-regressive language models (Transformer-XL and XLNet) and two auto-encoder models (BERT and Albert) on 80 billion amino acids from 200 million protein sequences (UniRef100) and one language model (Transformer-XL) on 393 billion amino acids from 2.1 billion protein sequences taken from the Big Fat Database (BFD), today{\textquoteright}s largest set of protein sequences (corresponding to 22- and 112-times, respectively of the entire English Wikipedia). The LMs were trained on the Summit supercomputer, using 936 nodes with 6 GPUs each (in total 5616 GPUs) and one TPU Pod, using V3-512 cores. Results: We validated the feasibility of training big LMs on proteins and the advantage of up-scaling LMs to larger models supported by more data. The latter was assessed by predicting secondary structure in three- and eight-states (Q3=75-83, Q8=63-72), localization for 10 cellular compartments (Q10=74) and whether a protein is membrane-bound or water-soluble (Q2=89). Dimensionality reduction revealed that the LM-embeddings from unlabelled data (only protein sequences) captured important biophysical properties of the protein alphabet, namely the amino acids, and their well orchestrated interplay in governing the shape of proteins. In the analogy of NLP, this implied having learned some of the grammar of the language of life realized in protein sequences. The successful up-scaling of protein LMs through HPC slightly reduced the gap between models trained on evolutionary information and LMs. Additionally, our results highlighted the importance of bi-directionality when processing proteins as the uni-directional TransformerXL was outperformed by its bi-directional counterparts;Competing Interest StatementThe authors have declared no competing interest.},
URL = {https://www.biorxiv.org/content/early/2020/07/12/2020.07.12.199554},
eprint = {https://www.biorxiv.org/content/early/2020/07/12/2020.07.12.199554.full.pdf},
journal = {bioRxiv}
}