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 Transformers Models.
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ProtTrans
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.
📈 Benchmark:
Please check:
Benchmark Section. More information coming soon.
📊 Expected Results
🧬 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
60
66
61
ProtElectra-Discriminator
62
69
65
ProtTXL
59
64
59
ProtTXL-BFD
60
65
60
🧬 Membrane-bound vs Water-soluble (Q2):
Model
DeepLoc (FE)
DeepLoc (FT)
ProtBert-BFD
89
91
ProtBert
89
91
ProtAlbert
88
comming soon
ProtXLNet
87
comming soon
ProtElectra-Generator
85
comming soon
ProtElectra-Discriminator
86
comming soon
ProtTXL
85
comming soon
ProtTXL-BFD
86
comming soon
🧬 Subcellular Localization (Q10):
Model
DeepLoc (FE)
DeepLoc (FT)
ProtBert-BFD
74
78
ProtBert
74
79
ProtAlbert
74
comming soon
ProtXLNet
68
comming soon
ProtElectra-Generator
59
comming soon
ProtElectra-Discriminator
70
comming soon
ProtTXL
66
comming soon
ProtTXL-BFD
65
comming soon
❤️ Community and Contributions
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.
📫 Have a question?
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
🤝 Found a bug?
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!.
✅ Requirements
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.
🤵 Team
Technical University of Munich:
Ahmed Elnaggar
Michael Heinzinger
Christian Dallago
Ghalia Rehawi
Burkhard Rost
Med AI Technology:
Yu Wang
Google:
Llion Jones
Nvidia:
Tom Gibbs
Tamas Feher
Christoph Angerer
Seoul National University:
Martin Steinegger
ORNL:
Debsindhu Bhowmik
💰 Sponsors
Nvidia
Google
Google
ORNL
Software Campus
📘 License
The ProtTrans pretrained models are released under the under terms of the MIT License.
✏️ Citation
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 Rehawi, Ghalia and Wang, Yu and Jones, Llion and Gibbs, Tom and Feher, Tamas and Angerer, Christoph and Steinegger, Martin 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 = {Computational biology and bioinformatics provide vast data gold-mines from protein sequences, ideal for Language Models (LMs) taken from Natural Language Processing (NLP). These LMs reach for new prediction frontiers at low inference costs. Here, we trained two auto-regressive language models (Transformer-XL, XLNet) and two auto-encoder models (Bert, Albert) on data from UniRef and BFD containing up to 393 billion amino acids (words) from 2.1 billion protein sequences (22- and 112 times the entire English Wikipedia). The LMs were trained on the Summit supercomputer at Oak Ridge National Laboratory (ORNL), using 936 nodes (total 5616 GPUs) and one TPU Pod (V3-512 or V3-1024). We validated the advantage of up-scaling LMs to larger models supported by bigger data by predicting secondary structure (3-states: Q3=76-84, 8 states: Q8=65-73), sub-cellular 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 unlabeled data (only protein sequences) captured important biophysical properties governing protein shape. This implied learning some of the grammar of the language of life realized in protein sequences. The successful up-scaling of protein LMs through HPC to larger data sets slightly reduced the gap between models trained on evolutionary information and LMs. Availability ProtTrans: \<a href="https://github.com/agemagician/ProtTrans"\>https://github.com/agemagician/ProtTrans\</a\>Competing Interest StatementThe authors have declared no competing interest.},
URL = {https://www.biorxiv.org/content/early/2020/07/21/2020.07.12.199554},
eprint = {https://www.biorxiv.org/content/early/2020/07/21/2020.07.12.199554.full.pdf},
journal = {bioRxiv}
}
