ProtTrans is providing state of the art pretrained language models for proteins. ProtTrans was trained on thousands of GPUs from Summit and hundreds of Google TPUs using Transformers Models.
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
ProtT5-XL-UniRef50
81
87
86
ProtT5-XL-BFD
77
85
84
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
ProtT5-XL-UniRef50
70
77
74
ProtT5-XL-BFD
66
74
71
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
ProtT5-XL-UniRef50
91
ProtT5-XL-BFD
91
ProtBert-BFD
89
ProtBert
89
ProtAlbert
88
ProtXLNet
87
ProtElectra-Generator
85
ProtElectra-Discriminator
86
ProtTXL
85
ProtTXL-BFD
86
🧬 Subcellular Localization (Q10):
Model
DeepLoc
ProtT5-XL-UniRef50
81
ProtT5-XL-BFD
77
ProtBert-BFD
74
ProtBert
74
ProtAlbert
74
ProtXLNet
68
ProtElectra-Generator
59
ProtElectra-Discriminator
70
ProtTXL
66
ProtTXL-BFD
65
❤️ 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.
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 = {2021},
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 taken from NLP. These LMs reach for new prediction frontiers at low inference costs. Here, we trained two auto-regressive models (Transformer-XL, XLNet) and four auto-encoder models (BERT, Albert, Electra, T5) on data from UniRef and BFD containing up to 393 billion amino acids. The LMs were trained on the Summit supercomputer using 5616 GPUs and TPU Pod up-to 1024 cores. Dimensionality reduction revealed that the raw protein LM-embeddings from unlabeled data captured some biophysical features of protein sequences. We validated the advantage of using the embeddings as exclusive input for several subsequent tasks. The first was a per-residue prediction of protein secondary structure (3-state accuracy Q3=81\%-87\%); the second were per-protein predictions of protein sub-cellular localization (ten-state accuracy: Q10=81\%) and membrane vs. water-soluble (2-state accuracy Q2=91\%). For the per-residue predictions the transfer of the most informative embeddings (ProtT5) for the first time outperformed the state-of-the-art without using evolutionary information thereby bypassing expensive database searches. Taken together, the results implied that protein LMs learned some of the grammar of the language of life. To facilitate future work, we released our models at \<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/2021/05/04/2020.07.12.199554},
eprint = {https://www.biorxiv.org/content/early/2021/05/04/2020.07.12.199554.full.pdf},
journal = {bioRxiv}
}
About
ProtTrans is providing state of the art pretrained language models for proteins. ProtTrans was trained on thousands of GPUs from Summit and hundreds of Google TPUs using Transformers Models.
