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Title: proteingym · PyPI

Open Graph Title: proteingym

Description: ProteinGym: Large-Scale Benchmarks for Protein Design and Fitness Prediction

Open Graph Description: ProteinGym: Large-Scale Benchmarks for Protein Design and Fitness Prediction

Opengraph URL: https://pypi.org/project/proteingym/

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benchmarkshttps://github.com/OATML-Markslab/ProteinGym/tree/main/benchmarks
https://www.proteingym.org/https://www.proteingym.org/
Hopf, T.A., Ingraham, J., Poelwijk, F.J., Schärfe, C.P., Springer, M., Sander, C., & Marks, D.S. (2017). Mutation effects predicted from sequence co-variation. Nature Biotechnology, 35, 128-135.https://www.nature.com/articles/nbt.3769
Hopf, T.A., Ingraham, J., Poelwijk, F.J., Schärfe, C.P., Springer, M., Sander, C., & Marks, D.S. (2017). Mutation effects predicted from sequence co-variation. Nature Biotechnology, 35, 128-135.https://www.nature.com/articles/nbt.3769
Shin, J., Riesselman, A.J., Kollasch, A.W., McMahon, C., Simon, E., Sander, C., Manglik, A., Kruse, A.C., & Marks, D.S. (2021). Protein design and variant prediction using autoregressive generative models. Nature Communications, 12.https://www.nature.com/articles/s41467-021-22732-w
Riesselman, A.J., Ingraham, J., & Marks, D.S. (2018). Deep generative models of genetic variation capture the effects of mutations. Nature Methods, 15, 816-822.https://www.nature.com/articles/s41592-018-0138-4
Laine, É., Karami, Y., & Carbone, A. (2019). GEMME: A Simple and Fast Global Epistatic Model Predicting Mutational Effects. Molecular Biology and Evolution, 36, 2604 - 2619.https://pubmed.ncbi.nlm.nih.gov/31406981/
Frazer, J., Notin, P., Dias, M., Gomez, A.N., Min, J.K., Brock, K.P., Gal, Y., & Marks, D.S. (2021). Disease variant prediction with deep generative models of evolutionary data. Nature.https://www.nature.com/articles/s41586-021-04043-8
Alley, E.C., Khimulya, G., Biswas, S., AlQuraishi, M., & Church, G.M. (2019). Unified rational protein engineering with sequence-based deep representation learning. Nature Methods, 1-8https://www.nature.com/articles/s41592-019-0598-1
Rives, A., Goyal, S., Meier, J., Guo, D., Ott, M., Zitnick, C.L., Ma, J., & Fergus, R. (2019). Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. Proceedings of the National Academy of Sciences of the United States of America, 118https://www.biorxiv.org/content/10.1101/622803v4
Brandes, N., Goldman, G., Wang, C.H. et al. Genome-wide prediction of disease variant effects with a deep protein language model. Nat Genet 55, 1512–1522 (2023).https://doi.org/10.1038/s41588-023-01465-0
Meier, J., Rao, R., Verkuil, R., Liu, J., Sercu, T., & Rives, A. (2021). Language models enable zero-shot prediction of the effects of mutations on protein function. NeurIPS.https://proceedings.neurips.cc/paper/2021/hash/f51338d736f95dd42427296047067694-Abstract.html
Marquet, C., Heinzinger, M., Olenyi, T., Dallago, C., Bernhofer, M., Erckert, K., & Rost, B. (2021). Embeddings from protein language models predict conservation and variant effects. Human Genetics, 141, 1629 - 1647.https://link.springer.com/article/10.1007/s00439-021-02411-y
Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789.https://arxiv.org/abs/2205.05789
Ferruz, N., Schmidt, S., & Höcker, B. (2022). ProtGPT2 is a deep unsupervised language model for protein design. Nature Communications, 13.https://www.nature.com/articles/s41467-022-32007-7
Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517.https://arxiv.org/abs/2206.13517
Rao, R., Liu, J., Verkuil, R., Meier, J., Canny, J.F., Abbeel, P., Sercu, T., & Rives, A. (2021). MSA Transformer. ICML.http://proceedings.mlr.press/v139/rao21a.html
Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: protein fitness prediction with autoregressive transformers and inference-time retrieval. ICML.https://proceedings.mlr.press/v162/notin22a.html
Notin, P., Van Niekerk, L., Kollasch, A., Ritter, D., Gal, Y. & Marks, D.S. & (2022). TranceptEVE: Combining Family-specific and Family-agnostic Models of Protein Sequences for Improved Fitness Prediction. NeurIPS, LMRL workshop.https://www.biorxiv.org/content/10.1101/2022.12.07.519495v1?rss=1
Yang, K.K., Fusi, N., Lu, A.X. (2022). Convolutions are competitive with transformers for protein sequence pretraining.https://doi.org/10.1101/2022.05.19.492714
Yang, K.K., Yeh, H., Zanichelli, N. (2022). Masked Inverse Folding with Sequence Transfer for Protein Representation Learning.https://doi.org/10.1101/2022.05.25.493516
J. Dauparas, I. Anishchenko, N. Bennett, H. Bai, R. J. Ragotte, L. F. Milles, B. I. M. Wicky, A. Courbet, R. J. de Haas, N. Bethel, P. J. Y. Leung, T. F. Huddy, S. Pellock, D. Tischer, F. Chan,B. Koepnick, H. Nguyen, A. Kang, B. Sankaran,A. K. Bera, N. P. King,D. Baker (2022). Robust deep learning-based protein sequence design using ProteinMPNN. Science, Vol 378.https://www.science.org/doi/10.1126/science.add2187
Chloe Hsu, Robert Verkuil, Jason Liu, Zeming Lin, Brian Hie, Tom Sercu, Adam Lerer, Alexander Rives (2022). Learning Inverse Folding from Millions of Predicted Structures. ICMLhttps://www.biorxiv.org/content/10.1101/2022.04.10.487779v2.full.pdf+html
Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability.https://www.biorxiv.org/content/10.1101/2023.12.01.569522v1
Jin Su, Chenchen Han, Yuyang Zhou, Junjie Shan, Xibin Zhou, Fajie Yuan. (2024). SaProt: Protein Language Modeling with Structure-aware Vocabulary. ICLRhttps://pypi.org/project/proteingym/href='https:/www.biorxiv.org/content/10.1101/2023.10.01.560349v5
Truong, Timothy F. and Tristan Bepler. PoET: A generative model of protein families as sequences-of-sequences. NeurIPShttps://papers.nips.cc/paper_files/paper/2023/hash/f4366126eba252699b280e8f93c0ab2f-Abstract-Conference.html
Daria Frolova, Daria Marina A. Pak, Anna Litvin, Ilya Sharov, Dmitry N. Ivankov, Ivan Oseledets. (2024). MULAN: Multimodal Protein Language Model for Sequence and Structure Encoding.https://www.biorxiv.org/content/10.1101/2024.05.30.596565v1
Mingchen Li, Pan Tan, Xinzhu Ma, Bozitao Zhong, Huiqun Yu, Ziyi Zhou, Wanli Ouyang, Bingxin Zhou, Liang Hong, Yang Tan (2024). ProSST: Protein Language Modeling with Quantized Structure and Disentangled Attention. NeurIPShttps://www.biorxiv.org/content/10.1101/2024.04.15.589672v3
Mustafa Tekpinar, Laurent David, Thomas Henry, Alessandra Carbone. (2024). PRESCOTT: a population aware, epistatic and structural model accurately predicts missense effect. medRxiv.https://www.medrxiv.org/content/10.1101/2024.02.03.24302219v1
Yang Tan, Ruilin Wang, Banghao Wu, Liang Hong, Bingxin Zhou. (2024). Retrieval-Enhanced Mutation Mastery: Augmenting Zero-Shot Prediction of Protein Language Model. ArXiv, abs/2410.21127.https://arxiv.org/abs/2410.21127
Matsvei Tsishyn, Pauline Hermans, Fabrizio Pucci, Marianne Rooman. (2025). Residue conservation and solvent accessibility are (almost) all you need for predicting mutational effects in proteins. bioRxiv.https://www.biorxiv.org/content/10.1101/2025.02.03.636212v1
Zuobai Zhang, Pascal Notin, Yining Huang, Aurelie C. Lozano, Vijil Chenthamarakshan, Debora Marks, Payel Das, Jian Tang. (2024). Multi-Scale Representation Learning for Protein Fitness Prediction. NeurIPShttps://papers.nips.cc/paper_files/paper/2024/hash/b7d795e655c1463d7299688d489e8ef4-Abstract-Conference.html
Sebastian Prillo, Wilson Wu, Yun Song. (2024). Ultrafast classical phylogenetic method beats large protein language models on variant effect prediction. NeurIPS.https://papers.nips.cc/paper_files/paper/2024/hash/eb2f4fb51ac3b8dc4aac9cf71b0e7799-Abstract-Conference.html
Hayes, T., Rao, R., Akin, H., Sofroniew, N.J., Oktay, D., Lin, Z., Verkuil, R., Tran, V.Q., Deaton, J., Wiggert, M., Badkundri, R., Shafkat, I., Gong, J., Derry, A., Molina, R.S., Thomas, N., Khan, Y.A., Mishra, C., Kim, C., Bartie, L.J., Nemeth, M., Hsu, P.D., Sercu, T., Candido, S., & Rives, A. (2025). Simulating 500 million years of evolution with a language model. Science.https://www.science.org/doi/10.1126/science.ads0018
ESM Teamhttps://evolutionaryscale.ai/blog/esm-cambrian
Chen, B., Cheng, X., Li, P., Geng, Y., Gong, J., Li, S., Bei, Z., Tan, X., Wang, B., Zeng, X., Liu, C., Zeng, A., Dong, Y., Tang, J., & Song, L. (2025). xTrimoPGLM: unified 100-billion-parameter pretrained transformer for deciphering the language of proteins. Nature methods.https://www.nature.com/articles/s41592-025-02636-z
Bhatnagar, A., Jain, S., Beazer, J., Curran, S.C., Hoffnagle, A.M., Ching, K., Martyn, M., Nayfach, S., Ruffolo, J.A., & Madani, A. (2025). Scaling unlocks broader generation and deeper functional understanding of proteins. bioRxiv, 2025.04.15.649055.https://doi.org/10.1101/2025.04.15.649055
this scripthttps://github.com/OATML-Markslab/ProteinGym/blob/main/proteingym/baselines/rita/compute_fitness.py
this scripthttps://github.com/OATML-Markslab/ProteinGym/blob/main/scripts/scoring_DMS_zero_shot/scoring_RITA_substitutions.sh
for zero-shot DMS benchmarkshttps://github.com/OATML-Markslab/ProteinGym/blob/main/proteingym/performance_DMS_benchmarks.py
https://github.com/OATML-Markslab/ProteinNPThttps://github.com/OATML-Markslab/ProteinNPT
config scripthttps://github.com/OATML-Markslab/ProteinGym/blob/main/scripts/zero_shot_config.sh
config.jsonhttps://github.com/OATML-Markslab/ProteinGym/blob/main/config.json
DMS_output_score_folder_subshttps://github.com/OATML-Markslab/ProteinGym/blob/main/scripts/zero_shot_config.sh#L19
merge scripthttps://github.com/OATML-Markslab/ProteinGym/blob/main/scripts/scoring_DMS_zero_shot/merge_all_scores.sh
scripts/scoring_DMS_zero_shot/performance_substitutions.shhttps://github.com/OATML-Markslab/ProteinGym/blob/main/scripts/scoring_DMS_zero_shot/performance_substitutions.sh
https://github.com/churchlab/UniRephttps://github.com/churchlab/UniRep
https://github.com/chloechsu/combining-evolutionary-and-assay-labelled-datahttps://github.com/chloechsu/combining-evolutionary-and-assay-labelled-data
https://github.com/OATML-Markslab/EVEhttps://github.com/OATML-Markslab/EVE
https://hub.docker.com/r/elodielaine/gemmehttps://hub.docker.com/r/elodielaine/gemme
https://github.com/facebookresearch/esmhttps://github.com/facebookresearch/esm
https://github.com/debbiemarkslab/EVcouplingshttps://github.com/debbiemarkslab/EVcouplings
https://github.com/salesforce/progenhttps://github.com/salesforce/progen
https://github.com/EddyRivasLab/hmmerhttps://github.com/EddyRivasLab/hmmer
https://github.com/rmrao/msa-transformerhttps://github.com/rmrao/msa-transformer
https://huggingface.co/nferruz/ProtGPT2https://huggingface.co/nferruz/ProtGPT2
https://github.com/dauparas/ProteinMPNNhttps://github.com/dauparas/ProteinMPNN
https://github.com/lightonai/RITAhttps://github.com/lightonai/RITA
https://github.com/OATML-Markslab/Tranceptionhttps://github.com/OATML-Markslab/Tranception
https://github.com/Rostlab/VESPAhttps://github.com/Rostlab/VESPA
https://github.com/microsoft/protein-sequence-modelshttps://github.com/microsoft/protein-sequence-models
https://github.com/microsoft/protein-sequence-modelshttps://github.com/microsoft/protein-sequence-models
https://github.com/steineggerlab/foldseekhttps://github.com/steineggerlab/foldseek
https://github.com/tyang816/ProtSSNhttps://github.com/tyang816/ProtSSN
https://github.com/westlake-repl/SaProthttps://github.com/westlake-repl/SaProt
https://github.com/OpenProteinAI/PoEThttps://github.com/OpenProteinAI/PoET
https://github.com/DFrolova/MULANhttps://github.com/DFrolova/MULAN
https://github.com/ai4protein/ProSSThttps://github.com/ai4protein/ProSST
http://gitlab.lcqb.upmc.fr/tekpinar/PRESCOTThttp://gitlab.lcqb.upmc.fr/tekpinar/PRESCOTT
https://github.com/tyang816/VenusREMhttps://github.com/tyang816/VenusREM
https://github.com/3BioCompBio/RSALORhttps://github.com/3BioCompBio/RSALOR
https://github.com/DeepGraphLearning/S3Fhttps://github.com/DeepGraphLearning/S3F
https://github.com/songlab-cal/CherryMLhttps://github.com/songlab-cal/CherryML
https://github.com/evolutionaryscale/esmhttps://github.com/evolutionaryscale/esm
https://github.com/biomap-research/xTrimoPGLMhttps://github.com/biomap-research/xTrimoPGLM
https://github.com/Profluent-AI/progen3https://github.com/Profluent-AI/progen3
ProteinGym_v1.0https://zenodo.org/records/13932633
ProteinGym_v1.1https://zenodo.org/records/13936340
ProteinGym_v1.2https://zenodo.org/records/14997691
ProteinGym_v1.3https://zenodo.org/records/15293562
https://www.proteingym.org/https://www.proteingym.org/
link to abstracthttps://papers.nips.cc/paper_files/paper/2023/hash/cac723e5ff29f65e3fcbb0739ae91bee-Abstract-Datasets_and_Benchmarks.html
link to abstracthttps://www.biorxiv.org/content/10.1101/2023.12.07.570727v1
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1.3 Apr 28, 2025 https://pypi.org/project/proteingym/1.3/
1.2.0 Mar 10, 2025 https://pypi.org/project/proteingym/1.2.0/
1.1 Oct 15, 2024 https://pypi.org/project/proteingym/1.1/
1.0 Oct 15, 2024 https://pypi.org/project/proteingym/1.0/
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proteingym-1.3.tar.gzhttps://files.pythonhosted.org/packages/05/74/4c9a5ec331087f25974e2044feab41675b4af487651ab37e69c3b52246fe/proteingym-1.3.tar.gz
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proteingym-1.3-py3-none-any.whlhttps://files.pythonhosted.org/packages/80/cb/f0a3adb6757dd5000a846b6d642a076473b7ac54e2374886850a9877cca3/proteingym-1.3-py3-none-any.whl
view detailshttps://pypi.org/project/proteingym/#proteingym-1.3-py3-none-any.whl
proteingym-1.3.tar.gzhttps://files.pythonhosted.org/packages/05/74/4c9a5ec331087f25974e2044feab41675b4af487651ab37e69c3b52246fe/proteingym-1.3.tar.gz
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proteingym-1.3-py3-none-any.whlhttps://files.pythonhosted.org/packages/80/cb/f0a3adb6757dd5000a846b6d642a076473b7ac54e2374886850a9877cca3/proteingym-1.3-py3-none-any.whl
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