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Title: GitHub - OATML-Markslab/ProteinGym: Official repository for the ProteinGym benchmarks · GitHub

Open Graph Title: GitHub - OATML-Markslab/ProteinGym: Official repository for the ProteinGym benchmarks

X Title: GitHub - OATML-Markslab/ProteinGym: Official repository for the ProteinGym benchmarks

Description: Official repository for the ProteinGym benchmarks. Contribute to OATML-Markslab/ProteinGym development by creating an account on GitHub.

Open Graph Description: Official repository for the ProteinGym benchmarks. Contribute to OATML-Markslab/ProteinGym development by creating an account on GitHub.

X Description: Official repository for the ProteinGym benchmarks. Contribute to OATML-Markslab/ProteinGym development by creating an account on GitHub.

Opengraph URL: https://github.com/OATML-Markslab/ProteinGym

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219 Commitshttps://github.com/OATML-Markslab/ProteinGym/commits/main/
https://github.com/OATML-Markslab/ProteinGym/commits/main/
benchmarkshttps://github.com/OATML-Markslab/ProteinGym/tree/main/benchmarks
benchmarkshttps://github.com/OATML-Markslab/ProteinGym/tree/main/benchmarks
environmentshttps://github.com/OATML-Markslab/ProteinGym/tree/main/environments
environmentshttps://github.com/OATML-Markslab/ProteinGym/tree/main/environments
notebookshttps://github.com/OATML-Markslab/ProteinGym/tree/main/notebooks
notebookshttps://github.com/OATML-Markslab/ProteinGym/tree/main/notebooks
proteingymhttps://github.com/OATML-Markslab/ProteinGym/tree/main/proteingym
proteingymhttps://github.com/OATML-Markslab/ProteinGym/tree/main/proteingym
reference_fileshttps://github.com/OATML-Markslab/ProteinGym/tree/main/reference_files
reference_fileshttps://github.com/OATML-Markslab/ProteinGym/tree/main/reference_files
scriptshttps://github.com/OATML-Markslab/ProteinGym/tree/main/scripts
scriptshttps://github.com/OATML-Markslab/ProteinGym/tree/main/scripts
.gitignorehttps://github.com/OATML-Markslab/ProteinGym/blob/main/.gitignore
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README.mdhttps://github.com/OATML-Markslab/ProteinGym/blob/main/README.md
README.mdhttps://github.com/OATML-Markslab/ProteinGym/blob/main/README.md
assays.bibhttps://github.com/OATML-Markslab/ProteinGym/blob/main/assays.bib
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config.jsonhttps://github.com/OATML-Markslab/ProteinGym/blob/main/config.json
config.jsonhttps://github.com/OATML-Markslab/ProteinGym/blob/main/config.json
setup.pyhttps://github.com/OATML-Markslab/ProteinGym/blob/main/setup.py
setup.pyhttps://github.com/OATML-Markslab/ProteinGym/blob/main/setup.py
READMEhttps://github.com/OATML-Markslab/ProteinGym
MIT licensehttps://github.com/OATML-Markslab/ProteinGym
https://github.com/OATML-Markslab/ProteinGym#proteingym
https://doi.org/10.5281/zenodo.15293562
https://pypi.org/project/proteingym/
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https://github.com/OATML-Markslab/ProteinGym#table-of-contents
Overviewhttps://github.com/OATML-Markslab/ProteinGym#overview
Resultshttps://github.com/OATML-Markslab/ProteinGym#results
Resourceshttps://github.com/OATML-Markslab/ProteinGym#resources
How to contribute?https://github.com/OATML-Markslab/ProteinGym#how-to-contribute
Usage and reproducibilityhttps://github.com/OATML-Markslab/ProteinGym#usage-and-reproducibility
Acknowledgementshttps://github.com/OATML-Markslab/ProteinGym#acknowledgements
Releaseshttps://github.com/OATML-Markslab/ProteinGym#releases
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benchmarkshttps://github.com/OATML-Markslab/ProteinGym/tree/main/benchmarks
https://github.com/OATML-Markslab/ProteinGym#proteingym-benchmarks---leaderboard
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://elifesciences.org/articles/98033
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, Yang Tan, Xinzhu Ma, Bozitao Zhong, Huiqun Yu, Ziyi Zhou, Wanli Ouyang, Bingxin Zhou, Pan Tan, Liang Hong (2024). ProSST: Protein Language Modeling with Quantized Structure and Disentangled Attention. NeurIPShttps://proceedings.neurips.cc/paper_files/paper/2024/hash/3ed57b293db0aab7cc30c44f45262348-Abstract-Conference.html
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). From high-throughput evaluation to wet-lab studies: advancing mutation effect prediction with a retrieval-enhanced model. ISMB/ECCB.https://academic.oup.com/bioinformatics/article/41/Supplement_1/i401/8199374
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
Sun, N., Zou, S., Tao, T., Mahbub, S., Li, D., Zhuang, Y., Wang, H., Cheng, X., Song, L., & Xing, E.P. (2024). Mixture of Experts Enable Efficient and Effective Protein Understanding and Design. bioRxiv.https://www.biorxiv.org/content/10.1101/2024.11.29.625425v1
https://github.com/OATML-Markslab/ProteinGym#resources
https://github.com/OATML-Markslab/ProteinGym#how-to-contribute
https://github.com/OATML-Markslab/ProteinGym#new-assays
https://github.com/OATML-Markslab/ProteinGym#new-baselines
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/ProteinGym#notes
https://github.com/OATML-Markslab/ProteinNPThttps://github.com/OATML-Markslab/ProteinNPT
https://github.com/OATML-Markslab/ProteinGym#usage-and-reproducibility
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/OATML-Markslab/ProteinGym#acknowledgements
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/ai4protein/ProtSSNhttps://github.com/ai4protein/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/ai4protein/VenusREMhttps://github.com/ai4protein/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
https://github.com/genbio-ai/AIDOhttps://github.com/genbio-ai/AIDO
https://github.com/OATML-Markslab/ProteinGym#releases
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://github.com/OATML-Markslab/ProteinGym#license
https://github.com/OATML-Markslab/ProteinGym#reference
https://github.com/OATML-Markslab/ProteinGym#links
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
link to zenodohttps://zenodo.org/records/15293562
link to pypihttps://pypi.org/project/proteingym/
link to HFhttps://huggingface.co/datasets/OATML-Markslab/ProteinGym_v1
proteingym.org/https://proteingym.org/
benchmark https://github.com/topics/benchmark
computational-biology https://github.com/topics/computational-biology
protein https://github.com/topics/protein
protein-design https://github.com/topics/protein-design
protein-fitness https://github.com/topics/protein-fitness
Readme https://github.com/OATML-Markslab/ProteinGym#readme-ov-file
MIT license https://github.com/OATML-Markslab/ProteinGym#MIT-1-ov-file
Please reload this pagehttps://github.com/OATML-Markslab/ProteinGym
Activityhttps://github.com/OATML-Markslab/ProteinGym/activity
Custom propertieshttps://github.com/OATML-Markslab/ProteinGym/custom-properties
58 forkshttps://github.com/OATML-Markslab/ProteinGym/forks
Report repository https://github.com/contact/report-content?content_url=https%3A%2F%2Fgithub.com%2FOATML-Markslab%2FProteinGym&report=OATML-Markslab+%28user%29
Releases 4https://github.com/OATML-Markslab/ProteinGym/releases
PG_v1.3 Latest Apr 28, 2025 https://github.com/OATML-Markslab/ProteinGym/releases/tag/PG_v1.3
+ 3 releaseshttps://github.com/OATML-Markslab/ProteinGym/releases
Packages 0https://github.com/orgs/OATML-Markslab/packages?repo_name=ProteinGym
Please reload this pagehttps://github.com/OATML-Markslab/ProteinGym
Please reload this pagehttps://github.com/OATML-Markslab/ProteinGym
Contributorshttps://github.com/OATML-Markslab/ProteinGym/graphs/contributors
Please reload this pagehttps://github.com/OATML-Markslab/ProteinGym
HTML 50.8% https://github.com/OATML-Markslab/ProteinGym/search?l=html
Python 43.5% https://github.com/OATML-Markslab/ProteinGym/search?l=python
TeX 4.2% https://github.com/OATML-Markslab/ProteinGym/search?l=tex
Shell 1.4% https://github.com/OATML-Markslab/ProteinGym/search?l=shell
https://github.com
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Communityhttps://github.community/
Docshttps://docs.github.com/
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