Democratizing protein language model training, sharing and collaboration
Open accessPublished version of a preprint
Abstract
Training and deploying large-scale protein language models typically requires deep machine learning expertise-a barrier for researchers outside this field. SaprotHub overcomes this challenge by offering an intuitive platform that facilitates training and prediction as well as storage and sharing of models. Here we provide the ColabSaprot framework built on Google Colab, which potentially powers hundreds of protein training and prediction applications, enabling researchers to collaboratively build and share customized models.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764 · see citing works
Self-citationAccurate structure prediction of biomolecular interactions with AlphaFold 3
Abramson, J., Adler, J., Dunger, J., Evans, R., Green, T., Pritzel, A., Ronneberger, O., Willmore, L., Ballard, A. J., Bambrick, J., et al. (2024) · Nature · doi:10.1038/s41586-024-07487-w · Cited by 15,861 · see citing works
ColabFold - Making protein folding accessible to all
Mirdita, M., Schütze, K., Moriwaki, Y., Heo, L., Ovchinnikov, S., & Steinegger, M. (2021) · doi:10.1101/2021.08.15.456425 · Cited by 9,391 · see citing works
Self-citationEvolutionary-scale prediction of atomic level protein structure with a language model
Lin, Z., Akin, H., Rao, R., Hie, B., Zhu, Z., Lu, W., Smetanin, N., Verkuil, R., Kabeli, O., Shmueli, Y., et al. (2022) · doi:10.1101/2022.07.20.500902 · Cited by 5,523 · see citing works
ClinVar: improving access to variant interpretations and supporting evidence
Landrum, M. J., Lee, J. M., Benson, M., Brown, G. R., Chao, C., Chitipiralla, S., Gu, B., Hart, J., Hoffman, D., Jang, W., et al. (2018) · Nucleic Acids Research · doi:10.1093/nar/gkx1153 · Cited by 4,196 · see citing works
Rives, A., Meier, J., Sercu, T., Goyal, S., Lin, Z., Liu, J., Guo, D., Ott, M., Zitnick, C. L., Ma, J., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2016239118 · Cited by 3,286 · see citing works
Self-citationRives, A., Meier, J., Sercu, T., Goyal, S., Lin, Z., Liu, J., Guo, D., Ott, M., Zitnick, C. L., Ma, J., et al. (2019) · doi:10.1101/622803 · Cited by 3,280 · cited 1× · see citing works
“This progress is driven by a suite of powerful PLMs that have demonstrated remarkable efficacy across diverse tasks 5–14 .”
Self-citationFast and accurate protein structure search with Foldseek
van Kempen, M., Kim, S. S., Tumescheit, C., Mirdita, M., Lee, J., Gilchrist, C. L. M., Söding, J., & Steinegger, M. (2022) · doi:10.1101/2022.02.07.479398 · Cited by 2,598 · see citing works
Self-citationFast and accurate protein structure search with Foldseek
van Kempen, M., Kim, S. S., Tumescheit, C., Mirdita, M., Lee, J., Gilchrist, C. L. M., Söding, J., & Steinegger, M. (2024) · Nature Biotechnology · doi:10.1038/s41587-023-01773-0 · Cited by 2,596 · see citing works
Self-citationLoRA: Low-Rank Adaptation of Large Language Models
Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., & Chen, W. (2021) · arXiv · doi:10.48550/arxiv.2106.09685 · Cited by 2,523 · see citing works
ProtTrans: Toward Understanding the Language of Life Through Self-Supervised Learning
Elnaggar, A., Heinzinger, M., Dallago, C., Rehawi, G., Wang, Y., Jones, L., Gibbs, T., Feher, T., Angerer, C., Steinegger, M., et al. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3095381 · Cited by 2,329 · see citing works
Robust deep learning based protein sequence design using ProteinMPNN
Dauparas, J., Anishchenko, I., Bennett, N., Bai, H., Ragotte, R. J., Milles, L. F., Wicky, B. I. M., Courbet, A., de Haas, R. J., Bethel, N., et al. (2022) · doi:10.1101/2022.06.03.494563 · Cited by 2,107 · see citing works
Robust deep learning–based protein sequence design using ProteinMPNN
Dauparas, J., Anishchenko, I., Bennett, N., Bai, H., Ragotte, R. J., Milles, L. F., Wicky, B. I. M., Courbet, A., de Haas, R. J., Bethel, N., et al. (2022) · Science · doi:10.1126/science.add2187 · Cited by 2,070 · see citing works
Disease variant prediction with deep generative models of evolutionary data
Frazer, J., Notin, P., Dias, M., Gomez, A., Min, J. K., Brock, K., Gal, Y., & Marks, D. S. (2021) · Nature · doi:10.1038/s41586-021-04043-8 · Cited by 791 · see citing works
Self-citationEvaluating Protein Transfer Learning with TAPE
Rao, R., Bhattacharya, N., Thomas, N., Duan, Y., Chen, X., Canny, J., Abbeel, P., & Song, Y. S. (2019) · doi:10.1101/676825 · Cited by 719 · see citing works
ProGen2: Exploring the boundaries of protein language models
Nijkamp, E., Ruffolo, J. A., Weinstein, E. N., Naik, N., & Madani, A. (2023) · Cell Systems · doi:10.1016/j.cels.2023.10.002 · Cited by 480 · see citing works
Flagged first, then by citations
- Authors
- Su, J., Li, Z., Tao, T., Han, C., He, Y., Dai, F., Yuan, Q., Gao, Y., Si, T., Zhang, X., Zhou, Y., Shan, J., Zhou, X., Chang, X., Jiang, S., Ma, D., The, O. P. M. C., Gitter, A., Mirdita, M., Yang, K. K., Notin, P., Marks, D. S., Chatterjee, P., Singh, R., Romero, P. A., Heinzinger, M., Liu, J., Zheng, J., Li, S. Z., Zeng, A., Shen, H., Chai, J., Ju, F., Ferruz, N., Glasgow, A., Kim, P. M., Snow, C., Ntranos, V., Yang, J., Hong, L., Gao, C., Si, T., Bronstein, M., Chang, X., Yuan, F., Su, J., Li, Z., Tao, T., Han, C., He, Y., Dai, F., Zhang, X., Zhou, Y., Shan, J., Zhou, X., Jiang, S., Gao, Y., Zhang, J., Fan, Y., Tao, Y., Cheng, L., Zheng, X., Chen, L., Long, R., Kong, L., Pu, Z., Guan, J., Zhang, T., Li, C., Steinegger, M., Ovchinnikov, S., & Yuan, F.
- Affiliations
- Zhejiang Lab (CN)
- Helmholtz Munich (DE)
- Westlake University (CN)
- University of Wisconsin–Madison (US)
- Seoul National University (KR)
- Harvard University (US)
- Universitat Pompeu Fabra (ES)
- Duke University (US)
- University of Toronto (CA)
- University of California, San Francisco (US)
- Shandong University (CN)
- Chinese Academy of Sciences (CN)
- Shanghai Jiao Tong University (CN)
- ShanghaiTech University (CN)
- Rice University (US)
- South China Agricultural University (CN)
- Massachusetts Institute of Technology (US)
- ORCID
- Yuan, Qingyan
- Ma, Dacheng
- Gitter, Anthony
- Mirdita, Milot
- Yang, Kevin K.
- Notin, Pascal
- Marks, Debora S.
- Chatterjee, Pranam
- Singh, Rohit
- Romero, Philip A.
- Heinzinger, Michael
- Liu, Jianming
- Zheng, Jia
- Li, Stan Z.
- Shen, Huaizong
- Chai, Jijie
- Ju, Feng
- Ferruz, Noelia
- Glasgow, Anum
- Kim, Philip M.
- Snow, Christopher
- Ntranos, Vasilis
- Yang, Jianyi
- Gao, Caixia
- Bronstein, Michael
- Zhang, Jiawei
- Cheng, Linqi
- Chen, Lei
- Pu, Zhongji
- Zhang, Tianyuan
- Li, Cheng
- Steinegger, Martin
- Ovchinnikov, Sergey
- Venue
- Nature Biotechnology
- Published
- 2025-10-24
- Volume
- 44
- Issue
- 10
- Pages
- 1628-1634
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- OpenAlex
- W4415506496
- Cited by
- 35
- References
- 16
- Citations by year
- 2026: 24 · 2025: 9 · 2024: 2
- FWCI
- 9.96
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Nature Biotechnology (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed (publishedVersion)
- Seoul National University Open Repository (Seoul National University) (submittedVersion)
- Funders
- The National Key Research and Develeopment Program of China
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Social Sciences › Decision Sciences › Information Systems and Management › Scientific Computing and Data Management
- Keywords
- protein language models, Google Colab, model sharing, collaborative model training, protein sequence prediction, collaborative research platform
- MeSH terms
- Large Language Models, Humans, Proteins, Software
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
