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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.* ; Steinegger, M.* ; Ovchinnikov, S.* ; Yuan, F.* ; The OPMC (Heinzinger, M.)

Democratizing protein language model training, sharing and collaboration.

Nat. Biotechnol., DOI: 10.1038/s41587-025-02859-7 (2025)
Publ. Version/Full Text Research data DOI PMC
Open Access Green as soon as Postprint is submitted to ZB.
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.
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Publication type Article: Journal article
Document type Scientific Article
Language english
Publication Year 2025
HGF-reported in Year 2025
ISSN (print) / ISBN 1087-0156
e-ISSN 1546-1696
Journal Nature Biotechnology
Publisher Nature Publishing Group
Publishing Place New York, NY
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)
POF-Topic(s) 30205 - Bioengineering and Digital Health
Research field(s) Enabling and Novel Technologies
PSP Element(s) G-503800-010
DOI 10.1038/s41587-025-02859-7
PubMed ID 41136773
Erfassungsdatum 2025-10-28
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