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DeepLoc 2.1: multi-label membrane protein type prediction using protein language models
Abstract
DeepLoc 2.0 is a popular web server for the prediction of protein subcellular localization and sorting signals. Here, we introduce DeepLoc 2.1, which additionally classifies the input proteins into the membrane protein types Transmembrane, Peripheral, Lipid-anchored and Soluble. Leveraging pre-trained transformer-based protein language models, the server utilizes a three-stage architecture for sequence-based, multi-label predictions. Comparative evaluations with other established tools on a test set of 4933 eukaryotic protein sequences, constructed following stringent homology partitioning, demonstrate state-of-the-art performance. Notably, DeepLoc 2.1 outperforms existing models, with the larger ProtT5 model exhibiting a marginal advantage over the ESM-1B model. The web server is available at https://services.healthtech.dtu.dk/services/DeepLoc-2.1.
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“Here, we identified 234 proteins with differential abundance in the TET2-super competitors compared to WT competitors (adjusted p<0.05, log2FC >0.5) (Figure 5D), 75 of which were classified as probable secreted proteins by the DeepLoc protein localization tool”
From A scalable in vitro cell competition model identifies preleukemic super competitor clones (2026) · cites this paper as Ødum et al, 2024
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How many drug targets are there?
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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
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“We use sequence-based embeddings from the ProtT5 ( 6 ) and ESM-1B protein language models ( 7 ) and implement the same model architecture used by DeepLoc 2.0, performing interpretable attention pooling over sequence embeddings with discrete cosine transform regularization.”
InfluentialProtTrans: Toward Understanding the Language of Life Through Self-Supervised Learning
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Rao, R., Meier, J., Sercu, T., Ovchinnikov, S., & Rives, A. (2020) · doi:10.1101/2020.12.15.422761 · Cited by 341
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Cited by
Synergistic role of CSPBP and SGS1 in sporozoite entry into Aedes aegypti salivary glands
Morvay, A., Araújo, H. R. C., Capurro, M. L., & Burini, B. C. (2026) · Scientific Reports · doi:10.1038/s41598-026-46444-7
A scalable in vitro cell competition model identifies preleukemic super competitor clones
Cabrera-Cosme, L., Rubio-Lara, J. A., Bennett, E., Miłek, J., Baldreki, C., Dowle, A., Vijayabaskar, M. S., Davies, S. J., Taylor-Smith, E., Bain, F. M., et al. (2026) · doi:10.64898/2026.10.21.760611
Metadata
- Authors
- Ødum, M. T., Teufel, F., Thumuluri, V., Almagro Armenteros, J. J., Johansen, A. R., Winther, O., & Nielsen, H.
- Affiliations
- Section for Bioinformatics, Department of Health Technology, Technical University of Denmark , 2800 Kongens Lyngby, Denmark
- Bioinformatics Centre, Department of Biology, University of Copenhagen , 2200 Copenhagen, Denmark
- University of California , San Diego, CA 92093, USA
- Bristol Myers Squibb Company, Informatics and Predictive Sciences Research , Calle Isaac Newton 4, Sevilla 41092, Spain
- Department of Computer Science, Stanford University , Stanford, CA 94305, USA
- ORCID
- Nielsen, Henrik
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2024-04-08
- Volume
- 52
- Issue
- W1
- Pages
- W215-W220
- ISSN
- 0305-1048
- 1362-4962
- PMC
- PMC11223819
- Cited by
- 285
- References
- 8
- Language
- en
- Funders
- Novo Nordisk Foundation (NNF20OC0062606)
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- Ødum, M. T., Teufel, F., Thumuluri, V., Almagro Armenteros, J. J., Johansen, A. R., Winther, O., & Nielsen, H. (2024). DeepLoc 2.1: multi-label membrane protein type prediction using protein language models. Nucleic Acids Research. https://doi.org/10.1093/nar/gkae237
