DeepLoc 2.1: multi-label membrane protein type prediction using protein language models
Research output: Contribution to journal › Journal article › Research › peer-review
Documents
- Fulltext
Final published version, 833 KB, PDF document
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.
Original language | English |
---|---|
Journal | Nucleic Acids Research |
Volume | 52 |
Issue number | W1 |
Pages (from-to) | W215-W220 |
Number of pages | 6 |
ISSN | 0305-1048 |
DOIs | |
Publication status | Published - 2024 |
Bibliographical note
Publisher Copyright:
© 2024 The Author(s).
ID: 399110403