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DeepLoc 2.0 is a multi-label predictor, which means that is able to predict one or more localizations for any given protein. It can differentiate between 10 different localizations: Nucleus, Cytoplasm, Extracellular, Mitochondrion, Cell membrane, Endoplasmic reticulum, Chloroplast, Golgi apparatus, Lysosome/Vacuole and Peroxisome.
DeepLoc-1.0 predicts the subcellular localization of eukaryotic proteins. It can differentiate between 10 different localizations: Nucleus, Cytoplasm, Extracellular, Mitochondrion, Cell membrane, Endoplasmic reticulum, Chloroplast, Golgi apparatus, Lysosome/Vacuole and Peroxisome.
Multi-label subcellular localization and sorting signal prediction based on protein foundation models ( https://github.com/agemagician/ProtTrans, https://github.com/facebookresearch/esm ). Prediction webserver is available at https://services.healthtech.dtu.dk/services/DeepLoc-2.0/. More details can be found in the paper https://academic.oup.
30 de abr. de 2022 · DeepLoc 2.0 uses a transformer-based protein language model to predict multi-label subcellular localization and provides interpretability via the attention and sorting signal prediction.
5 de jul. de 2022 · Protein Sorting Signals. The prediction of protein subcellular localization is of great relevance for proteomics research. Here, we propose an update to the popular tool DeepLoc with multi-localization prediction and improvements in both performance and interpretability.
8 de abr. de 2024 · 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.
DeepLoc2 on Biowulf. DeepLoc2 uses deep learning to predict subcellular localization of eukaryotic proteins. DeepLoc 2.0 predicts the subcellular localization (s) of eukaryotic proteins. DeepLoc 2.0 is a multi-label predictor, which means that is able to predict one or more localizations for any given protein.
