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DeepLoc - 2.0 Prediction of eukaryotic protein subcellular localization using deep learning. 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.
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.
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.
We propose DeepLocPro, an extension to the popular method DeepLoc, tailored specifically to archaeal and bacterial organisms. DeepLocPro is a multiclass subcellular location prediction tool for prokaryotic proteins, trained on experimentally verified data curated from UniProt and PSORTdb.
- Introduction
- Webserver
- Data
- Ploc 2.0 Overview
- Results and Discussion
- Conclusion
- Data Availability
- Funding
Identifying protein localization in different cellular compartments plays a key role in functional annotation. It can also aid in identifying drug targets (1), and understanding diseases linked to aberrant subcellular localization (2,3). Some proteins are known to localize in multiple cellular compartments (4–6). Several biological mechanisms have ...
The webserver is free and open to all and there is no login requirement. It takes in a maximum of 500 input sequences in the FASTA format. The model’s attention is shown in a figure when the long result format is toggled. Regions with high attention values are used by the model for its prediction and they are indicative of the presence of sorting s...
We curate three datasets: two datasets with subcellular localization labels for cross-validation and independent validation, respectively, and a third dataset consisting of sorting signal labels, both the presence and location within the sequence, which is a part of the cross-validation dataset. Detailed statistics regarding the distribution of sub...
As shown in Figure 2, the method can be broadly divided into three stages, each of which is briefly described below. More detailed information can be found in the Supplementary Section S2.
We chose YLoc+, DeepLoc 1.0, Fuel-mLoc, and LAProtT5 tools for comparison. These tools have public webservers or easily available local implementations. Since the outputs are different for each of the methods, we map the locations to the ten classes used in this work. We also reduce the Fuel-mLoc database by about 2% to remove close homologs to the...
We provide a multi-label subcellular localization prediction tool, based on protein language models, that uses only the sequence information and outperforms existing methods. This is made possible by the use of a large curated dataset with annotations of multi-location proteins. Additionally, using a small dataset of sorting signals, we were able t...
The data used for training and testing are available at https://services.healthtech.dtu.dk/service.php?DeepLoc-2.0.
O.W. is supported by Novo Nordisk Fonden [NNF20OC0062606] and Danish National Research Foundation [the Pioneer Centre for AI, grant number P1]. Funding for open access charge: Public research funding (to O.W.). Conflict of interest statement. The downloadable version of DeepLoc 2.0 has been commercialized (it is licensed for a fee to commercial use...
DeepLoc 2.0 Multi-label subcellular localization and sorting signal prediction based on protein foundation models ( https://github.com/agemagician/ProtTrans , https://github.com/facebookresearch/esm ).
1 de nov. de 2017 · Motivation: The prediction of eukaryotic protein subcellular localization is a well-studied topic in bioinformatics due to its relevance in proteomics research. Many machine learning methods have been successfully applied in this task, but in most of them, predictions rely on annotation of homologues from knowledge databases.
