MultiLoc2: integrating phylogeny and Gene Ontology terms improves subcellular protein localization prediction

By Torsten Blum, Sebastian Briesemeister, Oliver Kohlbacher. 2009

293 citations recorded by the source corpus.

BackgroundKnowledge of subcellular localization of proteins is crucial to proteomics, drug target discovery and systems biology since localization and biological function are highly correlated. In recent years, numerous computational prediction methods have been developed. Nevertheless, there is still a need for prediction methods that show more robustness and higher accuracy.ResultsWe extended our previous MultiLoc predictor by incorporating phylogenetic profiles and Gene Ontology terms. Two different datasets were used for training the system, resulting in two versions of this high-accuracy prediction method. One version is specialized for globular proteins and predicts up to five localizations, whereas a second version covers all eleven main eukaryotic subcellular localizations. In a benchmark study with five localizations, MultiLoc2 performs considerably better than other methods for animal and plant proteins and comparably for fungal proteins. Furthermore, MultiLoc2 performs clearly better when using a second dataset that extends the benchmark study to all eleven main eukaryotic subcellular localizations.ConclusionMultiLoc2 is an extensive high-performance subcellular protein localization prediction system. By incorporating phylogenetic profiles and Gene Ontology terms MultiLoc2 yields higher accuracies compared to its previous version. Moreover, it outperforms other prediction systems in two benchmarks studies. MultiLoc2 is available as user-friendly and free web-service, available at: http://www-bs.informatik.uni-tuebingen.de/Services/MultiLoc2.

Subfields: Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

Citations in the corpus

Modeling aspects of the language of life through transfer-learning protein sequences (2019, methodology): “Subcellular localization prediction was trained and evaluated using the DeepLoc data set [47] for which performance was measured for several methods, namely: LocTree2 [72], MultiLoc2 [73], Sher-Loc2 [74], CELLO [75], iLoc-Euk [52], WoLF PSORT [76] and YLoc [77].”
Modeling the language of life – Deep Learning Protein Sequences (2019, methodology): “Localization prediction 3 was trained and evaluated using the DeepLoc data set [46] for which performance was 4 measured for several methods, namely: LocTree2 [66], MultiLoc2 [67], SherLoc2 [68], CELLO 5 [69], iLoc-Euk [51], WoLF PSORT [70] and YLoc [71].”
SUBA3: a database for integrating experimentation and prediction to define the SUBcellular location of proteins in Arabidopsis (2012, unknown): “1 (23), EpiLoc (24), iPSORT (25), MitoPred (26), MitoProt (27), MultiLoc2 (28), Nucleo (29), PCLR 0.”
CELLO2GO: A Web Server for Protein subCELlular LOcalization Prediction with Functional Gene Ontology Annotation (2014, background): “[7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22, 23] Some of these programs provide additional information, e.g., protein-protein interactions [14,19] or three-dimensional structure comparisons [18], although most just attempt to determine the subcellular compartment of the targeted protein.”
Convolutional LSTM Networks for Subcellular Localization of Proteins (2015, background): “MultiLoc1/2 accuracies are reprinted from (Goldberg et al., 2012) and the SherLoc accuracy from (Briesemeister et al., 2009). dependencies.”
YLoc—an interpretable web server for predicting subcellular localization (2010, methodology): “The Ho¨glund IDS (20) contains animals proteins from remaining locations, the er, go, pe, pm, ex and ly, and was constructed with the same restrictions as the BaCelLo data set.”