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

Abstract

Background: Knowledge 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.

Results: We 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.

Conclusion: MultiLoc2 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.

Figure 1

MultiLoc2 architecture. The architecture of MultiLoc2-HighRes (animal version). A query sequence is processed by a first layer of six subprediction methods (SVMTarget, SVMSA, SVMaac, PhyloLoc, GOLoc and MotifSearch). The two new subprediction methods, PhyloLoc and GOLoc, are highlighted in bold. The individual output of the methods of the first layer are collected in the protein profile vector (PPV), which enters a second layer of SVMs producing probability estimates for each localization.

Figure 2

PhyloLoc and GOLoc architecture. The architectures of PhyloLoc and GOLoc from MultiLoc2-LowRes. The input of PhyloLoc is a vector of similarities (phylogenetic profile) between the query sequence and the best sequence match in each genome inferred from BLAST. The input of GOLoc is a binary-coded vector representing the GO terms of the query sequence inferred from InterPro using InterProScan. PhyloLoc and GOLoc use one-versus-one SVMs to process their input and to calculate probability estimates for each localization.