Motivation: Functional annotation of unknown proteins is a major goal in proteomics. A key annotation is the prediction of a protein's subcellular localization. Numerous prediction techniques have been developed, typically focusing on a single underlying biological aspect or predicting a subset of all possible localizations. An important step is taken towards emulating the protein sorting process by capturing and bringing together biologically relevant information, addressing the clear need to improve prediction accuracy and localization coverage. Results: Here we present a novel SVM-based approach for predicting subcellular localization, which integrates N-terminal targeting sequences, amino acid composition, and protein sequence motifs. We show how this approach improves the prediction based on Nterminal targeting sequences, by comparing our method TargetLoc against existing methods. Furthermore, MultiLoc performs considerably better than comparable methods predicting all major eukaryotic subcellular localizations, and shows better or comparable results to methods that are specialized on fewer localizations or for one organism.
Höglund et al. has achieved the overall accuracy of more than 74% by combining N-terminal signals, amino acid compositions and sequence motifs REF .
MultiLoc : prediction of protein subcellular localization using N-terminal targeting sequences , sequence motifs , and amino acid composition
Bioinformatics
Bioinformatics
Computer Science Medicine