Author(s): Goldberg T, Hamp T, Rost B
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Abstract MOTIVATION: Subcellular localization is one aspect of protein function. Despite advances in high-throughput imaging, localization maps remain incomplete. Several methods accurately predict localization, but many challenges remain to be tackled. RESULTS: In this study, we introduced a framework to predict localization in life's three domains, including globular and membrane proteins (3 classes for archaea; 6 for bacteria and 18 for eukaryota). The resulting method, LocTree2, works well even for protein fragments. It uses a hierarchical system of support vector machines that imitates the cascading mechanism of cellular sorting. The method reaches high levels of sustained performance (eukaryota: Q18=65\%, bacteria: Q6=84\%). LocTree2 also accurately distinguishes membrane and non-membrane proteins. In our hands, it compared favorably with top methods when tested on new data. AVAILABILITY: Online through PredictProtein (predictprotein.org); as standalone version at http://www.rostlab.org/services/loctree2. CONTACT: firstname.lastname@example.org SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
This article was published in Bioinformatics
and referenced in International Journal of Biomedical Data Mining