LocTree2 predicts localization for all domains of life

Tatyana Goldberg, Tobias Hamp, Burkhard Rost

Research output: Contribution to journalArticlepeer-review

90 Scopus citations

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.

Original languageEnglish
Article numberbts390
Pages (from-to)i458-i465
JournalBioinformatics
Volume28
Issue number18
DOIs
StatePublished - Sep 2012

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