LocTree2 predicts localization for all domains of life

Bioinformatics. 2012 Sep 15;28(18):i458-i465. doi: 10.1093/bioinformatics/bts390.


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: localization@rostlab.org

Supplementary information: Supplementary data are available at Bioinformatics online.

Publication types

  • Research Support, Non-U.S. Gov't
  • Validation Study

MeSH terms

  • Animals
  • Archaeal Proteins / analysis*
  • Bacterial Proteins / analysis*
  • Membrane Proteins / analysis*
  • Molecular Sequence Annotation
  • Proteins / analysis*
  • Sequence Analysis, Protein
  • Support Vector Machine*


  • Archaeal Proteins
  • Bacterial Proteins
  • Membrane Proteins
  • Proteins