LocTree2 predicts localization for all domains of life.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 22962467.
- Also identified by DOI 10.1093/bioinformatics/bts390 and PMC identifier 3436817.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
Abstract
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. 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. Online through PredictProtein (predictprotein.org); as standalone version at http://www.rostlab.org/services/loctree2. localization@rostlab.org Supplementary data are available at Bioinformatics online.
Medical subject headings
- Archaeal Proteins
- Bacterial Proteins
- Membrane Proteins
- Proteins
- Support Vector Machine