A nearest neighbor approach for automated transporter prediction and categorization from protein sequences

Motivation: Membrane transport proteins play a crucial role in the import and export of ions, small molecules or macromolecules across biological membranes. Currently, there are a limited number of published computational tools which enable the systematic discovery and categorization of transporters prior to costly experimental validation. To approach this problem, we utilized a nearest neighbor method which seamlessly integrates homologous search and topological analysis into a machine-learning framework. Results: Our approach satisfactorily distinguished 484 transporter families in the Transporter Classification Database, a curated and representative database for transporters. A five-fold cross-validation on the database achieved a positive classification rate of 72.3 on average. Furthermore, this method successfully detected transporters in seven model and four non-model organisms, ranging from archaean to mammalian species. A preliminary literature-based validation has cross-validated 65.8 of our predictions on the 11 organisms, including 55.9 of our predictions overlapping with 83.6 of the predicted transporters in TransportDB. Availability and Supplementary information: http://www.w3.org/1999/xlink">http://bioinfo.noble.org/manuscript-support/transporter/ Contact: pzhao@noble.org.