Membrane proteins are estimated to constitute a quarter of all proteins encoded in plant genomes, yet only a limited number have been experimentally characterized. This is mainly due to the large variation in particular physical properties coupled with purification difficulties. Computational methods are therefore very helpful for the initial characterization of a candidate membrane protein. Individual prediction tools can, with varying levels of success, predict the occurrence of transmembrane spans, the subcellular location, and lipid posttranslational modifications. Since it can be tedious to consult each prediction tool separately, ARAMEMNON has been designed to compile various computational predictions for plant membrane proteins and to present the results via a user-friendly web interface. This protocol describes how to use ARAMEMNON to identify and characterize plant membrane proteins.
Keywords: Computational prediction tool; Integral membrane protein; Lipid-anchored membrane protein; Membrane protein database; Plant membrane protein; Plant permeome; Prediction data visualization; Subcellular location.