Developing computational descriptors to analyse and classify Glycosylphosphatidylinositol (GPI) proteins in Apicomplexan

Glycosylphosphatidylinositol (GPI) and GPI-anchored proteins, represent potential new malaria drug targets. GPI proteins are outer plasma membranes anchored proteins, found in the majority of living organisms, including mammals, yeast, protozoan in addition to archaebacteria. Parasitic GPI proteins have numerous functions including; surface coat proteins; receptors; adhesion molecules; enzymes; in addition to possible roles in host-parasite interactions and immune escape. Sensitivity and specificity of machine-learning algorithms predicting GPI-proteins is weakened by the diversity of the N- and C-terminus specific signals. We present a feature-based approach, predicting GPI-proteins from full-length amino-acid sequence. We show that predicting the hydrophobic properties of the N- and C-terminal, in addition to the central core of the protein was more accurate for the prediction of GPI-proteins than current approaches GPI proteins constitute a group which is highly variable from one species to another, but main functions are related to cell-cell interactions. Few enzymatic function are associated with GPI proteins. Therefore, prediction of the GPI proteome can only rely on the detection of the C-terminus region interacting with the enzyme catalyzing transfer of the protein on the GPI anchor.