Refining Protein Subcellular Localization
Figure 1
Structure of the PSLT2 Bayesian Network
The PSLT2 predictor is composed of three independent modules that can predict localization individually or in combination: the motif, targeting, and interaction modules. Each module can be characterized by the protein information used as input and the localization probabilities (for all compartments [C]) that are generated as output. The motif module accepts combinations of InterPro motifs (M) as input. The targeting module considers the presence of mitochondrial targeting signals (Mi), signal peptides/anchors (Si; S, signal peptide; A, signal anchor; Q, neither), GPI anchors (G), and the number of transmembrane domains (Tm) to predict localization. The interaction module considers the three compartments to which are localized the largest number of interactions partners (see Materials and Methods for more details). The full network (illustrated as the localization module) takes into account the output of all three modules to predict the probability of localization to all compartments (C).