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).
Table 1.
Ten-Fold Cross-Validation Test Accuracy and Coverage for Every Module Combination
Table 2.
Ten-Fold Cross-Validation Test Results of the Full PSLT2 Localization Predictor
Table 3.
Number of Yeast Proteins Predicted in Every Compartment Pair
Figure 2.
Comparison between PSLT2, TRIPLES, and YeastGFP Datasets
Panels A through C represent an illustration of the Pearson correlation for the probability of localization between all compartment pairs for each pair of datasets (see Materials and Methods for details). SecPath, secretory pathway (ER and Golgi); Cyt, cytosol; Nuc, nucleus; Mit, mitochondrial or peroxisomal; PM & P, plasma membrane and periphery (including secreted and vacuolar proteins).
Table 4.
Comparison of Localization Annotations between PSLT2, YeastGFP, and TRIPLES Datasets for Different Classes of Proteinsa
Table 5.
Comparison with Previous Methods
Figure 3.
Sub-Compartmental Prediction Scheme
Proteins are predicted to be localized in one specific sub-compartment by first considering the most likely PSLT2 compartment (blue boxes). Further decisions depend on the PSLT2 second most likely compartment prediction (orange boxes) and targeting information (green boxes). Once all information has been analyzed, the protein is predicted to be in one of 18 sub-compartments (pink boxes). When the second most likely sub-compartments are considered, the default prediction is shown with a star (this branch of the tree is used in particular when proteins have no second most likely compartment as predicted by PSLT2). Pero, peroxisome; Vac, vacuole; Cyt, cytosolic; memb, membrane; TMD, number of transmembrane domains in protein; ER, endoplasmic reticulum; GPI, presence of GPI anchor; Nuc, nuclear; PM, plasma membrane; Mito, mitochondria; S, signal peptide; A, signal anchor; Q, neither signal peptide nor signal anchor.
Figure 4.
The protein–protein interaction maps for all proteins in the secretory pathway (B) or all proteins in the ER (C). Proteins are depicted as circles coloured according to their predicted sub-compartmental localization, as specified in the legend in (A). Interactions are shown as lines between proteins.