Fungal peroxisomes are characterized by a number of specific biological functions. To understand the physiology and biochemistry of these organelles knowledge of the proteome content is crucial. Here, we address different strategies to predict peroxisomal proteins by bioinformatics approaches. These tools range from simple text searches to network based learning strategies. A complication of this analysis is the existence of cryptic peroxisomal proteins, which are overlooked in conventional bioinformatics queries. These include proteins where targeting information results from transcriptional and posttranscriptional alterations. But also proteins with low efficiency targeting motifs that are predominantly localized in the cytosol, and proteins lacking any canonical targeting information, can play important roles within peroxisomes. Many of these proteins are so far unpredictable. Detection and characterization of these cryptic peroxisomal proteins revealed the presence of novel peroxisomal enzymatic reaction networks in fungi.
Keywords: Alternative splicing; Dual targeting; Glycolysis; Motif prediction; Peroxisome; Redox homeostasis; Translational readthrough.