It combines several prediction methods and algorithms for the amino-acid sequences which potentially represent localization signals in the cell. Based on such analysis it will predict the subcellular compartment the protein is most probably located in.
PSort will incorporate in its prediction various information about the aminoacid sequences. For example, it will detect the presence of: signal sequence and its cleavage site (PSG score, McGeoch's method and GvH score, von Heijne's method), transmembrane domains (ALOM score, Klein et al's method), membrane topology i.e. whether N- or C- terminus is cytoplasmatic (Hartmann et al.), mitochondrial targeting sequence (MITDISC and Gavel scores), nuclear localization signals (NUCDISC), KDEL ER membrane retention signals, SKL1 and SKL2 peroxisomal targeting signals, VAC possible vacuolar targeting motif, RNA-binding motifs, lipid anchors - i.e. NMYR N-myristoylation motif and prenylation motif, memYQRL transport motif from cell surface to Golgi, tyrosines in the tail, dileucine motif in the tail, PROSITE DNA binding motifs, PROSITE ribosomal protein motifs, coiled-coil regions.
At the end PSort will give an estimate in percents what is the probabilitty of the protein to be localized in each cellular compartment.
55.6 %: extracellular, including cell wall
22.2 %: nuclear
22.2 %: mitochondrial
Wolf PSort is the useful extension of the PSort algorithm and it uses PSort parameters and their scores to search the UniProt (and Gene Ontology) databases of proteins to find proteins that have similar PSort scores as the query protein. The search will exclude highly homologous protein sequences as they will likely have the same scores as the query sequence and this kind of search could be achieved using e.g. BLAST. Instead Wolf PSort will give mainly non-homologous proteins that have similar localization motifs as the query sequence. Wolf PSort will also give a sequence with 100% homology as the query sequence.