WoLF PSORT is an advanced protein subcellular localization predictor that extends the PSORT II program. It converts protein amino acid sequences into numerical localization features, including sorting signals, amino acid composition, and functional motifs. These features are then classified using a weighted k-nearest neighbor classifier. The tool provides detailed information about the evidence supporting each prediction, such as a list of proteins with similar localization features and tables with individual localization feature details. The server is implemented using Mason and Perl, and it supports multiple queries. WoLF PSORT is available at wolfpsort.org and is designed to help users understand the evidence behind protein localization predictions. The dataset includes proteins from fungi, plants, and animals, with a focus on high-quality training data from UniProt and Gene Ontology. The prediction accuracy is estimated to be around 70% for major localization sites like the nucleus, mitochondria, and cytosol, but lower for less studied sites. The tool also provides sequence alignments and links to UniProt, Gene Ontology, and other databases for additional context.WoLF PSORT is an advanced protein subcellular localization predictor that extends the PSORT II program. It converts protein amino acid sequences into numerical localization features, including sorting signals, amino acid composition, and functional motifs. These features are then classified using a weighted k-nearest neighbor classifier. The tool provides detailed information about the evidence supporting each prediction, such as a list of proteins with similar localization features and tables with individual localization feature details. The server is implemented using Mason and Perl, and it supports multiple queries. WoLF PSORT is available at wolfpsort.org and is designed to help users understand the evidence behind protein localization predictions. The dataset includes proteins from fungi, plants, and animals, with a focus on high-quality training data from UniProt and Gene Ontology. The prediction accuracy is estimated to be around 70% for major localization sites like the nucleus, mitochondria, and cytosol, but lower for less studied sites. The tool also provides sequence alignments and links to UniProt, Gene Ontology, and other databases for additional context.