WoLF PSORT: protein localization predictor

By Paul Horton, Keun-Joon Park, T. Obayashi, Naoya Fujita, Hajime Harada, C. J. Adams-Collier, Kenta Nakai. 2007

3,509 citations recorded by the source corpus.

WoLF PSORT is an extension of the PSORT II program for protein subcellular location prediction. WoLF PSORT converts protein amino acid sequences into numerical localization features; based on sorting signals, amino acid composition and functional motifs such as DNA-binding motifs. After conversion, a simple k-nearest neighbor classifier is used for prediction. Using html, the evidence for each prediction is shown in two ways: (i) a list of proteins of known localization with the most similar localization features to the query, and (ii) tables with detailed information about individual localization features. For convenience, sequence alignments of the query to similar proteins and links to UniProt and Gene Ontology are provided. Taken together, this information allows a user to understand the evidence (or lack thereof) behind the predictions made for particular proteins. WoLF PSORT is available at wolfpsort.org

Subfields: Molecular Biology

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Citations in the corpus

Modeling aspects of the language of life through transfer-learning protein sequences (2019, methodology): “This concept resembles that of using k-mers for database searches [33], clustering [81], motifs [82, 83], and prediction methods [72, 76, 84 – 88].”
Modeling the language of life – Deep Learning Protein Sequences (2019, methodology): “Localization prediction 3 was trained and evaluated using the DeepLoc data set [46] for which performance was 4 measured for several methods, namely: LocTree2 [66], MultiLoc2 [67], SherLoc2 [68], CELLO 5 [69], iLoc-Euk [51], WoLF PSORT [70] and YLoc [71].”
Properties and identification of human protein drug targets (2009, methodology): “The WoLF PSORT program (http://wolfpsort.seq.cbrc.jp/) (Horton et al., 2007) was used to predict protein subcellular localization.”
Genome of an arbuscular mycorrhizal fungus provides insight into the oldest plant symbiosis (2013, methodology): “Secreted proteins were identified using a custom pipeline including the SignalP v4 (39),WolfPSort (40), TMHMM, TargetP (41), and PS-Scan algorithms (42).”
YLoc—an interpretable web server for predicting subcellular localization (2010, methodology): “Note that YLoc+, WoLF PSORT and Euk-mPloc are evaluated using the generalized ACC and F 1 from multilabel classification (28).”
MultiLoc2: integrating phylogeny and Gene Ontology terms improves subcellular protein localization prediction (2009, methodology): “More recent and advanced methods combine composition information with the detection of sorting signals [22,23].”
The RAVEN Toolbox and Its Use for Generating a Genome-scale Metabolic Model for Penicillium chrysogenum (2013, methodology): “The default predictor is WoLF PSORT, which is distributed with the RAVEN Toolbox [35].”
GLK Transcription Factors Coordinate Expression of the Photosynthetic Apparatus in Arabidopsis[W][OA] (2009, methodology): “Of 114 proteins, 66 could be unambiguously localized to a cellular compartment based on annotation or computational prediction using WoLF PSORT (Horton et al., 2007).”
Identification of the lipoxygenase gene family from Vitis vinifera and biochemical characterisation of two 13-lipoxygenases expressed in grape berries of Sauvignon Blanc. (2010, neutral): “org/, accessed 21 May 2010) servers (Horton et al. 2007).”
Molecular Cloning and Characterization of Rubber Biosynthetic Genes from Taraxacum koksaghyz (2010, methodology): “Putative transmembrane regions were detected using the localization prediction program PSORTII (Horton et al. 2007).”