PSORTb 3.0.3 / PSORT / PSORT II /WoLF PSORT 0.2 – Prediction of Protein Localization sites for Bacterial Sequences / Eukaryotic Sequences/ Plant Sequences



PSORTb (for “bacterial” PSORT) is a high-precision localization prediction method for bacterial proteins.PSORTb has remained the most precise bacterial protein subcellular localization (SCL) predictor since it was first made available in 2003.  PSORTb version improved recall, higher proteome-scale prediction coverage, and new refined localization subcategories. It is the first SCL predictor specifically geared for all prokaryotes, including archaea and bacteria with atypical membrane/cell wall topologies.


PSORT is a computer program for the prediction of protein localization sites in cells. It receives the information of an amino acid sequence and its source orgin, e.g., Gram-negative bacteria, as inputs. Then, it analyzes the input sequence by applying the stored rules for various sequence features of known protein sorting signals. Finally, it reports the possiblity for the input protein to be localized at each candidate site with additional information.

PSORT II is a new version of PSORT for eukaryotic sequences.

WoLF PSORT (an update of PSORT II for fungi/animal/plant sequences)


Brinkman Laboratory, Simon Fraser University /  Kenta Nakai








N.Y. Yu, J.R. Wagner, M.R. Laird, G. Melli, S. Rey, R. Lo, P. Dao, S.C. Sahinalp, M. Ester, L.J. Foster, and F.S.L. Brinkman (2010)
PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes.
Bioinformatics 26(13):1608-1615.

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Expert system for predicting protein localization sites in gram-negative bacteria.
Nakai K, Kanehisa M.

Proc Int Conf Intell Syst Mol Biol. 1997;5:147-52.
Better prediction of protein cellular localization sites with the k nearest neighbors classifier.
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Paul Horton, Keun-Joon Park, Takeshi Obayashi, Naoya Fujita, Hajime Harada, C.J. Adams-Collier, & Kenta Nakai,
WoLF PSORT: Protein Localization Predictor“,
Nucleic Acids Research, doi:10.1093/nar/gkm259, 2007.

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