PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes

By N. Y. Yu, James R. Wagner, Matthew R. Laird, Gabor Melli, Sébastien Rey, Raymond Lo, Phuong Dao, Süleyman Cenk Sahinalp, Martin Ester, L. Foster and 1 more. Bioinform., 2010

2,426 citations recorded by the source corpus.

Motivation: PSORTb has remained the most precise bacterial protein subcellular localization (SCL) predictor since it was first made available in 2003. However, the recall needs to be improved and no accurate SCL predictors yet make predictions for archaea, nor differentiate important localization subcategories, such as proteins targeted to a host cell or bacterial hyperstructures/organelles. Such improvements should preferably be encompassed in a freely available web-based predictor that can also be used as a standalone program. Results: We developed PSORTb version 3.0 with 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. It features an improved standalone program, with a new batch results delivery system complementing its web interface. We evaluated the most accurate SCL predictors using 5-fold cross validation plus we performed an independent proteomics analysis, showing that PSORTb 3.0 is the most accurate but can benefit from being complemented by Proteome Analyst predictions. Availability: http://www.psort.org/psortb (download open source software or use the web interface). Contact: psort-mail@sfu.ca Supplementary Information: Supplementary data are availableat Bioinformatics online.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/0100cdc7a8a18bdcfbfa5cf03b757609e1bb71d8

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In Genetics

Geoffrey L. Winsor

In Molecular Biology

Citations in the corpus

NsrA, a Predicted β-Barrel Outer Membrane Protein Involved in Plant Signal Perception and the Control of Secondary Infection in Sinorhizobium meliloti (2018, background): “0 program (16) predicted an outer membrane location of the mature protein.”
Transcriptomic Insight in the Control of Legume Root Secondary Infection by the Sinorhizobium meliloti Transcriptional Regulator Clr (2017, background): “PSORTb 3.0 analysis (Yu et al., 2010) predicted a noncytoplasmic location for all three SMc02177, SMc02178 and SMb20495 proteins without specifying more precisely their subcellular localization.”
DRAM for distilling microbial metabolism to automate the curation of microbiome function (2020, methodology): “Genes with an asterisk had an unidentified localization by PSORTb (101).”
LocTree3 prediction of localization (2014, neutral): “Many machine learning methods predict the native localization of a protein from its amino acid sequence; among the best known are CELLO (7), WoLF PSORT (8), YLoc (9) and PSORTb (10).”
Enhanced annotations and features for comparing thousands of Pseudomonas genomes in the Pseudomonas genome database (2015, background): “0 prediction of protein subcellular localization now contains new localization subcategories suited for identifying proteins associated with the Type III secretion apparatus, fimbria, flagella or proteins that are targeted to a host cell (11).”
CELLO2GO: A Web Server for Protein subCELlular LOcalization Prediction with Functional Gene Ontology Annotation (2014, background): “[22] The reason why certain computational tools provide improved subcellular localization prediction appears to be that GO information [8,9,10,12,15,20,21] or a homologybased modular structure comparison [23] is included in the prediction routine.”