Identifying novel peroxisomal proteins

Hawkins, J., Mahony, D., Maetschke, S., Wakabayashi, M., Teasdale, R. D. and Boden, M. (2007) Identifying novel peroxisomal proteins. Proteins: Structure Function and Bioinformatics, 69 3: 606-616. doi:10.1002/prot.21420


Author Hawkins, J.
Mahony, D.
Maetschke, S.
Wakabayashi, M.
Teasdale, R. D.
Boden, M.
Title Identifying novel peroxisomal proteins
Journal name Proteins: Structure Function and Bioinformatics   Check publisher's open access policy
ISSN 0887-3585
Publication date 2007-01-01
Sub-type Article (original research)
DOI 10.1002/prot.21420
Volume 69
Issue 3
Start page 606
End page 616
Total pages 11
Editor Lattman, E.E.
Place of publication USA
Publisher John Wiley & Sons
Language eng
Subject C1
270103 Protein Targeting and Signal Transduction
780105 Biological sciences
Abstract Peroxisomes are small subcellular compartments responsible for a range of essential metabolic processes. Efforts in predicting peroxisomal protein import are challenged by species variation and sparse sequence data sets with experimentally confirmed localization. We present a predictor of peroxisomal import based on the presence of the dominant peroxisomal targeting signal one (PTS1), a seemingly well conserved but highly unspecific motif. The signal appears to rely on subtle dependencies with the preceding residues. We evaluate prediction accuracies against two alternative predictor services, PEROXIP and the PTS1 PREDICTOR. We test the integrity of prediction on a range of prokaryotic and eukaryotic proteomes lacking peroxisomes. Similarly we test the accuracy on peroxisomal proteins known to not overlap with training data. The model identified a number of proteins within the RIKEN IPS7 mouse protein dataset as potentially novel peroxisomal proteins. Three were confirmed in vitro using immunofluorescent detection of myc-epitope-tagged proteins in transiently transfected BHK-21 cells (Dhrs2, Serhl, and Ehhadh). The final model has a superior specificity to both alternatives, and an accuracy better than PEROXIP and on par with PTs1 PREDICTOR. Thus, the model we present should prove invaluable for labeling PTS1 targeted proteins with high confidence. We use the predictor to screen several additional eukaryotic genomes to revise previously estimated numbers of peroxisomal proteins. Available at http://pprowler.itee.uq.edu.au. (C) 2007 Wiley-Liss, Inc.
Keyword Biochemistry & Molecular Biology
Biophysics
subcellular localization
peroxisome
prediction
machine learning
peroxisomal proteins
immunofluorescent detection
Predicting Subcellular-localization
Support Vector Machines
Amino-acid-composition
Sorting Signals
Sequence
Location
Biogenesis
Glycosomes
Classification
Networks
Q-Index Code C1
Q-Index Status Confirmed Code

 
Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 15 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 17 times in Scopus Article | Citations
Google Scholar Search Google Scholar
Created: Tue, 19 Feb 2008, 01:27:35 EST