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BMC Bioinformatics. 2008 Sep 23;9:392. doi: 10.1186/1471-2105-9-392.

PredGPI: a GPI-anchor predictor.

Author information

1
Biocomputing Group, Department of Biology, University of Bologna, Via Irnerio 42, 40126 Bologna, Italy. andrea@biocomp.unibo.it

Abstract

BACKGROUND:

Several eukaryotic proteins associated to the extracellular leaflet of the plasma membrane carry a Glycosylphosphatidylinositol (GPI) anchor, which is linked to the C-terminal residue after a proteolytic cleavage occurring at the so called omega-site. Computational methods were developed to discriminate proteins that undergo this post-translational modification starting from their aminoacidic sequences. However more accurate methods are needed for a reliable annotation of whole proteomes.

RESULTS:

Here we present PredGPI, a prediction method that, by coupling a Hidden Markov Model (HMM) and a Support Vector Machine (SVM), is able to efficiently predict both the presence of the GPI-anchor and the position of the omega-site. PredGPI is trained on a non-redundant dataset of experimentally characterized GPI-anchored proteins whose annotation was carefully checked in the literature.

CONCLUSION:

PredGPI outperforms all the other previously described methods and is able to correctly replicate the results of previously published high-throughput experiments. PredGPI reaches a lower rate of false positive predictions with respect to other available methods and it is therefore a costless, rapid and accurate method for screening whole proteomes.

PMID:
18811934
PMCID:
PMC2571997
DOI:
10.1186/1471-2105-9-392
[Indexed for MEDLINE]
Free PMC Article
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