PredGPI

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.

PredGPI: a GPI-anchor predictor.

PredGPI is a web-based tool to predict Glycosylphosphatidylinositol (GPI) anchors and positions of omega sites. The PredGPI algorithm uses <a href="https://www.nature.com/articles/nbt1004-1315" target="_blank" rel="noopener">Hidden Markov Model</a> (HMM) and a <a href="https://www.nature.com/articles/nbt1206-1565" target="_blank" rel="noopener">Support Vector Machine</a> (SVM).

PredGPI

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