Comparison of predicted GPI-anchoring potential for WT α2δ-1, PIN-α2δ chimera, mutant α2δ-2 GAS:WKW and Thy-1.

Proteins were analysed using three independent algorithms Big-PI [30] (http://mendel.imp.ac.at/gpi/gpi_server.html), FragAnchor [55] (http://navet.ics.hawaii.edu/~fraganchor/NNHMM/NNHMM.html) and PredGPI [56]http://gpcr.biocomp.unibo.it/predgpi/). Big-PI is a predictor based on scoring the presence of an amino terminal signal peptide and features of canonical carboxy-terminal GPI-anchoring motifs. FragAnchor identifies GPI motifs using a Neural Network (NN) and Hidden Markov Model (HMM). PredGPI integrates a Support Vector Machine and HMM and employs accurately trained datasets. Likelihood of GPI anchoring is indicated by positive scores in Big-PI, NN values ≈1 in Frag Anchor and a ranking (Highly probable, probable, lowly probable and not GPI-anchored) in PredGPI. Of the three algorithms only Big-PI and PredGPI predict ω-site residues (bold and underlined in tetrapeptide sequences indicated), with the latter reported to afford the lowest rate of false positive predictions. Note that the ω-site residues giving the highest potential for GPI-modification are indicated, irrespective of the protein's potential for GPI modification. While differences exist in the predicted ω-site residues obtained between algorithms, these are generally in very close physical proximity. Of the four WT Cav-α2δ subunits only α2δ-3 is predicted to be GPI-anchored by all three algorithms while WT α2δ-1 is only predicted to be, using PredGPI. In addition, the predicted ω-site for WT α2δ-1 differs between algorithms (Big-Pi: CGGV; PredGPI: CGGV) and also to that reported [20](CGGV).