FragAnchor: a large-scale predictor of glycosylphosphatidylinositol anchors in eukaryote protein sequences by qualitative scoring.
A glycosylphosphatidylinositol (GPI) anchor is a common but complex C-terminal post-translational modification of extracellular proteins in eukaryotes. Here we investigate the problem of correctly annotating GPI-anchored proteins for the growing number of sequences in public databases. We developed a computational system, called FragAnchor, based on the tandem use of a neural network (NN) and a hidden Markov model (HMM). Firstly, NN selects potential GPI-anchored proteins in a dataset, then HMM parses these potential GPI signals and refines the prediction by qualitative scoring. FragAnchor correctly predicted 91% of all the GPI-anchored proteins annotated in the Swiss-Prot database. In a large-scale analysis of 29 eukaryote proteomes, FragAnchor predicted that the percentage of highly probable GPI-anchored proteins is between 0.21% and 2.01%. The distinctive feature of FragAnchor, compared with other systems, is that it targets only the C-terminus of a protein, making it less sensitive to the background noise found in databases and possible incomplete protein sequences. Moreover, FragAnchor can be used to predict GPI-anchored proteins in all eukaryotes. Finally, by using qualitative scoring, the predictions combine both sensitivity and information content. The predictor is publicly available at [see text].
Poisson
Guylaine
G
Department of Information and Computer Sciences, University of Hawaii at Manoa, Honolulu, HI 96822, USA. guylaine@hawaii.edu
Chauve
Cedric
C
Chen
Xin
X
Bergeron
Anne
A
D000595:Amino Acid Sequence; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D005057:Eukaryotic Cells / Q000737:chemistry*; D017261:Glycosylphosphatidylinositols / Q000737:chemistry* / Q000302:isolation & purification / Q000378:metabolism*; D006801:Humans; D057927:Hydrophobic and Hydrophilic Interactions; D008390:Markov Chains; D008957:Models, Genetic; D008969:Molecular Sequence Data; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D011499:Protein Processing, Post-Translational; D020543:Proteome / Q000032:analysis; D012680:Sensitivity and Specificity; D020539:Sequence Analysis, Protein
D017261:Glycosylphosphatidylinositols; D020543:Proteome