FragAnchor: A Large-Scale Predictor of Glycosylphosphatidylinositol Anchors in Eukaryote Protein Sequences by Qualitative Scoring  

作　　者：Guylaine Poisson Cedric Chauve Xin Chen Anne Bergeron 

机构地区：[1]Department of Information and Computer Sciences, University of Hawaii at Manoa, Honolulu, HI 96822, USA [2]CGL ＆ Département d＇informatique, Université du Québee à Montréal, Montréal QC, Canada [3]LA CIM, Université du QuEbec à MontrEal, Montréal QC, Canada [4]Department of Mathematics, Simon Fraser University, Burnaby BC, Canada.

出　　处：《Genomics, Proteomics & Bioinformatics》2007年第2期121-130,共10页基因组蛋白质组与生物信息学报（英文版）

摘　　要：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 http：//navet .ics.hawaii.edu/- fraganchor/NNHMM/NNHMM.ht ml.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 http：//navet .ics.hawaii.edu/- fraganchor/NNHMM/NNHMM.ht ml.

关 键 词：BIOINFORMATICS post-translational modification proteome analysis 

分 类 号：Q51[生物学—生物化学]