An N-glycosylation site predictor for human proteins is available at http://www.cbs.dtu.dk/ services/NetNGlyc/.
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Title: Prediction of N-Glycosylation sites in Human Proteins | P51 |
Gupta, Ramneek; Jung, Eva; Brunak, Soren ramneek@cbs.dtu.dk Technical University of Denmark |
| Contrary to widespread belief, acceptor sites for N-linked glycosylation on protein sequences are not well characterised. The consensus sequence, Asn-Xaa-Ser/Thr (where Xaa is not Pro), is known to be a prerequisite for the modification. However, not all of these sequons are modified and it is thus not discriminatory between glycosylated and non-glycosylated asparagines. We train artificial neural networks on the surrounding sequence context, in an attempt to discriminate between acceptor and non-acceptor sequons. In a cross-validated performance, the networks could identify 86% of the glycosylated and 61% of the non-glycosylated sequons, with an overall accuracy of 76%. The method can be optimised for high specificity or high sensitivity. For example, 98% specificity can be obtained at about 50% sensitivity. Apart from characterising individual proteins, the prediction method can rapidly scan complete proteomes. We have studied the spread of N-glycosylation sites across the human proteome. An N-glycosylation site predictor for human proteins is available at http://www.cbs.dtu.dk/ services/NetNGlyc/. |
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