Format

Send to

Choose Destination
BMC Bioinformatics. 2008 Nov 27;9:500. doi: 10.1186/1471-2105-9-500.

Prediction of glycosylation sites using random forests.

Author information

1
School of Chemistry, University of Nottingham, University Park, Nottingham NG7 2RD, UK. pcxsh1@nottingham.ac.uk

Abstract

BACKGROUND:

Post translational modifications (PTMs) occur in the vast majority of proteins and are essential for function. Prediction of the sequence location of PTMs enhances the functional characterisation of proteins. Glycosylation is one type of PTM, and is implicated in protein folding, transport and function.

RESULTS:

We use the random forest algorithm and pairwise patterns to predict glycosylation sites. We identify pairwise patterns surrounding glycosylation sites and use an odds ratio to weight their propensity of association with modified residues. Our prediction program, GPP (glycosylation prediction program), predicts glycosylation sites with an accuracy of 90.8% for Ser sites, 92.0% for Thr sites and 92.8% for Asn sites. This is significantly better than current glycosylation predictors. We use the trepan algorithm to extract a set of comprehensible rules from GPP, which provide biological insight into all three major glycosylation types.

CONCLUSION:

We have created an accurate predictor of glycosylation sites and used this to extract comprehensible rules about the glycosylation process. GPP is available online at http://comp.chem.nottingham.ac.uk/glyco/.

PMID:
19038042
PMCID:
PMC2651179
DOI:
10.1186/1471-2105-9-500
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for BioMed Central Icon for PubMed Central
Loading ...
Support Center