Abstract
Bacterial lipoproteins have many important functions and represent a class of possible vaccine candidates. The prediction of lipoproteins from sequence is thus an important task for computational vaccinology. Naïve-Bayesian networks were trained to identify SpaseII cleavage sites and their preceding signal sequences using a set of 199 distinct lipoprotein sequences. A comprehensive range of sequence models was used to identify the best model for lipoprotein signal sequences. The best performing sequence model was found to be 10-residues in length, including the conserved cysteine lipid attachment site and the nine residues prior to it. The sensitivity of prediction for LipPred was 0.979, while the specificity was 0.742. Here, we describe LipPred, a web server for lipoprotein prediction; available at the URL: http://www.jenner.ac.uk/LipPred/. LipPred is the most accurate method available for the detection of SpaseIIcleaved lipoprotein signal sequences and the prediction of their cleavage sites.
| Original language | English |
|---|---|
| Pages (from-to) | 176-179 |
| Number of pages | 4 |
| Journal | Bioinformation |
| Volume | 1 |
| Issue number | 5 |
| Early online date | 19 Jul 2006 |
| Publication status | Published - 2006 |
Bibliographical note
This is an open-access article, which permits unrestricted use, distribution, and reproduction in any medium, for non-commercial purposes, provided the original author and source are credited.Keywords
- lipoprotein signal sequence
- naive-bayesian networks
- reverse vaccinology
- prediction
- server