Title

LIPPRED: A web server for accurate prediction of lipoprotein signal sequences and cleavage sites

Authors

Paul D. Taylor¹, Christopher P. Toseland¹, Teresa K. Attwood ² & Darren R. Flower^1,3*

Affiliation

¹The Jenner Institute, University of Oxford, Compton, Newbury, Berkshire, RG20 7NN, UK, ²Faculty of Life Sciences & School of Computer Science, The University of Manchester, Oxford Road, Manchester M13 9PT, UK, ³Corresponding author. The Jenner Institute, University of Oxford, Compton, Newbury, Berkshire, RG20 7NN, UK.

E-mail*

darren.flower@jenner.ac.uk; * Corresponding author

Phone

44 1635 577954

Fax

44 1635 577908

Article Type

Web Server

Date

received July 11, 2006; revised July18, 2006; accepted July 18, 2006; published online July 19, 2006

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 SpaseII-cleaved lipoprotein signal sequences and the prediction of their cleavage sites.

Keywords

lipoprotein signal sequences; Naïve-Bayesian networks; reverse vaccinology; prediction; server

Citation

Taylor et al., Bioinformation 1(5):176-179 (2006)

Edited by

P. Kangueane

ISSN

0973-2063

Publisher

Biomedical Informatics

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.