Peptide Vaccine Models Using Statistical Data Mining

Title: Peptide Vaccine Models Using Statistical Data Mining

Volume: 14 Issue: 6

Author(s): Rajani R. Joshi

Affiliation:

Keywords: B-cell epitope, paratope, logistic regression, sequence-alignment, solvent accessibility, heuristics

Abstract: Design and synthesis of peptide vaccines is of significant pharmaceutical importance. A knowledge based statistical model is fitted here for prediction of binding of an antigenic site of a protein or a B-cell epitope on a CDR (complementarity determining region) of an immunoglobulin. Linear analogues of the 3D structure of the epitopes are computed using this model. Extension for prediction of peptide epitopes from the protein sequence alone is also presented. Validation results show promising potential of this approach in computer-aided peptide vaccine production. The computed probabilities of binding also provide a pioneering approach for ab-initio prediction of ‘potency’ of protein or peptide vaccines modeled by this method.

Cite this article as:

Rajani R. Joshi , Peptide Vaccine Models Using Statistical Data Mining, Protein & Peptide Letters 2007; 14 (6) . https://dx.doi.org/10.2174/092986607780990000

DOI https://dx.doi.org/10.2174/092986607780990000	Print ISSN 0929-8665
Publisher Name Bentham Science Publisher	Online ISSN 1875-5305